LLM Fine-Tuning

LLM: Large Language Model

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LLM fine-tuning studies how additional training can adapt a pretrained model to new tasks, specialized knowledge, languages, or desired behaviors. Common approaches include supervised fine-tuning on curated or synthetic examples, preference optimization and reinforcement learning, and parameter-efficient methods such as LoRA and QLoRA that update only a small part of the model. Researchers also develop data-selection methods and selective layer or neuron updates to reduce training cost, and evaluate results on settings such as reasoning, code generation, multilingual tasks, and domain-specific question answering. A central challenge is gaining reliable task performance without losing general abilities or safety; results depend on the data, model, and evaluation, and retrieval can sometimes be a better alternative for adding knowledge.

Latest papers

758 papers76 papers in the last four weeks, up 15% on the four weeks before. 0.4% of all new papers.

Apr 5, 2026

Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges

This work presents a comparative analysis of embedding-based and generative models for classifying geoscience technical documents. Using a multi-disciplinary benchmark dataset, we evaluated the trade-offs between model accuracy, stability, and computational cost. We find that generative Vision-Language Models (VLMs) like Qwen2.5-VL, enhanced with Chain-of-Thought (CoT) prompting, achieve superior zero-shot accuracy (82%) compared to state-of-the-art multimodal embedding models like QQMM (63%). We also demonstrate that while supervised fine-tuning (SFT) can improve VLM performance, it is sensitive to training data imbalance.

Rong Lu, Hao Liu, Song Hou

Earth Science Text Classification VLM Evaluation Text Embedding Models Zero-Shot Text Classification LLM Fine-Tuning

Apr 4, 2026

Aletheia: Gradient-Guided Layer Selection for Efficient LoRA Fine-Tuning Across Architectures

Low-Rank Adaptation (LoRA) has become the dominant parameter-efficient fine-tuning method for large language models, yet standard practice applies LoRA adapters uniformly to all transformer layers regardless of their relevance to the downstream task. We introduce Aletheia, a gradient-guided layer selection method that identifies the most task-relevant layers via a lightweight gradient probe and applies LoRA adapters only to those layers with asymmetric rank allocation. Across 81 experiment rows covering 14 successful models from 8 architecture families (0.5B-72B parameters, including dense and Mixture-of-Experts architectures), with one additional documented failed Pythia/GPT-NeoX attempt in Campaign 2, Aletheia achieves a 15-28% training speedup (mean 23.1%, p < 0.001) with bounded extra forgetting and broadly matched downstream behavior on the evaluated MMLU, GSM8K, and HumanEval benchmark pack. Across the tested families and scales, Campaign 1 shows a 100% per-model speed win rate and Campaign 2 shows broadly preserved downstream behavior within a bounded-degradation framing. Together these results support a practical model-economics claim: intelligent layer selection can make LoRA fine-tuning materially more efficient without introducing major downstream damage on the evaluated set.

Abdulmalek Saket

Royal Fenice Kft / ALETHEIA PROTOCOL research, Budapest, Hungary

LLM Fine-Tuning Efficient Language Model Training Parameter-Efficient Fine-Tuning Low-Rank Adaptation

Apr 3, 2026

Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notes

Timely discharge prediction is essential for optimizing bed turnover and resource allocation in elective spine surgery units. This study evaluates the feasibility of lightweight, fine-tuned large language models (LLMs) and traditional text-based models for predicting next-day discharge using postoperative clinical notes. We compared 13 models, including TF-IDF with XGBoost and LGBM, and compact LLMs (DistilGPT-2, Bio_ClinicalBERT) fine-tuned via LoRA. TF-IDF with LGBM achieved the best balance, with an F1-score of 0.47 for the discharge class, a recall of 0.51, and the highest AUC-ROC (0.80). While LoRA improved recall in DistilGPT2, overall transformer-based and generative models underperformed. These findings suggest interpretable, resource-efficient models may outperform compact LLMs in real-world, imbalanced clinical prediction tasks.

Ha Na Cho, Sairam Sutari, Alexander Lopez +2

University of California, Irvine, Donald Bren School of Information, Department of Informatics · University of California, Irvine, Department of Neurosurgery

Clinical Prediction Clinical Outcome Prediction LLM Fine-Tuning

Apr 3, 2026

The limits of bio-molecular modeling with large language models : a cross-scale evaluation

The modeling of bio-molecular system across molecular scales remains a central challenge in scientific research. Large language models (LLMs) are increasingly applied to bio-molecular discovery, yet systematic evaluation across multi-scale biological problems and rigorous assessment of their tool-augmented capabilities remain limited. We reveal a systematic gap between LLM performance and mechanistic understanding through the proposed cross-scale bio-molecular benchmark: BioMol-LLM-Bench, a unified framework comprising 26 downstream tasks that covers 4 distinct difficulty levels, and computational tools are integrated for a more comprehensive evaluation. Evaluation on 13 representative models reveals 4 main findings: chain-of-thought data provides limited benefit and may even reduce performance on biological tasks; hybrid mamba-attention architectures are more effective for long bio-molecular sequences; supervised fine-tuning improves specialization at the cost of generalization; and current LLMs perform well on classification tasks but remain weak on challenging regression tasks. Together, these findings provide practical guidance for future LLM-based modeling of molecular systems.

