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

• URL: http://arxiv.org/abs/2307.10236v4
• Date: Sun, 05 Jan 2025 06:15:04 GMT
• Title: Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
• Authors: Yuheng Huang, Jiayang Song, Zhijie Wang, Shengming Zhao, Huaming Chen, Felix Juefei-Xu, Lei Ma,
• Abstract summary: We study the risk assessment of Large Language Models (LLMs) from the lens of uncertainty. Our findings validate the effectiveness of uncertainty estimation for revealing LLMs' uncertain/non-factual predictions. Insights from our study shed light on future design and development for reliable LLMs.
• Score: 15.735715641327836
• License:
• Abstract: 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.

Related papers

• Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception [58.62352010928591]
  Large language models (LLMs) exhibit impressive performance across diverse tasks but often struggle to accurately gauge their knowledge boundaries. This paper explores leveraging LLMs' internal states to enhance their perception of knowledge boundaries from efficiency and risk perspectives.
  arXiv  Detail & Related papers  (2025-02-17T11:11:09Z)
• An Empirical Analysis of Uncertainty in Large Language Model Evaluations [28.297464655099034]
  We conduct experiments involving 9 widely used LLM evaluators across 2 different evaluation settings. We pinpoint that LLM evaluators exhibit varying uncertainty based on model families and sizes. We find that employing special prompting strategies, whether during inference or post-training, can alleviate evaluation uncertainty to some extent.
  arXiv  Detail & Related papers  (2025-02-15T07:45:20Z)
• UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models [41.67393607081513]
  Large Language Models (LLMs) often struggle to accurately express the factual knowledge they possess. We propose the UAlign framework, which leverages Uncertainty estimations to represent knowledge boundaries. We show that the proposed UAlign can significantly enhance the LLMs' capacities to confidently answer known questions.
  arXiv  Detail & Related papers  (2024-12-16T14:14:27Z)
• A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions [9.045698110081686]
  Large language models (LLMs) generate plausible, factually-incorrect responses, which are expressed with striking confidence. Previous work has shown that hallucinations and other non-factual responses generated by LLMs can be detected by examining the uncertainty of the LLM in its response to the pertinent prompt. This survey seeks to provide an extensive review of existing uncertainty quantification methods for LLMs, identifying their salient features, along with their strengths and weaknesses.
  arXiv  Detail & Related papers  (2024-12-07T06:56:01Z)
• CLUE: Concept-Level Uncertainty Estimation for Large Language Models [49.92690111618016]
  We propose a novel framework for Concept-Level Uncertainty Estimation for Large Language Models (LLMs) We leverage LLMs to convert output sequences into concept-level representations, breaking down sequences into individual concepts and measuring the uncertainty of each concept separately. We conduct experiments to demonstrate that CLUE can provide more interpretable uncertainty estimation results compared with sentence-level uncertainty.
  arXiv  Detail & Related papers  (2024-09-04T18:27:12Z)
• Exploring Automatic Cryptographic API Misuse Detection in the Era of LLMs [60.32717556756674]
  This paper introduces a systematic evaluation framework to assess Large Language Models in detecting cryptographic misuses. Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives. The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T15:31:26Z)
• Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners [10.746821861109176]
  Large Language Models (LLMs) have witnessed remarkable performance as zero-shot task planners for robotic tasks. However, the open-loop nature of previous works makes LLM-based planning error-prone and fragile. In this work, we introduce a framework for closed-loop LLM-based planning called KnowLoop, backed by an uncertainty-based MLLMs failure detector.
  arXiv  Detail & Related papers  (2024-06-01T12:52:06Z)
• Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
  Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. Our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
  arXiv  Detail & Related papers  (2024-04-16T13:22:54Z)
• Benchmarking LLMs via Uncertainty Quantification [91.72588235407379]
  The proliferation of open-source Large Language Models (LLMs) has highlighted the urgent need for comprehensive evaluation methods. We introduce a new benchmarking approach for LLMs that integrates uncertainty quantification. Our findings reveal that: I) LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III) Instruction-finetuning tends to increase the uncertainty of LLMs.
  arXiv  Detail & Related papers  (2024-01-23T14:29:17Z)
• Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity [61.54815512469125]
  This survey addresses the crucial issue of factuality in Large Language Models (LLMs) As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
  arXiv  Detail & Related papers  (2023-10-11T14:18:03Z)

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.