Knowledge Augmentation for Language Models

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11 papers in the last four weeks, up 120% on the four weeks before. 0.1% of all new papers.

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AI-Friendly LaTeX: Using LaTeX Code as a Knowledge Source for Retrieval-Augmented Generation
May 21, 2026Tom VerhoeffRetrieval-Augmented Generation Knowledge Augmentation for Language Models
Department of Mathematics & Computer Science, Eindhoven University of Technology
More Context, Larger Models, or Moral Knowledge? A Systematic Study of Schwartz Value Detection in Political Texts
May 21, 2026Víctor Yeste, Paolo RossoLLM Evaluation Knowledge Augmentation for Language Models
1PRHLT Research Center, Universitat Politècnica de València, Spain · School of Science, Engineering and Design, Universidad Europea de Valencia, Spain · 3Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI)
Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation
May 21, 2026Genoveffa Martone, Helena Bonaldi, Marco GueriniKnowledge Augmentation for Language Models Text Generation
Fondazione Bruno Kessler, Italy · Università Cattolica del Sacro Cuore, Italy
From Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languages
May 21, 2026Siddhant Hitesh Mantri, Dhara Gorasiya, Malhar Kulkarni +1Knowledge Augmentation for Language Models Synthetic Data Generation for Language Models
NMIMS, Mumbai · CFILT, IIT Bombay
Do LLMs Know What Luxembourgish Borrows? Probing Lexical Neology in Low-Resource Multilingual Models
May 20, 2026Nina Hosseini-KivananiMultilingual Language Model Evaluation Knowledge Augmentation for Language Models
University of Luxembourg, Luxembourg · Radio Télévision Luxembourg (RTL), Luxembourg
KoRe: Compact Knowledge Representations for Large Language Models
May 19, 2026Davide Cavicchini, Fausto Giunchiglia, Jacopo StaianoLLM Grounding Knowledge Augmentation for Language Models
University of Trento, Via Calepina, 14, 38122 Trento TN, Italy
NGM: A Plug-and-Play Training-Free Memory Module for LLMs
May 16, 2026Yuwen Qu, Wenhui Dong, Chenyang Si +1Memory-Augmented Language Models Knowledge Augmentation for Language Models
Nanjing University
MixSD: Mixed Contextual Self-Distillation for Knowledge Injection
May 16, 2026Jiarui Liu, Lechen Zhang, Yongjin Yang +5Knowledge Augmentation for Language Models Factual Knowledge in Language Models
Carnegie Mellon University · Jinesis Lab, University of Toronto & Vector Institute · University of Illinois Urbana-Champaign +6
SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation
May 15, 2026Xin Zhang, Yang Cao, Baoxing Wu +2LLM Grounding Knowledge Augmentation for Language Models
School of Information Science and Engineering, Chongqing Jiaotong University · School of Computer Science and Technology, Chongqing University of Posts and Telecommunications
MeMo: Memory as a Model
May 14, 2026Ryan Wei Heng Quek, Sanghyuk Lee, Alfred Wei Lun Leong +6Retrieval-Augmented Generation Memory-Augmented Language Models
Institute of Data Science, National University of Singapore, Singapore · Integrative Sciences and Engineering Programme, NUSGS, Singapore · Agency for Science, Technology, Research (A*STAR), Singapore +6
Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model
May 13, 2026Tianhao Qiu, Xiaojun ChenKnowledge Augmentation for Language Models Synthetic Data Generation
Shenzhen University Shenzhen, China
Context Training with Active Information Seeking
May 13, 2026Zeyu Huang, Adhiguna Kuncoro, Qixuan Feng +4Knowledge Augmentation for Language Models LLM Prompting
The University of Edinburgh · Google DeepMind
BadSKP: Backdoor Attacks on Knowledge Graph-Enhanced LLMs with Soft Prompts
May 12, 2026Xiaoting Lyu, Yufei Han, Hangwei Qian +6LLM Backdoor Attacks Knowledge Augmentation for Language Models
Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, 710049, Xi’an, Shaanxi, China · INRIA, 35042, Rennes, Bretagne, France · CFAR, A*STAR, 138632, Singapore +4
Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation
May 12, 2026Qiuyu Ding, Heng-Da Xu, Wei Zhang +4Sequential Recommendation Knowledge Augmentation for Language Models
Amap, Alibaba Group · Xi’an Jiaotong University
DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning
May 11, 2026Haoyu Huang, Jiaxin Bai, Shujie Liu +6Knowledge Editing Knowledge Augmentation for Language Models
1HKUST · 2HKBU · 3Microsoft Research Asia
K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs
May 10, 2026Hao Liang, Qihan Lin, Zhaoyang Han +5KG Construction Knowledge Augmentation for Language Models
1Peking University · Institute for Advanced Algorithms Research, Shanghai · 3OriginHub Technology +1
CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators
May 9, 2026Nicolás Astorga, Anita Kriz, Mihaela van der SchaarStructural Causal Models Knowledge Augmentation for Language Models
DAMTP, University of Cambridge, Cambridge, UK
XPERT: Expert Knowledge Transfer for Effective Training of Language Models
