mlsanityData to 14 Oct 2026

A Survey on the Honesty of Large Language Models

arXiv 2409.18786 ↗First posted 27 Sep 2024cs.CL cs.AIcited by 23

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

Abstract (arXiv)

Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to faithfully express their knowledge. Despite promising, current LLMs still exhibit significant dishonest behaviors, such as confidently presenting wrong answers or failing to express what they know. In addition, research on the honesty of LLMs also faces challenges, including varying definitions of honesty, difficulties in distinguishing between known and unknown knowledge, and a lack of comprehensive understanding of related research. To address these issues, we provide a survey on the honesty of LLMs, covering its clarification, evaluation approaches, and strategies for improvement. Moreover, we offer insights for future research, aiming to inspire further exploration in this important area.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 162 of its 188 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

162 of 188 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision14 Dec
  2. 2022Discovering Language Model Behaviors with Model-Written Evaluations19 Dec

Other references · 160

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  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
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  10. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
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  5. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  6. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
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50 most cited of 160 All 160 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 14; 2026: 8.

23 papers.

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  1. 2024FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"cited by 86
  2. 2025The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systemscited by 72
  3. 2025The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMscited by 36
  4. 2025Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trendscited by 20
  5. 2025Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challengescited by 15
  6. 2025Evaluation Faking: Unveiling Observer Effects in Safety Evaluation of Frontier AI Systemscited by 14
  7. 2025The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucinationcited by 14
  8. 2025GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigationcited by 11
  9. 2025Shadow-FT: Tuning Instruct Model via Training on Paired Base Modelcited by 10
  10. 2025ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Modelscited by 8
  11. 2025ImF: Implicit Fingerprint for Large Language Modelscited by 7
  12. 2026Reasoning Models Will Sometimes Lie About Their Reasoningcited by 6
11 more
  1. 2026Emergent Risks in Generative Multi-Agent Systemscited by 5
  2. 2025On the Robustness of Verbal Confidence of LLMs in Adversarial Attackscited by 3
  3. 2026Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Modelscited by 3
  4. 2026Architecting Trust in Artificial Epistemic Agentscited by 3
  5. 2025When LLMs Team Up: The Emergence of Collaborative Affective Computingcited by 2
  6. 2026From Scalars to Tensors: Declared Losses Recover Epistemic Distinctions That Neutrosophic Scalars Cannot Expresscited by 1
  7. 2025AI Identity Disclosure Under Professional Personas: A Gap Between Capacity and Consistencycited by 0
  8. 2025Assessing the Software Security Comprehension of Large Language Modelscited by 0
  9. 2026MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Modelscited by 0
  10. 2026Beyond the Shadows of Plato's Cave: Evaluating False Memory in Autonomous Agents via Counterfactual Reasoningcited by 0
  11. 2026Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflictcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024HonestLLM: Toward an Honest and Helpful Large Language Model
  2. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
  3. 2026Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning
  4. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
  5. 2025MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions
  6. 2025Aligning Large Language Models for Faithful Integrity Against Opposing Argument
  7. 2024Fine-Tuning Language Models for Ethical Ambiguity: A Comparative Study of Alignment with Human Responses
  8. 2024To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts