Estimating LLM Uncertainty with Logits

• URL: http://arxiv.org/abs/2502.00290v2
• Date: Tue, 11 Feb 2025 05:26:22 GMT
• Title: Estimating LLM Uncertainty with Logits
• Authors: Huan Ma, Jingdong Chen, Guangyu Wang, Changqing Zhang,
• Abstract summary: We introduce Logits- Token Uncertainty (LogU), a novel framework designed to estimate token-specific uncertainty in Large Language Models in real time. Our experimental findings highlight the substantial effectiveness and potential of LogU, marking a significant advancement in addressing the challenge of model hallucinations.
• Score: 39.145322355643906
• License:
• Abstract: In recent years, Large Language Models (LLMs) have seen remarkable advancements and have been extensively integrated across various fields. Despite their progress, LLMs are prone to hallucinations, producing responses that may not be dependable if the models lack sufficient grounding knowledge. To mitigate this issue, methods for estimating uncertainty have been adopted, with a focus on critical tokens as indicators of reliability. Nevertheless, probability-based approaches have shown limitations in assessing token-level reliability due to the erosion of evidence strength information acquired during training. In this paper, we introduce Logits-induced Token Uncertainty (LogU), a novel framework designed to estimate token-specific uncertainty in LLMs in real time, without the need for multiple sampling rounds. By leveraging evidence modeling for the implementation of LogU, we utilize the derived uncertainty measures to steer downstream tasks. Our experimental findings highlight the substantial effectiveness and potential of LogU, marking a significant advancement in addressing the challenge of model hallucinations.

Related papers

• 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)
• Do LLMs estimate uncertainty well in instruction-following? [9.081508933326644]
  Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. We present the first systematic evaluation of the uncertainty estimation abilities of LLMs in the context of instruction-following. Our findings show that existing uncertainty methods struggle, particularly when models make subtle errors in instruction following.
  arXiv  Detail & Related papers  (2024-10-18T16:32:10Z)
• Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models [96.43562963756975]
  We train a regression model, which target variable is the gap between the conditional and the unconditional generation confidence. We use this learned conditional dependency model to modulate the uncertainty of the current generation step based on the uncertainty of the previous step.
  arXiv  Detail & Related papers  (2024-08-20T09:42:26Z)
• 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)
• Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach [6.209293868095268]
  We study the problem of uncertainty estimation and calibration for LLMs. We propose a supervised approach that leverages labeled datasets to estimate the uncertainty in LLMs' responses. Our method is easy to implement and adaptable to different levels of model accessibility including black box, grey box, and white box.
  arXiv  Detail & Related papers  (2024-04-24T17:10:35Z)
• Uncertainty Quantification for In-Context Learning of Large Language Models [52.891205009620364]
  In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion.
  arXiv  Detail & Related papers  (2024-02-15T18:46:24Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)
• Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models [15.735715641327836]
  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.
  arXiv  Detail & Related papers  (2023-07-16T08:28:04Z)
• Uncertainty Estimation by Fisher Information-based Evidential Deep Learning [61.94125052118442]
  Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL) In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
  arXiv  Detail & Related papers  (2023-03-03T16:12:59Z)

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.