Yaxin Xu, Yue Zhou, Tianyu Zhao +2

Southern University of Science and Technology, Shenzhen, 518055, China. · Pengcheng Laboratory, Shenzhen, 518055, China. · Institute of Mechanics, Chinese Academy of Sciences, Beijing, 100190, China.

LLM Evaluation Large Language Model-Guided Scientific Discovery LLM Fine-Tuning

Apr 3, 2026

Activation-Efficient Fine-Tuning via Sequence-Compressed Low-Rank Subspaces

Parameter-Efficient Fine-Tuning (PEFT) has become the standard approach for adapting large language models (LLMs). However, despite reducing trainable parameter counts, many PEFT methods remain memory-bound by token-level activation intermediates that scale linearly with sequence length, limiting their practicality for long-context and on-device adaptation. Unlike prior memory-efficient PEFT approaches that preserve token-level adapter activations, LARS (Low-memory Activation-Rank Subspace) reduces the sequence-length scaling of stored adapter activation intermediates from O(BSRL) to O(BRL) by performing adaptation within a sequence-level low-rank subspace. Through pooled feature extraction, low-rank subspace modulations, and residual projection, LARS reduces the overall memory footprint by 23% on GPUs and 47.8% on CPUs relative to LoRA at S = 1024, with relative savings increasing with context length. Experiments across reasoning, understanding, long-context, retrieval, and token-level tasks show that LARS maintains competitive performance relative to LoRA while substantially improving memory efficiency. Beyond GPUs, LARS also reduces average CPU power and energy consumption per optimisation step by 22.43% and 22.06%, respectively. These results demonstrate that activation-efficient adaptation provides a practical path toward resource-constrained and on-device fine-tuning of LLMs.

Irene Tenison, Stella Ahn, Miriam Kim +2

MIT CSAIL · Harvard SEAS

Memory-Efficient Fine-Tuning LLM Fine-Tuning Parameter-Efficient Fine-Tuning

Apr 2, 2026

Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuning

General-purpose large language models (LLMs) often struggle to generate reliable responses in specialized engineering domains due to limited domain grounding and insufficient exposure to structured technical knowledge. This study investigates practical strategies for adapting a foundation LLM to the additive manufacturing (AM) domain in order to improve answer accuracy, relevance, and usability for expert-level question answering. AM knowledge is distributed across heterogeneous sources such as academic literature, manufacturer documentation, technical standards, and procedural guides. Although general LLMs demonstrate strong linguistic capabilities, they frequently fail to retrieve and contextualize such domain-specific information. Two common approaches to address this limitation are domain-specific fine-tuning and retrieval-augmented generation (RAG). We construct a curated AM corpus and evaluate three configurations based on LLaMA-3-8B: (1) the pretrained baseline model, (2) a RAG system that retrieves relevant document chunks from a vector database, and (3) a model fine-tuned on raw domain text. Performance is evaluated using 200 expert-designed AM questions assessed by mechanical engineering experts for accuracy, relevance, and overall preference. Results show that the RAG model consistently outperforms the baseline. Among the 200 questions, 75.5% of RAG responses are judged more accurate, 85.2% are preferred overall, and 90.8% are rated more relevant than baseline responses. In contrast, fine-tuning on raw AM text reduces performance, producing more accurate answers in only 5.6% of cases and more relevant answers in 32.5% of cases. These results indicate that retrieval-augmented approaches provide a more effective pathway for adapting LLMs to specialized engineering domains than naive fine-tuning on unstructured technical data.

Saiful Islam Sagor, Tania Haghighi, Minhaj Nur Alam +1

Department of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, NC 28223, USA. · Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC 28223, USA.

Domain Adaptation for LLMs LLM Fine-Tuning Retrieval-Augmented Generation Question Answering Additive Manufacturing

Apr 2, 2026

Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models

Retrieval-Augmented Language Models (RALMs) have shown strong potential in knowledge-intensive tasks, yet they remain vulnerable when retrieved contexts are noisy or irrelevant. Robustness against such contexts requires two distinct capabilities: abstention when contexts are uninformative, and selective extraction when relevant evidence is buried in noise. Yet existing methods face two key limitations: they do not train separately for these two capabilities, and they adapt the model at a coarse layer- or module-level granularity, overlooking that only a small subset of neurons is strongly activated for a given input. We propose NeuRIT, a Neuron-guided Robust Instruction-Tuning framework built on a localization-first perspective. NeuRIT mines context-aware neurons associated with relevant and irrelevant context processing, and uses them as anchors to selectively adapt both the identified neuron groups and the layers in which they concentrate. NeuRIT then performs two-stage instruction tuning that teaches complementary behaviors: suppress generation when there is nothing to extract, and extract relevant evidence when there is. NeuRIT consistently outperforms strong baselines across diverse QA benchmarks and generator backbones. Our code is available at https://github.com/HYU-ARK-Lab/NeuRIT.

