Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

• URL: http://arxiv.org/abs/2308.16175v2
• Date: Wed, 4 Oct 2023 15:05:24 GMT
• Title: Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness
• Authors: Jiuhai Chen, Jonas Mueller
• Abstract summary: We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model. Our uncertainty quantification technique works for any LLM accessible only via a black-box API.
• Score: 16.35655151252159
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model by estimating a numeric confidence score for any output it generated. Our uncertainty quantification technique works for any LLM accessible only via a black-box API, whose training data remains unknown. By expending a bit of extra computation, users of any LLM API can now get the same response as they would ordinarily, as well as a confidence estimate that cautions when not to trust this response. Experiments on both closed and open-form Question-Answer benchmarks reveal that BSDetector more accurately identifies incorrect LLM responses than alternative uncertainty estimation procedures (for both GPT-3 and ChatGPT). By sampling multiple responses from the LLM and considering the one with the highest confidence score, we can additionally obtain more accurate responses from the same LLM, without any extra training steps. In applications involving automated evaluation with LLMs, accounting for our confidence scores leads to more reliable evaluation in both human-in-the-loop and fully-automated settings (across both GPT 3.5 and 4).

Related papers

• Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models [79.76293901420146]
  Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. Our research investigates the fragility of uncertainty estimation and explores potential attacks. We demonstrate that an attacker can embed a backdoor in LLMs, which, when activated by a specific trigger in the input, manipulates the model's uncertainty without affecting the final output.
  arXiv  Detail & Related papers  (2024-07-15T23:41:11Z)
• 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)
• SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales [29.33581578047835]
  SaySelf is a training framework that teaches large language models to express more accurate fine-grained confidence estimates. In addition, SaySelf directs LLMs to produce self-reflective rationales that clearly identify gaps in their parametric knowledge. We show that the generated self-reflective rationales are reasonable and can further contribute to the calibration.
  arXiv  Detail & Related papers  (2024-05-31T16:21:16Z)
• Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection [90.71323430635593]
  We propose a novel self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers. It thoroughly compares the trustability of multiple candidate answers to mitigate the over-trust in LLM-generated incorrect answers. This framework can be seamlessly integrated with existing approaches for superior self-detection.
  arXiv  Detail & Related papers  (2024-03-15T02:38:26Z)
• Calibrating Large Language Models Using Their Generations Only [44.26441565763495]
  APRICOT is a method to set confidence targets and train an additional model that predicts an LLM's confidence based on its textual input and output alone. It is conceptually simple, does not require access to the target model beyond its output, does not interfere with the language generation, and has a multitude of potential usages. We show how our approach performs competitively in terms of calibration error for white-box and black-box LLMs on closed-book question-answering to detect incorrect LLM answers.
  arXiv  Detail & Related papers  (2024-03-09T17:46:24Z)
• Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models [89.20169610517381]
  We propose Fact-and-Reflection (FaR) prompting, which improves the LLM calibration in two steps. Experiments show that FaR achieves significantly better calibration; it lowers the Expected Error by 23.5%. FaR even elicits the capability of verbally expressing concerns in less confident scenarios.
  arXiv  Detail & Related papers  (2024-02-27T01:37:23Z)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)
• Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs [60.61002524947733]
  Previous confidence elicitation methods rely on white-box access to internal model information or model fine-tuning. This leads to a growing need to explore the untapped area of black-box approaches for uncertainty estimation. We define a systematic framework with three components: prompting strategies for eliciting verbalized confidence, sampling methods for generating multiple responses, and aggregation techniques for computing consistency.
  arXiv  Detail & Related papers  (2023-06-22T17:31:44Z)
• 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.