Factual Knowledge in Language Models

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Overview

This field studies which facts language models learn from training data, when they can retrieve them, and whether their answers match reliable evidence. Researchers use closed-book question-answering benchmarks such as SimpleQA and TruthfulQA, alongside multilingual, temporal, and long-tail tests; probing and causal tracing help investigate how knowledge is represented and accessed. Common interventions include retrieval-augmented generation, reinforcement learning, fine-tuning, and targeted knowledge editing. These methods can improve recall or update answers, but a central challenge is doing so without introducing hallucinations, losing other knowledge, or leaving gaps in unfamiliar languages, domains, and multi-step reasoning.

Latest papers

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195 papers36 papers in the last four weeks, up 57% on the four weeks before. 0.2% of all new papers.

Jan 16, 2026

Relational Linearity is a Predictor of Hallucinations

Hallucination is a central failure mode of language models (LMs). We focus on hallucinations in response to questions like: "Which instrument did Glenn Gould play?", but we ask these questions for synthetic entities designed to be unknown to the model. We find that LMs like Gemma-7B-IT frequently hallucinate, i.e., they have difficulty recognizing that the hallucinated fact is not part of their knowledge. Based on the idea of linear relational embeddings, we put forward the following hypothesis. (i) Due to the abstract scheme that is used to represent them, LMs can easily produce plausible objects for non-existing subjects of linear relations, which can lead to hallucinations. (ii) For nonlinear relations, this mechanism for producing an object is not available and so a hallucination is easier to avoid. To test this hypothesis, we create SynthHal, a synthetic unknown-entity benchmark for 15 relations. We find that across four instruction-tuned models, relational linearity is a strong predictor of models hallucinating an object for an unknown subject vs refusing to give an answer, with correlations r∈[.58,.84]r \in [.58, .84]. While this is not direct evidence for the hypothesized causal mechanism, it is suggestive and opens up a new line of inquiry into understanding LM hallucinations.

Yuetian Lu, Yihong Liu, Sebastian Gerstner +3

Center for Information and Language Processing (CIS), LMU Munich · Ubiquitous Knowledge Processing Lab (UKP Lab), Technical University of Darmstadt · Munich Center for Machine Learning (MCML)

LLM InterpretabilityHallucination in Language ModelsLLM ReliabilityFactual Knowledge in Language Models
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Nov 8, 2025

Reinforcement Learning Improves Traversal of Parametric Knowledge in LLMs

Reinforcement learning (RL) is often credited with improving language model reasoning at the expense of knowledge. We challenge this narrative by showing that reasoning models consistently outperform their instruction-tuned versions on pure knowledge recall tasks. These gains do not reflect newly acquired information, but rather an improved procedural skill in navigating and searching existing knowledge hierarchies within the model parameters. Structured prompting, which explicitly guides models through hierarchical traversal -- recovers most of the instruct-reasoning gap across five model families. A controlled RL experiment on unseen, non-extractable facts improves recall of held-out frequent but previously inaccessible facts, ruling out simple data exposure. On depth-stratified retrieval tasks, reasoning models exhibit superior traversal as retrieval depth grows. Layerwise activation analysis further shows that while factual representations maintain high cosine similarity between instruct and reasoning models, query representations diverge noticeably, indicating that reasoning primarily reshapes how models traverse knowledge rather than the knowledge representation itself. Finally, we find that distilled models often fail to match reasoning models on knowledge recall because they imitate self-correction without acquiring the exploratory behavior needed for hierarchical navigation. Together, these findings suggest that improving factual recall in LLMs depends not only on expanding what models know but also on teaching them to navigate it -- motivating future post-training methods that optimize traversal.