Jae O Lee, Jaemin Kim, Sumyeong Ahn +1

1Hanyang University · 2KENTECH

Retrieval-Augmented Generation Fine-Tuning Knowledge Augmentation for Language Models LLM Fine-Tuning Neural Network Robustness Retrieval Robustness+2

Apr 2, 2026

Adam's Law: Textual Frequency Law on Large Language Models

While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel research direction in terms of textual data frequency, which is an understudied topic, to the best of our knowledge. Our framework is composed of three units. First, this paper proposes Textual Frequency Law (TFL), which indicates that frequent textual data should be preferred for LLMs for both prompting and fine-tuning. Since many LLMs are closed-source in their training data, we propose using online resources to estimate the sentence-level frequency. We then utilize an input paraphraser to paraphrase the input into a more frequent textual expression. Next, we propose Textual Frequency Distillation (TFD) by querying LLMs to conduct story completion by further extending the sentences in the datasets, and the resulting corpora are used to adjust the initial estimation. Finally, we propose Curriculum Textual Frequency Training (CTFT) that fine-tunes LLMs in an increasing order of sentence-level frequency. Experiments are conducted on our curated dataset Textual Frequency Paired Dataset (TFPD) on math reasoning, machine translation, commonsense reasoning and agentic tool calling. Results show the effectiveness of our framework.

Hongyuan Adam Lu, Z. L., Victor Wei +5

FaceMind Corporation · The Chinese University of Hong Kong

LLM Prompting LLM Fine-Tuning LLM Training

Apr 2, 2026

DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment

Reinforcement Learning from Human Feedback (RLHF), using algorithms like Proximal Policy Optimization (PPO), aligns Large Language Models (LLMs) with human values but is costly and unstable. Alternatives have been proposed to replace PPO or integrate Supervised Fine-Tuning (SFT) and contrastive learning for direct fine-tuning and value alignment. However, these methods still require voluminous data to learn preferences and may weaken the generalization ability of LLMs. To further enhance alignment efficiency and performance while mitigating the loss of generalization ability, this paper introduces Distribution-guided Efficient Fine-Tuning (DEFT), an efficient alignment framework incorporating data filtering and distributional guidance by calculating the differential distribution reward based on the output distribution of language model and the discrepancy distribution of preference data. A small yet high-quality subset is filtered from the raw data using a differential distribution reward, which is then incorporated into existing alignment methods to guide the model's output distribution. Experimental results demonstrate that the methods enhanced by DEFT outperform the original methods in both alignment capability and generalization ability, with significantly reduced training time.

Liang Zhu, Feiteng Fang, Yuelin Bai +4

Southern University of Science and Technology · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences · University of Science and Technology of China +1

LLM Alignment LLM Fine-Tuning Preference Learning

Apr 2, 2026

Taming CATS: Controllable Automatic Text Simplification through Instruction Fine-Tuning with Control Tokens

Controllable Automatic Text Simplification (CATS) produces user-tailored outputs, yet controllability is often treated as a decoding problem and evaluated with metrics that are not reflective to the measure of control. We observe that controllability in ATS is significantly constrained by data and evaluation. To this end, we introduce a domain-agnostic CATS framework based on instruction fine-tuning with discrete control tokens, steering open-source models to target readability levels and compression rates. Across three model families with different model sizes (Llama, Mistral, Qwen; 1-14B) and four domains (medicine, public administration, news, encyclopedic text), we find that smaller models (1-3B) can be competitive, but reliable controllability strongly depends on whether the training data encodes sufficient variation in the target attribute. Readability control (FKGL, ARI, Dale-Chall) is learned consistently, whereas compression control underperforms due to limited signal variability in the existing corpora. We further show that standard simplification and similarity metrics are insufficient for measuring control, motivating error-based measures for target-output alignment. Finally, our sampling and stratification experiments demonstrate that naive splits can introduce distributional mismatch that undermines both training and evaluation.

Hanna Hubarava, Yingqiang Gao

Department of Computational Linguistics, University of Zurich, Switzerland · Department of Clinical Research, University of Bern, Switzerland

Text Simplification Controllable Text Generation LLM Fine-Tuning Language Model Generation Evaluation

Apr 2, 2026

FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models

Parameter-efficient fine-tuning (PEFT) has emerged as a crucial paradigm for adapting large language models (LLMs) under constrained computational budgets. However, standard PEFT methods often struggle in multi-task fine-tuning settings, where diverse optimization objectives induce task interference and limited parameter budgets lead to representational deficiency. While recent approaches incorporate mixture-of-experts (MoE) to alleviate these issues, they predominantly operate in the spatial domain, which may introduce structural redundancy and parameter overhead. To overcome these limitations, we reformulate adaptation in the spectral domain. Our spectral analysis reveals that different tasks exhibit distinct frequency energy distributions, and that LLM layers display heterogeneous frequency sensitivities. Motivated by these insights, we propose FourierMoE, which integrates the MoE architecture with the inverse discrete Fourier transform (IDFT) for frequency-aware adaptation. Specifically, FourierMoE employs a frequency-adaptive router to dispatch tokens to experts specialized in distinct frequency bands. Each expert learns a set of conjugate-symmetric complex coefficients, preserving complete phase and amplitude information while theoretically guaranteeing lossless IDFT reconstruction into real-valued spatial weights. Extensive evaluations across 28 benchmarks, multiple model architectures, and scales demonstrate that FourierMoE consistently outperforms competitive baselines in both single-task and multi-task settings while using significantly fewer trainable parameters. These results highlight the promise of spectral-domain expert adaptation as an effective and parameter-efficient paradigm for LLM fine-tuning.

Juyong Jiang, Fan Wang, Hong Qi +2

The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology

LLM Fine-Tuning Parameter-Efficient Fine-Tuning Mixture-of-Experts Language Models Multi-Task Learning

Apr 2, 2026

MiCA Learns More Knowledge Than LoRA and Full Fine-Tuning

Minor Component Adaptation (MiCA) is a novel parameter-efficient fine-tuning method for large language models that focuses on adapting underutilized subspaces of model representations. Unlike conventional methods such as Low-Rank Adaptation (LoRA), which target dominant subspaces, MiCA leverages Singular Value Decomposition to identify subspaces related to minor singular vectors associated with the least significant singular values and constrains the update of parameters during fine-tuning to those directions. This strategy leads to up to 5.9x improvement in knowledge acquisition under optimized training hyperparameters and a minimal parameter footprint of 6-60% compared to LoRA. These results suggest that constraining adaptation to minor singular directions provides a more efficient and stable mechanism for integrating new knowledge into pre-trained language models.

Sten Rüdiger, Sebastian Raschka

Independent Researcher · RAIR Lab

LLM Fine-Tuning Parameter-Efficient Fine-Tuning Low-Rank Adaptation

Apr 2, 2026

Contrast Matters: Understanding Robustness of In-Context Fine-Tuning to Target-Context Relatedness

In-context fine-tuning (IC-Train), training an LLM with labeled examples in-context, is increasingly used in place of standard fine-tuning for domain adaptation and continual absorption of labeled data. We study the robustness of the in-context learning ability that emerges from such training: does the fine-tuned model perform well across test inputs whose in-context examples range from unrelated to nearly identical? Across 32 configurations spanning four open-source LLMs and eight test sets over machine translation, Text-to-SQL, and multilingual semantic parsing, we show that robustness hinges on an overlooked design choice: how in-context examples are selected relative to the target during training. The two prevailing strategies turn out to be accurate over complementary parts of this spectrum: random contexts yield a model that gains little from related examples even when they are placed in its context, while retrieved similar contexts weaken accuracy on targets lacking close neighbors and raise the propensity to copy labels from context. Probes tracking in-weights learning, in-context learning, and copying trace these failures to distinct training dynamics, and show that introducing contrast in target-context similarity both within a context and across batches, restores robustness across the entire spectrum.

Deeptanshu Malu, Deevyanshu Malu, Aditya Nemiwal +1

IIT Bombay

In-Context Example Selection LLM Fine-Tuning In-Context Learning Language Model Robustness

Apr 1, 2026

Infeasibility Aware Large Language Models for Combinatorial Optimization

Large language models (LLMs) are increasingly explored for NP-hard combinatorial optimization problems, but most existing methods emphasize feasible-instance solution generation and do not explicitly address infeasibility detection. We propose an infeasibility-aware framework that combines certifiable dataset construction, supervised fine-tuning, and LLM-assisted downstream search. For the minor-embedding problem, we introduce a new mathematical programming formulation together with provable zero-phase infeasibility screening, which enables scalable construction of training instances labeled either as feasible with structured certificates or as certifiably infeasible. Using training data generated through this exact optimization pipeline, we show that an 8B-parameter LLM can be fine-tuned to jointly perform solution generation and infeasibility detection. We further utilize LLM outputs as warm starts for downstream local search, providing a practical way to accelerate optimization even when the LLM outputs are imperfect. Experiments show that our fine-tuned model improves overall accuracy by up to 30% over GPT-5.2; meanwhile LLM-guided warm starts provide up to

2\times

speedup compared with starting from scratch in downstream local search.

Yakun Wang, Min Chen, Zeguan Wu +3

Lehigh University, Bethlehem, PA 18015, USA · Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA · Johnson & Johnson, New Brunswick, NJ 08933, USA

Combinatorial Optimization Large Language Model-Guided Optimization LLM Fine-Tuning

Apr 1, 2026

Brainstacks: Cross-Domain Cognitive Capabilities via Frozen MoE-LoRA Stacks for Continual LLM Learning

We present Brainstacks, a modular architecture for continual multi-domain fine-tuning of large language models that packages domain expertise as frozen adapter stacks composing additively on a shared frozen base at inference. Five interlocking components: (1) MoE-LoRA with Shazeer-style noisy top-2 routing across all seven transformer projections under QLoRA 4-bit quantization with rsLoRA scaling; (2) an inner loop performing residual boosting by freezing trained stacks and adding new ones; (3) an outer loop training sequential domain-specific stacks with curriculum-ordered dependencies; (4) null-space projection via randomized SVD constraining new stacks to subspaces orthogonal to prior directions, achieving zero forgetting in isolation; (5) an outcome-based sigmoid meta-router trained on empirically discovered domain-combination targets that selectively weights stacks, enabling cross-domain composition. Two boundary experiments: (6) PSN pretraining on a randomly initialized model; (7) per-domain RL (DPO/GRPO) validating compatibility with post-SFT alignment. Validated on TinyLlama-1.1B (4 domains, 9 stacks) and Gemma 3 12B IT (5 domains, 10 stacks), MoE-LoRA achieves 2.5x faster convergence than parameter-matched single LoRA, residual boosting breaks through the single-stack ceiling, and the routed system recovers generation quality destroyed by ungated stack accumulation. The central finding: the outcome-based router discovers that domain stacks encode transferable cognitive primitives (instruction-following clarity, numerical reasoning, procedural logic, chain-of-thought structure) rather than domain-specific knowledge, with medical prompts routing to chat+math stacks in 97% of cases despite zero medical data in those stacks.

