mlsanityData to 7 Oct 2026

Self-Evaluation Improves Selective Generation in Large Language Models

arXiv 2312.09300 ↗First posted 14 Dec 2023cs.CL cs.AI cs.LGcited by 74

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Cited by 21 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Safe deployment of large language models (LLMs) may benefit from a reliable method for assessing their generated content to determine when to abstain or to selectively generate. While likelihood-based metrics such as perplexity are widely employed, recent research has demonstrated the limitations of using sequence-level probability estimates given by LLMs as reliable indicators of generation quality. Conversely, LLMs have demonstrated strong calibration at the token level, particularly when it comes to choosing correct answers in multiple-choice questions or evaluating true/false statements. In this work, we reformulate open-ended generation tasks into token-level prediction tasks, and leverage LLMs' superior calibration at the token level. We instruct an LLM to self-evaluate its answers, employing either a multi-way comparison or a point-wise evaluation approach, with the option to include a ``None of the above'' option to express the model's uncertainty explicitly. We benchmark a range of scoring methods based on self-evaluation and evaluate their performance in selective generation using TruthfulQA and TL;DR. Through experiments with PaLM-2 and GPT-3, we demonstrate that self-evaluation based scores not only improve accuracy, but also correlate better with the overall quality of generated content.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). Builds on 30 of its 34 references that are in the map. First posted on arXiv 14 Dec 2023.

Builds on

30 of 34 references.

References · 30

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  6. 2017On Calibration of Modern Neural Networkscited by 4,860
  7. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  8. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  9. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  10. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  11. 2020Learning to summarize from human feedbackcited by 2,562
  12. 2022Language Models (Mostly) Know What They Knowcited by 1,783
18 more
  1. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  2. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  3. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  4. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  5. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  6. 2020Towards a Human-like Open-Domain Chatbotcited by 467
  7. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  8. 2023SLiC-HF: Sequence Likelihood Calibration with Human Feedbackcited by 395
  9. 2023Robots That Ask For Help: Uncertainty Alignment for Large Language Model Plannerscited by 298
  10. 2022Leveraging Large Language Models for Multiple Choice Question Answeringcited by 199
  11. 2023Universal Self-Consistency for Large Language Model Generationcited by 198
  12. 2022Out-of-Distribution Detection and Selective Generation for Conditional Language Modelscited by 193
  13. 2022BRIO: Bringing Order to Abstractive Summarizationcited by 161
  14. 2023Selectively Answering Ambiguous Questionscited by 63
  15. 2023On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Studycited by 25
  16. 2020Collaborative Storytelling with Large-scale Neural Language Modelscited by 21
  17. 2021Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimationcited by 18
  18. 2023Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?cited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 21; 2025: 24; 2026: 29.

74 papers.

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  1. 2024Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learningcited by 238
  2. 2025Deep Think with Confidencecited by 199
  3. 2025Scalable Best-of-N Selection for Large Language Models via Self-Certaintycited by 182
  4. 2024Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similaritiescited by 156
  5. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Modelscited by 129
  6. 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 116
  7. 2024HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detectioncited by 96
  8. 2024Interpretable Contrastive Monte Carlo Tree Search Reasoningcited by 62
  9. 2025SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scalingcited by 54
  10. 2024Improving Uncertainty Estimation through Semantically Diverse Language Generationcited by 34
  11. 2025Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physicscited by 33
  12. 2024DataGen: Unified Synthetic Dataset Generation via Large Language Modelscited by 22
38 more
  1. 2025Does It Make Sense to Speak of Introspection in Large Language Models?cited by 22
  2. 2024LOVA3: Learning to Visual Question Answering, Asking and Assessmentcited by 21
  3. 2024A Survey on the Honesty of Large Language Modelscited by 21
  4. 2025Multi-Agent Debate for LLM Judges with Adaptive Stability Detectioncited by 20
  5. 2024Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measurecited by 18
  6. 2025Maestro: Self-Improving Text-to-Image Generation via Agent Orchestrationcited by 18
  7. 2025Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in Its Latent Thoughtscited by 18
  8. 2025Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorerscited by 16
  9. 2024Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Plannerscited by 15
  10. 2024On Subjective Uncertainty Quantification and Calibration in Natural Language Generationcited by 14
  11. 2025Agentic Keyframe Search for Video Question Answeringcited by 13
  12. 2024Matchmaker: Self-Improving Large Language Model Programs for Schema Matchingcited by 9
  13. 2025BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systemscited by 8
  14. 2026HALT: Hallucination Assessment via Log-probs as Time seriescited by 8
  15. 2025Evaluation of LLMs for mathematical problem solvingcited by 7
  16. 2024LLMs can learn self-restraint through iterative self-reflectioncited by 6
  17. 2025GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasetscited by 6
  18. 2024Autonomous Prompt Engineering in Large Language Modelscited by 5
  19. 2026The Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Modelscited by 5
  20. 2024Steering Large Language Models to Evaluate and Amplify Creativitycited by 4
  21. 2025FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systemscited by 4
  22. 2024Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controllercited by 3
  23. 2024Assessing Generative Language Models in Classification Tasks: Performance and Self-Evaluation Capabilities in the Environmental and Climate Change Domaincited by 3
  24. 2025Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Reviewcited by 3
  25. 2025Can LLMs Make (Personalized) Access Control Decisions?cited by 3
  26. 2025Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinationscited by 2
  27. 2025Confident RAG: Enhancing the Performance of LLMs for Mathematics Question Answering through Multi-Embedding and Confidence Scoringcited by 2
  28. 2025Efficient semantic uncertainty quantification in language models via diversity-steered samplingcited by 2
  29. 2026Best-of-Tails: Bridging Optimism and Pessimism in Inference-Time Alignmentcited by 2
  30. 2026Inference Time Optimization with Confidence Dynamicscited by 2
  31. 2026Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systemscited by 2
  32. 2026AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflowscited by 2
  33. 2025HalluField: Detecting LLM Hallucinations via Field-Theoretic Modelingcited by 1
  34. 2026Learning to Trust the Crowd: A Multi-Model Consensus Reasoning Engine for Large Language Modelscited by 1
  35. 2026AdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understandingcited by 1
  36. 2026From Data to Theory: Autonomous Large Language Model Agents for Materials Sciencecited by 1
  37. 2026When Should a Language Model Trust Itself? Same-Model Self-Verification as a Conditional Confidence Signalcited by 1
  38. 2026Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQAcited by 1

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