LLM Confidence Evaluation Measures in Zero-Shot CSS Classification

• URL: http://arxiv.org/abs/2410.13047v2
• Date: Fri, 01 Nov 2024 23:39:14 GMT
• Title: LLM Confidence Evaluation Measures in Zero-Shot CSS Classification
• Authors: David Farr, Iain Cruickshank, Nico Manzonelli, Nicholas Clark, Kate Starbird, Jevin West,
• Abstract summary: We propose an uncertainty quantification (UQ) performance measure tailored for data annotation tasks. We introduce a novel UQ aggregation strategy that effectively identifies low-confidence LLM annotations and disproportionately uncovers data incorrectly labeled by the LLMs. Our results demonstrate that our proposed UQ aggregation strategy improves upon existing methods andcan be used to significantly improve human-in-the-loop data annotation processes.
• Score: 1.6410524749379551
• License:
• Abstract: Assessing classification confidence is critical for leveraging large language models (LLMs) in automated labeling tasks, especially in the sensitive domains presented by Computational Social Science (CSS) tasks. In this paper, we make three key contributions: (1) we propose an uncertainty quantification (UQ) performance measure tailored for data annotation tasks, (2) we compare, for the first time, five different UQ strategies across three distinct LLMs and CSS data annotation tasks, (3) we introduce a novel UQ aggregation strategy that effectively identifies low-confidence LLM annotations and disproportionately uncovers data incorrectly labeled by the LLMs. Our results demonstrate that our proposed UQ aggregation strategy improves upon existing methods andcan be used to significantly improve human-in-the-loop data annotation processes.

Related papers

• Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models [76.17975723711886]
  Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs) In this work, we adapt Mahalanobis Distance (MD) - a well-established UQ technique in classification tasks - for text generation. Our method extracts token embeddings from multiple layers of LLMs, computes MD scores for each token, and uses linear regression trained on these features to provide robust uncertainty scores.
  arXiv  Detail & Related papers  (2025-02-20T10:25:13Z)
• Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [83.90988015005934]
  Uncertainty quantification is a key element of machine learning applications. We introduce a novel benchmark that implements a collection of state-of-the-art UQ baselines. We conduct a large-scale empirical investigation of UQ and normalization techniques across eleven tasks, identifying the most effective approaches.
  arXiv  Detail & Related papers  (2024-06-21T20:06:31Z)
• Improve Temporal Awareness of LLMs for Sequential Recommendation [61.723928508200196]
  Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data. We propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation.
  arXiv  Detail & Related papers  (2024-05-05T00:21:26Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data [3.9459077974367833]
  Large language models (LLMs) have demonstrated remarkable success in NLP tasks. We benchmarked one supervised classic machine learning model based on Support Vector Machines (SVMs), three supervised pretrained language models (PLMs) based on RoBERTa, BERTweet, and SocBERT, and two LLM based classifiers (GPT3.5 and GPT4), across 6 text classification tasks. Our comprehensive experiments demonstrate that employ-ing data augmentation using LLMs (GPT-4) with relatively small human-annotated data to train lightweight supervised classification models achieves superior results compared to training with human-annotated data
  arXiv  Detail & Related papers  (2024-03-27T22:05:10Z)
• Which is better? Exploring Prompting Strategy For LLM-based Metrics [6.681126871165601]
  This paper describes the DSBA submissions to the Prompting Large Language Models as Explainable Metrics shared task. Traditional similarity-based metrics such as BLEU and ROUGE have shown to misalign with human evaluation and are ill-suited for open-ended generation tasks.
  arXiv  Detail & Related papers  (2023-11-07T06:36:39Z)
• Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning [37.22349652230841]
  Large language models (LLMs) enable in-context learning (ICL) by conditioning on a few labeled training examples as a text-based prompt. In this paper, we demonstrate that factual knowledge is imperative for the performance of ICL in three core facets. We introduce a novel Knowledgeable In-Context Tuning (KICT) framework to further improve the performance of ICL.
  arXiv  Detail & Related papers  (2023-09-26T09:06:39Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)

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.