Mohammad R. Abu Ayyash

Brains Build Research, Ramallah, Palestine

Continual Learning for LLMs LLM Fine-Tuning Cross-Domain Transfer Learning Mixture-of-Experts Language Models

Apr 1, 2026

Positional Cognitive Specialization: Where Do LLMs Learn To Comprehend and Speak Your Language?

Adapting large language models (LLMs) to new languages is an expensive and opaque process. Understanding how language models acquire new languages and multilingual abilities is key to achieve efficient adaptation. Prior work on multilingual interpretability research focuses primarily on how trained models process multilingual instructions, leaving unexplored the mechanisms through which they acquire new languages during training. We investigate these training dynamics on decoder-only transformers through the lens of two functional cognitive specializations: language perception (input comprehension) and production (output generation). Through experiments on low-resource languages, we demonstrate how perceptual and productive specialization emerges in different regions of a language model by running layer ablation sweeps from the model's input and output directions. Based on the observed specialization patterns, we propose CogSym, a layer-wise heuristic that enables effective adaptation by exclusively fine-tuning a few early and late layers. We show that tuning only the 25% outermost layers achieves downstream task performance within 2-3% deviation from the full fine-tuning baseline. CogSym yields consistent performance with adapter methods such as LoRA, showcasing generalization beyond full fine-tuning. These findings provide insights to better understand how LLMs learn new languages and push toward accessible and inclusive language modeling.

Luis Frentzen Salim, Lun-Wei Ku, Hsing-Kuo Kenneth Pao

Institute of Information Science, Academia Sinica · Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology

Multilingual Language Models LLM Fine-Tuning LLM Interpretability Decoder-Only Language Models

Apr 1, 2026

More Human, More Efficient: Aligning Annotations with Quantized SLMs

As Large Language Model (LLM) capabilities advance, the demand for high-quality annotation of exponentially increasing text corpora has outpaced human capacity, leading to the widespread adoption of LLMs in automatic evaluation and annotation. However, proprietary LLMs often exhibit systematic biases that diverge from human expert consensus, lacks reproducibility, and raises data privacy concerns. Our work examines the viability of finetuning a quantized Small Language Model of 1.7B parameter size on limited human-annotated data to serve as a highly aligned, deterministic evaluator and annotator. By implementing a custom, multi-dimensional rubric framework and simple augmentation and regularization techniques, the proposed approach achieves higher inter-annotator agreement (0.23 points increase in Krippendorff's

α

) than the best performing state-of-the-art proprietary LLM. We also demonstrate the generalizability of the proposed training pipeline on a separate emotion classification task. The results show that task-specific alignment and efficient 4-bit quantized fine-tuning provide superior open-source alternative to using proprietary models for evaluation and annotation. Our finetuning approach is publicly available at https://github.com/jylee-k/slm-judge.

Jiayu Wang, Junyoung Lee

Home Team Science and Technology Agency

LLM-Assisted Annotation LLM Fine-Tuning LLM Evaluation LLM Alignment Small Language Models

Mar 31, 2026

Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMs

Large language models (LLMs) are widely applied to data analytics over documents, yet direct reasoning over long, noisy documents remains brittle and error-prone. Hence, we study document question answering (QA) that consolidates dispersed evidence into a structured output (e.g., a table, graph, or chunks) to support reliable, verifiable QA. We propose a two-pillar framework, LiteCoST, to achieve both high accuracy and low latency with small language models (SLMs). Pillar 1: Chain-of-Structured-Thought (CoST). We introduce a CoST template, a schema-aware instruction that guides a strong LLM to produce both a step-wise CoST trace and the corresponding structured output. The process induces a minimal structure, normalizes entities/units, aligns records, serializes the output, and verifies/refines it, yielding auditable supervision. Pillar 2: SLM fine-tuning. The compact models are trained on LLM-generated CoST data in two stages: Supervised Fine-Tuning for structural alignment, followed by Group Relative Policy Optimization (GRPO) incorporating triple rewards for answer/format quality and process consistency. By distilling structure-first behavior into SLMs, this approach achieves LLM-comparable quality on multi-domain long-document QA using 3B/7B SLMs, while delivering 2-4x lower latency than GPT-4o and DeepSeek-R1 (671B). The code is available at https://github.com/HKUSTDial/LiteCoST.

Zhuowen Liang, Xiaotian Lin, Zhengxuan Zhang +3

The Hong Kong University of Science and Technology (Guangzhou) · EvenUp, USA

Question Answering Document QA LLM Prompting LLM Fine-Tuning

Mar 29, 2026

KazByte: Adapting Qwen models to Kazakh via Byte-level Adapter

Large language models fragment Kazakh text into many more tokens than equivalent English text, because their tokenizers were built for high-resource languages. This tokenizer tax inflates compute, shortens the effective context window, and weakens the model's grip on Kazakh morphology. We propose to bypass the tokenizer entirely by feeding raw bytes through a small adapter that learns to speak the internal language of a frozen Qwen2.5-7B. Once the adapter is trained, we freeze it and fine-tune only the attention layers of Qwen on Kazakh text. Our central hypothesis is that this two-stage process -- first teach the interface, then adapt the model -- should match or exceed the accuracy of the original Qwen2.5-7B on standard Kazakh benchmarks. This report describes the ByteKaz architecture and training protocol. Empirical validation is ongoing; this version stakes the design and hypotheses for the record.