Renfei Zhang, Manasa Kaniselvan, Rylan Schaeffer abd Niloofar Mireshghallah

Carnegie Mellon University · MIT · Stanford University

RL for Language Model ReasoningFactual Knowledge in Language ModelsLarge Reasoning Models
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Nov 3, 2025

Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace Disentanglement

Natural Language Explanations (NLEs) describe how Large Language Models (LLMs) make decisions by drawing on external Context Knowledge (CK) and Parametric Knowledge (PK). Understanding the interaction between these sources is key to assessing NLE grounding, yet these dynamics remain underexplored. Prior work has largely focused on i) single-step generation and ii) modeled PK--CK interaction as a binary choice within a rank-1 subspace. This approach overlooks richer interactions and how they unfold over longer generations, such as complementary or supportive knowledge. We propose a novel rank-2 projection subspace that disentangles PK and CK contributions more accurately and use it for the first multi-step analysis of knowledge interactions across longer NLE sequences. Experiments across four QA datasets and three open-weight LLMs demonstrate that rank-1 subspaces struggle to represent diverse interactions, whereas our rank-2 formulation captures them effectively, highlighting PK alignment for supportive interactions and CK alignment for conflicting ones. Our multi-step analysis reveals that hallucinated generations exhibit strong alignment with the PK direction, though context-faithful generations maintain a more balanced alignment between PK and CK.

Sekh Mainul Islam, Pepa Atanasova, Isabelle Augenstein

University of Copenhagen

Disentangled Representation LearningLLM GroundingKnowledge Conflicts in Language ModelsFactual Knowledge in Language Models
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Oct 21, 2025

KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs

We present Korean SimpleQA (KoSimpleQA)\textbf{Korean SimpleQA (KoSimpleQA)}, a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is designed to be challenging yet easy to grade, consisting of 938 short, fact-seeking questions with unambiguous answers. We conduct a comprehensive evaluation across a diverse set of open-source LLMs of varying sizes that support Korean, and find that even the strongest model generates correct answer only 31.6% of the time, underscoring the challenging nature of KoSimpleQA. Notably, performance rankings on KoSimpleQA differ substantially from those on the English SimpleQA, highlighting the unique value of our dataset. Furthermore, we observe that reasoning helps mitigate the cross-lingual knowledge gap in LLMs, which refers to disparities in their ability to manifest knowledge across languages. KoSimpleQA can be found at https://github.com/naver-ai/KoSimpleQA.

Donghyeon Ko, Kyubyung Chae, Yeguk Jin +6

Naver Cloud · Graduate School of Data Science, Seoul National University

Multilingual Language Model EvaluationLLM EvaluationFactual Knowledge in Language ModelsCultural Knowledge in Language Models
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Oct 20, 2025

Zoom In Disparities in Healthcare LLM Q&A

Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, raising concerns about the reliability and consistency of multilingual Large Language Models (LLMs). We systematically examine cross-lingual disparities in pre-training source and factuality alignment in LLM answers for multilingual healthcare Q&A across English, German, Turkish, Chinese (Mandarin), and Italian. We (i) constructed Multilingual Wiki Health Care (MultiWikiHealthCare), a multilingual dataset from Wikipedia; (ii) analyzed cross-lingual healthcare coverage; (iii) assessed LLM response alignment with these references; and (iv) conducted a case study on factual alignment through the use of contextual information and Retrieval-Augmented Generation (RAG). Our findings reveal substantial cross-lingual disparities in both Wikipedia coverage and LLM factual alignment. Across LLMs, responses align more with English Wikipedia, even when the prompts are non-English. Providing contextual excerpts from non-English Wikipedia at inference time effectively shifts factual alignment toward culturally relevant knowledge. These results highlight practical pathways for building more equitable, multilingual AI systems for healthcare.

Ipek Baris Schlicht, Burcu Sayin, Zhixue Zhao +5

Universitat Politècnica de València, Valencia, Spain · University of Trento, Trento, Italy · University of Sheffield, Sheffield, UK +5

Multilingual Language Model EvaluationKnowledge Augmentation for Language ModelsHealthcareMultilingual QAFactual Knowledge in Language ModelsClinical Language Model Evaluation
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Oct 8, 2025

Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5

LLMs are remarkable artifacts that have revolutionized a range of knowledge-intensive tasks. A significant contributor is their factual knowledge, which, to date, remains poorly understood, and is usually analyzed from biased samples. In this paper, we provide a framework and the results of a multi-dimensional analysis of GPTKB v1.5 (Hu et al., 2025a), a recursively elicited Knowledge Base (KB) of 100 million facts (or beliefs) of a frontier LLM, namely, GPT-4.1. Given the scale of the elicited facts, we provide a multi-dimensional approach to qualitatively and quantitatively analyze these facts as opposed to the mainstream fact completion benchmarks, which are prone to availability bias. We find that the models' factual knowledge differs quite significantly from established knowledge bases, and that its accuracy is significantly lower than indicated by previous benchmarks. We also find that inconsistency, ambiguity and hallucinations are major issues, shedding light on future research opportunities in neuro-symbolic AI concerning extraction, consolidation and verification of factual LLM knowledge.

