Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?

• URL: http://arxiv.org/abs/2405.16908v2
• Date: Thu, 26 Sep 2024 08:53:01 GMT
• Title: Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?
• Authors: Gal Yona, Roee Aharoni, Mor Geva,
• Abstract summary: We show that large language models (LLMs) should be capable of expressing their intrinsic uncertainty in natural language. We formalize faithful response uncertainty based on the gap between the model's intrinsic confidence in the assertions it makes and the decisiveness by which they are conveyed.
• Score: 21.814007454504978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We posit that large language models (LLMs) should be capable of expressing their intrinsic uncertainty in natural language. For example, if the LLM is equally likely to output two contradicting answers to the same question, then its generated response should reflect this uncertainty by hedging its answer (e.g., "I'm not sure, but I think..."). We formalize faithful response uncertainty based on the gap between the model's intrinsic confidence in the assertions it makes and the decisiveness by which they are conveyed. This example-level metric reliably indicates whether the model reflects its uncertainty, as it penalizes both excessive and insufficient hedging. We evaluate a variety of aligned LLMs at faithfully communicating uncertainty on several knowledge-intensive question answering tasks. Our results provide strong evidence that modern LLMs are poor at faithfully conveying their uncertainty, and that better alignment is necessary to improve their trustworthiness.

Related papers

• Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence? [26.69630281310365]
  Large language models (LLMs) have been found to produce hallucinations when the question exceeds their internal knowledge boundaries. Existing research on LLMs' perception of their knowledge boundaries typically uses either the probability of the generated tokens or the verbalized confidence as the model's confidence in its response.
  arXiv  Detail & Related papers  (2024-08-19T08:01:11Z)
• Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models [55.332004960574004]
  Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established. This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt. We propose a prompt-response concept model that explains how LLMs generate responses and helps understand the relationship between prompts and response uncertainty.
  arXiv  Detail & Related papers  (2024-07-20T11:19:58Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities [79.9629927171974]
  Uncertainty in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. We propose Kernel Language Entropy (KLE), a novel method for uncertainty estimation in white- and black-box LLMs.
  arXiv  Detail & Related papers  (2024-05-30T12:42:05Z)
• "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust [51.542856739181474]
  We show how different natural language expressions of uncertainty impact participants' reliance, trust, and overall task performance. We find that first-person expressions decrease participants' confidence in the system and tendency to agree with the system's answers, while increasing participants' accuracy. Our findings suggest that using natural language expressions of uncertainty may be an effective approach for reducing overreliance on LLMs, but that the precise language used matters.
  arXiv  Detail & Related papers  (2024-05-01T16:43:55Z)
• Uncertainty in Language Models: Assessment through Rank-Calibration [65.10149293133846]
  Language Models (LMs) have shown promising performance in natural language generation. It is crucial to correctly quantify their uncertainty in responding to given inputs. We develop a novel and practical framework, termed $Rank$-$Calibration$, to assess uncertainty and confidence measures for LMs.
  arXiv  Detail & Related papers  (2024-04-04T02:31:05Z)
• Are self-explanations from Large Language Models faithful? [35.40666730867487]
  Large Language Models (LLMs) excel at many tasks and will even explain their reasoning, so-called self-explanations. It's important to measure if self-explanations truly reflect the model's behavior. We propose employing self-consistency checks to measure faithfulness.
  arXiv  Detail & Related papers  (2024-01-15T19:39:15Z)
• Quantifying Uncertainty in Natural Language Explanations of Large Language Models [29.34960984639281]
  Large Language Models (LLMs) are increasingly used as powerful tools for high-stakes natural language processing (NLP) applications. We propose two novel metrics -- $textitVerbalized Uncertainty$ and $textitProbing Uncertainty$ -- to quantify the uncertainty of generated explanations. Our empirical analysis of benchmark datasets reveals that verbalized uncertainty is not a reliable estimate of explanation confidence.
  arXiv  Detail & Related papers  (2023-11-06T21:14:40Z)
• Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning [76.98542249776257]
  Large-scale language models often face the challenge of "hallucination" We introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
  arXiv  Detail & Related papers  (2023-10-07T12:06:53Z)
• Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models [37.63939774027709]
  Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities. We propose and compare several confidence/uncertainty measures, applying them to *selective NLG* where unreliable results could either be ignored or yielded for further assessment. Results reveal that a simple measure for the semantic dispersion can be a reliable predictor of the quality of LLM responses.
  arXiv  Detail & Related papers  (2023-05-30T16:31:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.