mlsanityData to 7 Oct 2026

Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models

arXiv 2402.11279 ↗First posted 17 Feb 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

In the deployment of large language models (LLMs), accurate confidence estimation is critical for assessing the credibility of model predictions. However, existing methods often fail to overcome the issue of overconfidence on incorrect answers. In this work, we focus on improving the confidence estimation of large language models. Considering the fragility of self-awareness in language models, we introduce a Multi-Perspective Consistency (MPC) method. We leverage complementary insights from different perspectives within models (MPC-Internal) and across different models (MPC-Across) to mitigate the issue of overconfidence arising from a singular viewpoint. The experimental results on eight publicly available datasets show that our MPC achieves state-of-the-art performance. Further analyses indicate that MPC can mitigate the problem of overconfidence and is effectively scalable to other models.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 27 references that are in the map. First posted on arXiv 17 Feb 2024.

Builds on

23 of 27 references.

References · 23

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
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11 more
  1. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  2. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  3. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
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  10. 2021Calibrated and Sharp Uncertainties in Deep Learning via Density Estimationcited by 33
  11. 2023Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPTcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrongcited by 11
  2. 2026Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Modelscited by 3
  3. 2025Benchmarking Large Language Models for Diagnosing Students' Cognitive Skills from Handwritten Math Workcited by 1
  4. 2026The Score Granularity Gap in Black-Box LLM Classification: A Comparative Study of Confidence Constructionscited by 1