mlsanityData to 14 Oct 2026

Fine-Grained Self-Endorsement Improves Factuality and Reasoning

arXiv 2402.15631 ↗First posted 23 Feb 2024cs.CL cs.AIcited by 7

On the map: LLM reasoning → Hallucination detection and mitigation

Abstract (arXiv)

This work studies improving large language model (LLM) generations at inference time by mitigating fact-conflicting hallucinations. Particularly, we propose a self-endorsement framework that leverages the fine-grained fact-level comparisons across multiple sampled responses. Compared with prior ensemble methods (Wang et al., 2022;Chen et al., 2023)) that perform response-level selection, our approach can better alleviate hallucinations, especially for longform generation tasks. Our approach can broadly benefit smaller and open-source LLMs as it mainly conducts simple content-based comparisons. Experiments on Biographies show that our method can effectively improve the factuality of generations with simple and intuitive prompts across different scales of LLMs. Besides, comprehensive analyses on TriviaQA and GSM8K demonstrate the potential of self-endorsement for broader application.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning. Builds on 25 of its 34 references that are in the map. First posted on arXiv 23 Feb 2024.

Builds on

25 of 34 references.

References · 25

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  4. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  7. 2023Let's Verify Step by Stepcited by 3,789
  8. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  9. 2023Mistral 7Bcited by 3,164
  10. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  11. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
  12. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
13 more
  1. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  2. 2023Large Language Models as Optimizerscited by 864
  3. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  4. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  5. 2022Large Language Models Can Self-Improvecited by 664
  6. 2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Modelscited by 376
  7. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 353
  8. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 324
  9. 2023Evaluating Verifiability in Generative Search Enginescited by 303
  10. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  11. 2023Universal Self-Consistency for Large Language Model Generationcited by 203
  12. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 124
  13. 2023DocTER: Evaluating Document-based Knowledge Editingcited by 26

Built on by

Cited by year

Citing papers per year: 2024: 5; 2025: 2.

7 papers.

  1. 2024Multi-group Uncertainty Quantification for Long-form Text Generationcited by 13
  2. 2024Graph-based Uncertainty Metrics for Long-form Language Model Outputscited by 11
  3. 2024Atomic Self-Consistency for Better Long Form Generationscited by 10
  4. 2024Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generationcited by 6
  5. 2024RAC: Efficient LLM Factuality Correction with Retrieval Augmentationcited by 5
  6. 2025Place Matters: Comparing LLM Hallucination Rates for Place-Based Legal Queriescited by 3
  7. 2025The Illusionist's Prompt: Exposing the Factual Vulnerabilities of Large Language Models with Linguistic Nuancescited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation
  2. 2025Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models
  3. 2025Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification
  4. 2024Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement
  5. 2023RELIC: Investigating Large Language Model Responses using Self-Consistency
  6. 2025Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
  7. 2024Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output
  8. 2024Internal Consistency and Self-Feedback in Large Language Models: A Survey