May 9, 2026Chang Liu, Boyu Shi, Xu Yang +1Mixture-of-Experts Language Models Knowledge Augmentation for Language Models
School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China.
CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs
May 7, 2026Zhen Zeng, Leijiang Gu, Feng Li +2Knowledge Augmentation for Language Models Multimodal Large Language Models
1Hefei University of Technology · 2Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China · School of Information Engineering, Minzu University of China
Graph-Augmented LLMs for Swiss MP Ideology Prediction
May 6, 2026Yifei Yuan, Luis Salamanca, Sophia Schlosser +1Knowledge Augmentation for Language Models Computational Social Science
Swiss Data Science Center, ETH Zürich, Zürich, Switzerland · Department of Political Science, University of Zürich, Zürich, Switzerland
Memory as a Markov Matrix: Sample Efficient Knowledge Expansion via Token-to-Dictionary Mapping
May 5, 2026Kaustubh Pethkar, Ziyang Xiong, Zuofeng Shang +1Continual Learning for LLMs Memorization in Language Models
New Jersey Institute of Technology · University of California, Berkeley
Position: How can Graphs Help Large Language Models?
May 4, 2026Xiyuan Wang, Yi Hu, Yanbo Wang +2LLM Grounding Knowledge Augmentation for Language Models
Institute for Artificial Intelligence, Peking University, Beijing 100871, China · School of Computer Sciences, Beijing University of Posts and Telecommunications, Beijing 100876, China
Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines
May 1, 2026Hugo Abonizio, Filipe Rocha Lopes, Roberto Lotufo +1Knowledge Augmentation for Language Models Healthcare
FEEC, UNICAMP, Campinas, Brazil · NoHarm.ai, Brazil · Maritaca AI, Brazil
ChipLingo: A Systematic Training Framework for Large Language Models in EDA
Apr 30, 2026Lei Li, Xingwen Yu, Jianguo Ni +4Knowledge Augmentation for Language Models LLM Fine-Tuning
Ickylin AI Team
Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora
Apr 27, 2026Chenkai Pan, Xinglong Xu, Yuhang Xu +6LLM Evaluation Knowledge Augmentation for Language Models
Zhejiang University · University of Chinese Academy of Sciences · Shanghai Artificial Intelligence Laboratory
Learning from Execution: Self-Evolving Memory for Private-Library Code Generation
Apr 27, 2026Mofei Li, Taozhi Chen, Guowei Yang +1Memory-Augmented Language Models Knowledge Augmentation for Language Models
College of AI Tsinghua University Beijing, China · Fitten Tech Co., Ltd. Beijing, China · Emory University Atlanta, GA, USA
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning
Apr 27, 2026Yimin Deng, Zhenxi Lin, Yejing Wang +9Knowledge Augmentation for Language Models Medical Diagnosis
Xi’an Jiaotong University · City University of Hong Kong · Tencent Jarvis Lab +1
K-SENSE: A Knowledge-Guided Self-Augmented Encoder for Neuro-Semantic Evaluation of Mental Health Conditions on Social Media
Apr 26, 2026Vijay YadavSocial Media Analysis Knowledge Augmentation for Language Models
School of Psychology, University of New South Wales Sydney, Australia
Evolve: A Persistent Knowledge Lifecycle for Small Language Models
Apr 25, 2026Dikran HovagimianRetrieval-Augmented Generation Memory-Augmented Language Models
Independent Researcher
Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition
Apr 25, 2026Rujing Yao, Yufei Shi, Yang Wu +5Privacy-Preserving Language Models Knowledge Augmentation for Language Models
1Nankai University, China · 2The Hong Kong Polytechnic University, China · 3Worcester Polytechnic Institute, USA +3

May 21, 2026, cs.IRJ/K move · Enter open · S save

AI-Friendly LaTeX: Using LaTeX Code as a Knowledge Source for Retrieval-Augmented Generation

Tom Verhoeff

Department of Mathematics & Computer Science, Eindhoven University of Technology

Retrieval-Augmented Generation Knowledge Augmentation for Language Models Document Parsing

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Large language models can answer questions about textbooks, lecture notes, and programming exercises more reliably when their answers are grounded in an explicit knowledge source. Retrieval-augmented generation (RAG) is a common approach: relevant fragments of a document are retrieved and inserted into the model context before answering. For mathematical and technical material, the original LaTeX source can be a better starting point than a PDF, because it contains structural information, labels, sectioning commands, macros, and authorial intent that are often lost or distorted in PDF extraction. However, LaTeX source is not automatically AI-friendly. Cross-references must be resolved, custom macros must be interpreted, exercises and examples must be identified, and author-supplied semantic metadata may be needed. This article describes a focused preprocessing approach for turning LaTeX source, together with its compiled auxiliary files and optional author annotations, into Markdown and JSONL chunks suitable for indexing in a vector database.

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