Unc-TTP: A Method for Classifying LLM Uncertainty to Improve In-Context Example Selection

• URL: http://arxiv.org/abs/2408.09172v3
• Date: Sat, 24 Aug 2024 20:26:43 GMT
• Title: Unc-TTP: A Method for Classifying LLM Uncertainty to Improve In-Context Example Selection
• Authors: Hsiu-Yuan Huang, Zichen Wu, Yutong Yang, Junzhao Zhang, Yunfang Wu,
• Abstract summary: Large Language Models (LLMs) have demonstrated exceptional performance across various downstream tasks. It is challenging for users to discern whether the responses are generated with certainty or are fabricated to meet user expectations. We propose a novel Uncertainty Tripartite Testing Paradigm (Unc-TTP) to classify LLM uncertainty.
• Score: 6.813733517894384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowadays, Large Language Models (LLMs) have demonstrated exceptional performance across various downstream tasks. However, it is challenging for users to discern whether the responses are generated with certainty or are fabricated to meet user expectations. Estimating the uncertainty of LLMs is particularly challenging due to their vast scale and the lack of white-box access. In this work, we propose a novel Uncertainty Tripartite Testing Paradigm (Unc-TTP) to classify LLM uncertainty, via evaluating the consistency of LLM outputs when incorporating label interference into the sampling-based approach. Based on Unc-TTP outputs, we aggregate instances into certain and uncertain categories. Further, we conduct a detailed analysis of the uncertainty properties of LLMs and show Unc-TTP's superiority over the existing sampling-based methods. In addition, we leverage the obtained uncertainty information to guide in-context example selection, demonstrating that Unc-TTP obviously outperforms retrieval-based and sampling-based approaches in selecting more informative examples. Our work paves a new way to classify the uncertainty of both open- and closed-source LLMs, and introduces a practical approach to exploit this uncertainty to improve LLMs performance.

Related papers

• Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering [1.9214041945441436]
  We present a new approach for evaluating semanticencies of Large Language Model (LLM) Our approach evaluates whether LLM re-sponses are semantically congruent for a given question, recognizing that as syntactically different sentences may convey the same meaning. Using the TruthfulQA dataset to assess LLM responses, the study induces N re-sponses per question and clusters semantically equivalent sentences to measure semantic consistency across 37 categories.
  arXiv  Detail & Related papers  (2024-10-20T16:21:25Z)
• The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism [39.392450788666814]
  Current evaluations of large language models (LLMs) often overlook non-determinism. greedy decoding generally outperforms sampling methods for most evaluated tasks. Smaller LLMs can match or surpass larger models such as GPT-4-Turbo.
  arXiv  Detail & Related papers  (2024-07-15T06:12:17Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• 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)
• 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)
• 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)
• 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)
• 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)
• Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge [35.067234242461545]
  Large language models (LLMs) express uncertainty in situations where they lack sufficient parametric knowledge to generate reasonable responses. This work aims to systematically investigate LLMs' behaviors in such situations, emphasizing the trade-off between honesty and helpfulness.
  arXiv  Detail & Related papers  (2023-11-16T10:02:40Z)
• 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)

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.