Rauan Akylzhanov

Independent Researcher, Almaty, Kazakhstan

Low-Resource Language Processing Byte-Level Language Model LLM Fine-Tuning Domain Adaptation for LLMs

Mar 29, 2026

QU-NLP at QIAS 2026: Multi-Stage QLoRA Fine-Tuning for Arabic Islamic Inheritance Reasoning

Islamic inheritance law (ilm al-mawarıth) presents a challenging domain for evaluating large language models' structured reasoning capabilities, requiring multi-step legal analysis, rule-based blocking decisions, and precise fractional calculations. We present QU-NLP's submission to the QIAS 2026 shared task on Arabic Islamic inheritance reasoning. Our approach employs a multi-stage Quantized Low-Rank Adaptation (QLoRA) fine-tuning strategy on Qwen3-4B: (1) domain adaptation on 3,166 Islamic fatwa records to acquire inheritance terminology and jurisprudential reasoning patterns, followed by (2) task-specific training on 12,000 structured inheritance cases to optimize JSON-formatted output generation. Using 4-bit NF4 quantization with rank-128 LoRA adapters, our model achieves 90% MIR-E (Mawarith Inheritance Reasoning Evaluation) score on the test set, demonstrating competitive performance while requiring minimal computational resources. Our results show that domain-specific pre-adaptation combined with structured output training enables small language models to perform complex legal reasoning tasks effectively comparing to commercial systems such as Gemini-2.5-flash.

Mohammad AL-Smadi

Qatar University Doha, Qatar

Legal Reasoning Arabic NLP LLM Fine-Tuning Domain Adaptation for LLMs

Mar 28, 2026

Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa

Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health care system. This paper presents an experimental study on the development of a domain-specific Large Language Model (DS-LLM) for TB care that can help to alleviate the burden on patients and healthcare providers. To achieve this, a literature review was conducted to understand current LLM development strategies, specifically in the medical domain. Thereafter, data were collected from South African TB guidelines, selected TB literature, and existing benchmark medical datasets. We performed LLM fine-tuning by using the Quantised Low-Rank Adaptation (QLoRA) algorithm on a medical LLM (BioMistral-7B), and also implemented Retrieval-Augmented Generation using GraphRAG. The developed DS-LLM was evaluated against the base BioMistral-7B model and a general-purpose LLM using a mix of automated metrics and quantitative ratings. The results show that the DS-LLM had better performance compared to the base model in terms of its contextual alignment (lexical, semantic, and knowledge) for TB care in South Africa.

Thokozile Khosa, Olawande Daramola

Department of Computer Science, University of Pretoria, South Africa · Department of Informatics, University of Pretoria, South Africa

Medical LLMs Healthcare LLM Fine-Tuning Clinical Language Model Evaluation

Mar 27, 2026

The Last Fingerprint: How Markdown Training Shapes LLM Prose

Large language models produce em dashes at varying rates, and the observation that some models "overuse" them has become one of the most widely discussed markers of AI-generated text. Yet no mechanistic account of this pattern exists, and the parallel observation that LLMs default to markdown-formatted output has never been connected to it. We propose that the em dash is markdown leaking into prose -- the smallest surviving unit of the structural orientation that LLMs acquire from markdown-saturated training corpora. We present a five-step genealogy connecting training data composition, structural internalization, the dual-register status of the em dash, and post-training amplification. We test this with a two-condition suppression experiment across twelve models from five providers (Anthropic, OpenAI, Meta, Google, DeepSeek): when models are instructed to avoid markdown formatting, overt features (headers, bullets, bold) are eliminated or nearly eliminated, but em dashes persist -- except in Meta's Llama models, which produce none at all. Em dash frequency and suppression resistance vary from 0.0 per 1,000 words (Llama) to 9.1 (GPT-4.1 under suppression), functioning as a signature of the specific fine-tuning procedure applied. A three-condition suppression gradient shows that even explicit em dash prohibition fails to eliminate the artifact in some models, and a base-vs-instruct comparison confirms that the latent tendency exists pre-RLHF. These findings connect two previously isolated online discourses and reframe em dash frequency as a diagnostic of fine-tuning methodology rather than a stylistic defect.

E. M. Freeburg

Independent Researcher

LLM Training Language Model Fingerprinting LLM Fine-Tuning

Mar 26, 2026

Large Language Model as Token Compressor and Decompressor

In this paper, we study whether an off-the-shelf LLM can be adapted into a discrete, variable-length token compressor and decompressor for long-context processing. To this end, we design a self-expressive autoencoding framework that fine-tunes a pretrained LLM with lightweight LoRA adapters to map long texts into compact sequences of learned latent codes, termed Z-tokens, and to decode them back into natural language or task outputs. The resulting representation is content-adaptive: less predictable or information-dense segments can receive more Z-tokens, while redundant regions can be represented more compactly through a budget-aware length regularizer. Our method is evaluated on long-context datasets such as Wikipedia, CNN/DailyMail, HotpotQA, and QuALITY, showing that it preserves reconstruction quality and downstream performance while reducing effective context length, generation-stage memory usage, and end-to-end latency. This simple design supports both direct decoding from compressed contexts and autoregressive generation in the Z-token space, providing a practical interface for efficient long-context inference.