Shrestha Ghosh, Luca Giordano, Yujia Hu +2

University of Tübingen · ScaDS.AI & TU Dresden · VNU University of Engineering and Technology

Factual Knowledge in Language ModelsLLM AuditingFactual Consistency EvaluationLLM Information Extraction
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Oct 5, 2025

What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models

Large language models (LLMs) are increasingly used as primary knowledge sources, yet their epistemic diversity - defined as the diversity of real-world claims in their outputs - has never been measured. Low epistemic diversity would pose a risk of knowledge collapse as homogeneous LLMs mediate a shrinking in the range of accessible information over time. The dominant paradigm is that overall LLM diversity is low, but this is always with respect to a single point in time, with no reference baseline or consideration for variation across countries. We address this gap in knowledge by performing the first systematic study of epistemic diversity in LLMs across time and cultural context, testing 27 LLMs on 155 topics covering 12 countries, resulting in 1.7M responses and 70M individual claims. We find that epistemic diversity has increased substantially over the past three years, a positive counter to recent diversity pessimism. However, despite progress, we find that every system is less diverse than a search baseline. This gap is not uniform: RAG can improve diversity, while large models are counterintuitively less diverse than smaller ones. Moreover, LLM parametric knowledge systematically reflects English over local-language knowledge for country specific topics. Together, these results demonstrate that while progress on epistemic diversity is tangible, it is insufficient and unevenly distributed.

Dustin Wright, Sarah Masud, Jared Moore +5

University of Copenhagen · Stanford University · University of Colorado Boulder +1

LLM EvaluationCultural Bias in Language ModelsFactual Knowledge in Language Models
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Oct 3, 2025

Sample, Align, Synthesize: Graph-Based Response Synthesis with ConGrs

Language models can be sampled multiple times to access the distribution underlying their responses, but existing methods cannot efficiently synthesize rich epistemic signals across different long-form responses. We introduce Consensus Graphs (ConGrs), a flexible DAG-based data structure that represents shared information, as well as semantic variation in a set of sampled LM responses to the same prompt. We construct ConGrs using a light-weight lexical sequence alignment algorithm from bioinformatics, supplemented by the targeted usage of a secondary LM judge. Further, we design task-dependent decoding methods to synthesize a single, final response from our ConGr data structure. Our experiments show that synthesizing responses from ConGrs improves factual precision on two biography generation tasks by up to 31% over an average response and reduces reliance on LM judges by more than 80% compared to other methods. We also use ConGrs for three refusal-based tasks requiring abstention on unanswerable queries and find that abstention rate is increased by up to 56%. We apply our approach to the MATH and AIME reasoning tasks and find an improvement over self-verification and majority vote baselines by up to 6 points of accuracy. We show that ConGrs provide a flexible method for capturing variation in LM responses and using the epistemic signals provided by response variation to synthesize more effective responses.

Sayan Ghosh, Shahzaib Saqib Warraich, Dhruv Tarsadiya +2

Thomas Lord Department of Computer Science, University of Southern California

Text GenerationSelf-Consistency in Language ModelsLLM ReasoningFactual Knowledge in Language Models
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Sep 26, 2025

KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues

Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains. However, existing benchmarks are limited to single-turn dialogue, while multi-turn dialogue benchmarks typically assess other orthogonal capabilities rather than knowledge-intensive factuality. To bridge this critical gap, we introduce KnowMT-Bench, a new benchmark designed to systematically evaluate MT-LFQA for LLMs across knowledge-intensive fields, including medicine, finance, and law. To faithfully assess the model's real-world performance, KnowMT-Bench employs a dynamic evaluation setting where models generate their own multi-turn dialogue histories given logically progressive question sequences. Our experiments on a diverse suite of LLMs show a clear degradation in both factual capability and information delivery efficiency in multi-turn dialogue. We further probe the underlying causes and investigate mitigation strategies spanning domain-specific finetuning, active context denoising, and RAG. The results show domain-dependent gains from finetuning, while active context denoising and RAG more consistently mitigate these issues, with RAG notably reversing this performance degradation. These findings underscore the importance of our benchmark for evaluating LLMs' conversational factual capabilities in real-world applications. Code and data are publicly available.\footnote{https://github.com/Sven-LI-sankyuu/KnowMT-Bench}

Junhao Chen, Yu Huang, Siyuan Li +7

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

Knowledge-Intensive QAFactual Knowledge in Language ModelsMulti-Turn Dialogue Evaluation
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Sep 3, 2025

LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations

Language models (LMs) increasingly drive real-world applications that require world knowledge. However, the internal processes through which models turn data into representations of knowledge and beliefs about the world are poorly understood. To facilitate such studies, we present LMEnt, a suite including (1) a knowledge-rich pretraining corpus, fully annotated with entity mentions based on Wikipedia, (2) an entity-based retrieval method over pretraining data that outperforms existing tools by as much as 80.4%, and (3) 12 pretrained LMs with up to 1B parameters and 4K intermediate checkpoints, with comparable performance to popular open-source models on knowledge tasks. Together, these resources provide a controlled environment for analyzing connections between entity mentions in pretraining data and downstream performance. We show the utility of LMEnt by studying knowledge acquisition over training, finding that entity co-occurrence and mention forms-which are difficult to study with existing tools-affect learning trends. Moreover, as LMs form stronger associations between entities, their facts are harder to edit in-context, whereas inconsistencies in model predictions over training are indicative of editing success. We release LMEnt to support studies of knowledge in LMs, including knowledge representations, plasticity, editing, attribution, hallucinations, and learning dynamics.

Daniela Gottesman, Alon Gilae-Dotan, Ido Cohen +4

Tel Aviv University · Mila – Quebec AI Institute & McGill University

Language Model PretrainingKnowledge EditingKnowledge RepresentationFactual Knowledge in Language Models
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Jul 8, 2025

GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge

Language models are powerful artifacts, yet their factual knowledge is still poorly understood, and inaccessible to ad-hoc browsing and scalable statistical analysis. This demonstration introduces GPTKB v1.5, a densely interlinked 100-million-triple knowledge base (KB) built for $14,000 from GPT-4.1, using the GPTKB methodology for massive-recursive LLM knowledge materialization. This demo focuses on three use cases: (1) link-traversal-based LLM knowledge exploration, (2) SPARQL-based structured LLM knowledge querying, (3) comparative exploration of the strengths and weaknesses of LLM knowledge. Massive-recursive LLM knowledge materialization is a groundbreaking opportunity both for the systematic analysis of LLM knowledge, as well as for automated KB construction.

Yujia Hu, Tuan-Phong Nguyen, Shrestha Ghosh +2

ScaDS.AI Dresden/Leipzig & TU Dresden, Germany · VNU University of Engineering and Technology, Hanoi, Vietnam · University of Tübingen, Germany

KG ConstructionLLM Information ExtractionFactual Knowledge in Language ModelsKnowledge Graphs
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May 21, 2025

Language Specific Knowledge: Do Models Know Better in X than in English?

Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a nuance in this training objective, and find that by changing the language of the input query, we can improve the question answering ability of language models. We make two main contributions. First, we introduce the term Language Specific Knowledge (LSK) to denote queries that are best answered in an ``expert language'' for a given LLM, thereby enhancing its question-answering ability. We introduce the problem of language selection -- for some queries, language models can perform better when queried in languages other than English, sometimes even better in low-resource languages -- and the goal is to select the optimal language for the query. Second, we introduce a variety of simple to strong baselines to empirically motivate the language selection problem (including one of our own methods called LSKExtractor). During our evaluation, we employ three datasets that contain knowledge about both cultural and social behavioral norms. Overall, the results show that principled language selection can improve the performance of a language model, and that the expected question-to-language map is not always intuitive: Gemma models know most about China and Middle East in Spanish; Qwen models know most about authority and responsibility in Arabic and Chinese. Broadly, our research contributes to the open-source development of language models that are inclusive and more aligned with the cultural and linguistic contexts in which they are deployed.

