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Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models

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arxiv 2307.10236 v4 pith:L6GAUUIJ submitted 2023-07-16 cs.SE cs.AIcs.CL

Yuheng Huang , Jiayang Song , Zhijie Wang , Shengming Zhao , Huaming Chen , Felix Juefei-Xu , Lei Ma This is my paper · ORCID
classification cs.SEcs.AIcs.CL
keywords llmsuncertaintyestimationfourlanguagemodelsexploratoryextent
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The recent performance leap of Large Language Models (LLMs) opens up new opportunities across numerous industrial applications and domains. However, erroneous generations, such as false predictions, misinformation, and hallucination made by LLMs, have also raised severe concerns for the trustworthiness of LLMs', especially in safety-, security- and reliability-sensitive scenarios, potentially hindering real-world adoptions. While uncertainty estimation has shown its potential for interpreting the prediction risks made by general machine learning (ML) models, little is known about whether and to what extent it can help explore an LLM's capabilities and counteract its undesired behavior. To bridge the gap, in this paper, we initiate an exploratory study on the risk assessment of LLMs from the lens of uncertainty. In particular, we experiment with twelve uncertainty estimation methods and four LLMs on four prominent natural language processing (NLP) tasks to investigate to what extent uncertainty estimation techniques could help characterize the prediction risks of LLMs. Our findings validate the effectiveness of uncertainty estimation for revealing LLMs' uncertain/non-factual predictions. In addition to general NLP tasks, we extensively conduct experiments with four LLMs for code generation on two datasets. We find that uncertainty estimation can potentially uncover buggy programs generated by LLMs. Insights from our study shed light on future design and development for reliable LLMs, facilitating further research toward enhancing the trustworthiness of LLMs.

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Cited by 16 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. Neural Message-Passing on Attention Graphs for Hallucination Detection

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    CHARM trains graph neural networks on token-attention graphs built from LLM computational traces and outperforms prior hallucination detectors on five benchmarks at token and response level.

  3. Can Multiple Responses from an LLM Reveal the Sources of Its Uncertainty?

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    An auxiliary LLM can diagnose whether an LLM's uncertainty comes from ambiguous questions or missing knowledge by analyzing patterns of disagreement among multiple sampled answers.

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    SSP adds a learned, sample-specific noise prompt to an LLM input and scores hallucination by the cosine shift in intermediate representations, outperforming output-confidence baselines on QA benchmarks.

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    LLMs show limited, task-dependent accuracy at choosing correct epistemic modals and attitude verbs in controlled stories, with better performance on necessity and fact statements than on possibility and belief statements.

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  8. The "I Don't Know" Filter: Enhancing Agentic Reliability in Function Calling

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    A multi-sample uncertainty classifier filter improves agent reliability (IDKS) by abstaining on uncertain function calls across open SLMs and BFCL-style benchmarks.

  9. SAFECAST: Robust Failure Detection for VLA Policies with Contrast-Set Training and Calibration

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    SAFECAST augments hidden-state failure-probe training and conformal calibration with visual and language contrast sets, improving VLA failure detection under distribution shift in several tested settings.

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    Consistency-based methods are the most reliable confidence signal for text-to-SQL in black-box LLMs, and executing queries against a database adds a useful correctness signal.

  13. Evaluating Uncertainty and Quality of Visual Language Action-enabled Robots

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  15. ChemAU: Harness the Reasoning of LLMs in Chemical Research with Adaptive Uncertainty Estimation

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  16. HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs

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