Wenbing Li, Yiran Wang, Zikai Song +4

LLM Context Compression LLM Compression LLM Inference Acceleration Long-Context Language Modeling LLM Fine-Tuning

Mar 26, 2026

AnyDoc: Enhancing Document Generation via Large-Scale HTML/CSS Data Synthesis and Height-Aware Reinforcement Optimization

Document generation has gained growing attention in the field of AI-driven content creation. In this work, we push its boundaries by introducing AnyDoc, a framework capable of handling multiple generation tasks across a wide spectrum of document categories, all represented in a unified HTML/CSS format. To overcome the limited coverage and scale of existing human-crafted document datasets, AnyDoc first establishes a scalable data synthesis pipeline to automatically generate documents in HTML/CSS form. This pipeline yields DocHTML, a large-scale dataset containing 265,206 document samples, while spanning 111 categories and 32 distinct styles. Additionally, all documents are equipped with comprehensive metadata, including design intentions, HTML/CSS source code, visual assets, and rendered screenshots. Building on the curated dataset, AnyDoc fine-tunes multi-modal large language models (MLLMs) to achieve three practical document generation tasks: intention-to-document, document derendering, and element-to-document. To address the content overflow issue observed during fine-tuning, AnyDoc further incorporates a height-aware reinforcement learning (HARL) post-training procedure. By defining a reward function based on the difference between predicted and target document heights, overflow is penalized and gradually mitigated during HARL, thereby enhancing overall performance. Qualitative and quantitative experiments demonstrate that AnyDoc outperforms both general-purpose MLLMs and task-specific baselines across all three tasks.

Jiawei Lin, Wanrong Zhu, Vlad I Morariu +1

Xi’an Jiaotong University · Adobe Research

Text Generation Synthetic Data Generation for Language Models LLM Fine-Tuning Multimodal Large Language Models RL for Generative Models

Mar 26, 2026

OMIND: Framework for Knowledge Grounded Finetuning and Multi-Turn Dialogue Benchmark for Mental Health LLMs

Large Language Models (LLMs) have shown remarkable capabilities for complex tasks, yet adaptation in medical domain, specifically mental health, poses specific challenges. Mental health is a rising concern globally with LLMs having large potential to help address the same. We highlight three primary challenges for LLMs in mental health - lack of high quality interpretable and knowledge grounded training data; training paradigms restricted to core capabilities, and evaluation of multi turn dialogue settings. Addressing it, we present oMind framework which includes training and aligning LLM agents for diverse capabilities including conversations; high quality ~164k multi-task SFT dataset, as a result of our generation pipeline based on Structured Knowledge retrieval, LLM based pruning, and review actions. We also introduce oMind-Chat - a novel multi turn benchmark dataset with expert annotated turn level and conversation level rubrics. Our diverse experiments on both core capabilities and conversations shows oMind LLMs consistently outperform baselines. oMind-LLM also shows significantly better reasoning with up to 80% win rate.

Suraj Racha, Prashant Harish Joshi, Utkarsh Maurya +6

Indian Institute of Technology Bombay

Medical LLMs LLM Fine-Tuning LLM Grounding Clinical Language Model Evaluation

Mar 25, 2026

LiFT: How to Enable In-Context Learning for Longitudinal Modelling

Longitudinal NLP tasks such as mental health monitoring and stance evolution require modeling temporally ordered text to track persistence and detect change. Such tasks also suffer from data scarcity, often involving rare events and sparsely annotated data. Large Language Models (LLMs) can learn from small amounts of data through in-context learning (ICL), which is particularly important in low-data resource scenarios. However, when it comes to tracking evolving interactions or identifying rare events or changes, LLMs fall short in both zero- and few-shot ICL. To address this limitation, we introduce LiFT, a model-agnostic longitudinal Instruction Fine-Tuning framework that is able to induce ICL behaviour in LLMs for longitudinal tasks. LiFT unifies diverse tasks through a shared instruction schema that leverages sequential dependencies, curriculum learning, and temporal conditioning. We evaluate LiFT on five longitudinal datasets under zero-, one-, and three-shot settings, demonstrating that LiFT delivers superior performance to IFT models under identical ICL conditions. Furthermore, we demonstrate that LiFT yields larger gains for minority classes, meaningfully attends to recent demonstration history, and develops transferable longitudinal modeling capabilities beyond its training tasks

Iqra Ali, Talia Tseriotou, Mahmud Elahi Akhter +2

Queen Mary University of London (UK) · The Alan Turing Institute (UK)