Ishika Agarwal, Nimet Beyza Bozdag, Dilek Hakkani-Tür

Department of Computer Science University of Illinois, Urbana-Champaign

Multilingual Language ModelsMultilingual QAFactual Knowledge in Language ModelsCultural Knowledge in Language Models
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May 14, 2025

Atomic Consistency Preference Optimization for Long-Form Question Answering

Large Language Models (LLMs) often produce factoid hallucinations - plausible yet incorrect answers. A common mitigation strategy is model alignment, which improves factual accuracy by training on curated (factual, non-factual) pairs. However, this approach often relies on a stronger model (e.g., GPT-4) or an external knowledge base to assess factual correctness that may not always be accessible. Addressing this, we propose Atomic Consistency Preference Optimization (ACPO), a self-supervised preference-tuning method that enhances factual accuracy without external supervision. ACPO leverages atomic consistency signals (i.e., the agreement of individual facts across multiple stochastic responses) to identify high- and low-quality data pairs for model alignment. Despite being fully self-supervised, ACPO outperforms the strong supervised alignment baseline by 1.95 points averaged across Phi-3 and Llama3 on the LongFact and BioGen datasets, demonstrating its effectiveness in improving factual reliability without relying on external models or knowledge bases.

Jingfeng Chen, Raghuveer Thirukovalluru, Junlin Wang +2

LLM Hallucination MitigationLLM AlignmentFactual Knowledge in Language ModelsPreference OptimizationQuestion AnsweringSelf-Consistency in Language Models
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Nov 11, 2024

Continual Memorization of Factoids in Language Models

As new knowledge rapidly accumulates, language models (LMs) with pretrained knowledge quickly become obsolete. A common approach to updating LMs is fine-tuning them directly on new knowledge. However, recent studies have shown that fine-tuning for memorization may be ineffective in storing knowledge or may exacerbate hallucinations. In this work, we introduce a setting we call continual memorization, where a model must memorize and retain a set of factoids through multiple stages of fine-tuning on subsequent datasets. We characterized the forgetting patterns through extensive experiments and show that LMs widely suffer from forgetting, especially when needing to memorize factoids in the second stage. We posit that forgetting can be alleviated by modifying training dynamics: (1) protecting the memorization process when learning factoids or (2) reducing interference from subsequent training stages. Intriguingly, we find that mixing randomly generated word sequences or generic data sampled from pretraining corpora at different training stages effectively mitigates forgetting REMIX: Random and Generic Data Mixing). REMIX can recover performance from severe forgetting, outperforming replay methods and other continual learning baselines. We analyze how REMIX influences the learning process and find that robust memorization follows a distinct pattern: the model stores factoids in earlier layers than usual and diversifies the layers that retain them, which results in easier recall and manipulate of the learned factoids.

Howard Chen, Jiayi Geng, Adithya Bhaskar +2

Princeton Language and Intelligence (PLI), Princeton University

Continual Learning for LLMsMemorization in Language ModelsContinual LearningNeural Network MemorizationPersistent Memory for Language ModelsMemorization in Generative Models+2
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Oct 22, 2024

All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing

Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity typing tasks where the space of labels is extremely large. In this work, we explore the limitations of the knowledge acquired by PLMs by proposing a novel heuristic to approximate the pre-training distribution of entities when the pre-training data is unknown. Then, we systematically demonstrate that entity-typing approaches that rely solely on the parametric knowledge of PLMs struggle significantly with entities at the long tail of the pre-training distribution, and that knowledge-infused approaches can account for some of these shortcomings. Our findings suggest that we need to go beyond PLMs to produce solutions that perform well for infrequent entities.

Advait Deshmukh, Ashwin Umadi, Dananjay Srinivas +1

University of Colorado Boulder

Language Model PretrainingLong-Tail LearningKnowledge Augmentation for Language ModelsFactual Knowledge in Language Models
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