In-Context Learning LLM Fine-Tuning Temporal Reasoning in Language Models

Mar 25, 2026

Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset

Clinical documentation is a critical factor for patient safety, diagnosis, and continuity of care. The administrative burden of EHRs is a significant factor in physician burnout. This is a critical issue for low-resource languages, including Finnish. This study aims to investigate the effectiveness of a domain-aligned natural language processing (NLP); large language model for medical transcription in Finnish by fine-tuning LLaMA 3.1-8B on a small validated corpus of simulated clinical conversations by students at Metropolia University of Applied Sciences. The fine-tuning process for medical transcription used a controlled preprocessing and optimization approach. The fine-tuning effectiveness was evaluated by sevenfold cross-validation. The evaluation metrics for fine-tuned LLaMA 3.1-8B were BLEU = 0.1214, ROUGE-L = 0.4982, and BERTScore F1 = 0.8230. The results showed a low n-gram overlap but a strong semantic similarity with reference transcripts. This study indicate that fine-tuning can be an effective approach for translation of medical discourse in spoken Finnish and support the feasibility of fine-tuning a privacy-oriented domain-specific large language model for clinical documentation in Finnish. Beside that provide directions for future work.

Mohammed Nowshad Ruhani Chowdhury, Mohammed Nowaz Rabbani Chowdhury, Sakari Lukkarinen

School of ICT and Industrial Management, Metropolia University of Applied Sciences, Helsinki, Finland · Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA

LLM Fine-Tuning Medical LLMs Clinical Language Model Evaluation Low-Resource Language Processing

Mar 25, 2026

Fine-Tuning A Large Language Model for Systematic Review Screening

Systematic reviews traditionally have taken considerable amounts of human time and energy to complete, in part due to the extensive number of titles and abstracts that must be reviewed for potential inclusion. Recently, researchers have begun to explore how to use large language models (LLMs) to make this process more efficient. However, research to date has shown inconsistent results. We posit this is because prompting alone may not provide sufficient context for the model(s) to perform well. In this study, we fine-tune a small 1.2 billion parameter open-weight LLM specifically for study screening in the context of a systematic review in which humans rated more than 8500 titles and abstracts for potential inclusion. Our results showed strong performance improvements from the fine-tuned model, with the weighted F1 score improving 80.79% compared to the base model. When run on the full dataset of 8,277 studies, the fine-tuned model had 86.40% agreement with the human coder, a 91.18% true positive rate, a 86.38% true negative rate, and perfect agreement across multiple inference runs. Taken together, our results show that there is promise for fine-tuning LLMs for title and abstract screening in large-scale systematic reviews.

Kweku Yamoah, Noah Schroeder, Emmanuel Dorley +2

University of Florida, Gainesville, FL, 32611

Systematic Review Automation LLM Fine-Tuning Evidence Selection LLM Evaluation

Mar 25, 2026

Lightweight Multimodal LLM-Enabled Cost-Effective Defect Grading of Power Transmission Equipment

Defect grading of power transmission equipment (DGPTE) is crucial to the stability of electric energy transmission. Although existing machine learning methods exhibit strong capabilities in defect detection, they are plagued by difficulties in integrating expert experience and facing class imbalance in more refined defect grading field. To address this issue, this paper introduces a novel defect grading framework based on multimodal large language model (MLLM). Specifically, this approach maximizes the commercial MLLMs' potential of DGPTE through in-context learning and obtains the state-of-te-art (SOTA) model. By sending a secondary request to this model, a small number of chain of thought-based question-answer pairs (Q&As) are generated, which effectively reduces the cost of manual annotation. In this way, these high-quality interpretable Q&As are used to train Qwen3-VL-8B via Low-Rank Adaption-based supervised fine-tuning (SFT). Experimental results on three DGPTE tasks demonstrate that fine-tuning only the language model layer yields the SOTA performance. Furthermore, multi-task joint fine-tuning verifies the feasibility of handling multiple grading tasks within only a single lightweight MLLM.

Tao Wang, Lipeng Zhu, Jiayong Li +2

Multimodal Large Language Models LLM Fine-Tuning

Mar 25, 2026

Schema on the Inside: A Two-Phase Fine-Tuning Method for High-Efficiency Text-to-SQL at Scale

Applying large, proprietary API-based language models to text-to-SQL tasks poses a significant industry challenge: reliance on massive, schema-heavy prompts results in prohibitive per-token API costs and high latency, hindering scalable production deployment. We present a specialized, self-hosted 8B-parameter model designed for a conversational bot in CriQ, a sister app to Dream11, India's largest fantasy sports platform with over 250 million users, that answers user queries about cricket statistics. Our novel two-phase supervised fine-tuning approach enables the model to internalize the entire database schema, eliminating the need for long-context prompts. This reduces input tokens by over 99%, from a 17k-token baseline to fewer than 100, and replaces costly external API calls with efficient local inference. The resulting system achieves 98.4% execution success and 92.5% semantic accuracy, substantially outperforming a prompt-engineered baseline using Google's Gemini Flash 2.0 (95.6% execution, 89.4% semantic accuracy). These results demonstrate a practical path toward high-precision, low-latency text-to-SQL applications using domain-specialized, self-hosted language models in large-scale production environments.

Chinmay Soni, Shivam Chourasia, Gaurav Kumar +1

Sporta Technologies Private Limited, Mumbai, Maharashtra, India

Sports Analytics Text-to-SQL LLM Fine-Tuning Efficient Language Model Inference LLM Grounding