LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs

• URL: http://arxiv.org/abs/2409.14744v2
• Date: Mon, 30 Sep 2024 15:36:26 GMT
• Title: LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs
• Authors: Sihui Yang, Keping Bi, Wanqing Cui, Jiafeng Guo, Xueqi Cheng,
• Abstract summary: Non-Factoid (NF) Question Answering (QA) is challenging to evaluate due to diverse potential answers and no objective criterion. Large Language Models (LLMs) have been resorted to for NFQA evaluation due to their compelling performance on various NLP tasks. We propose a novel listwise NFQA evaluation approach, that utilizes LLMs to rank candidate answers in a list of reference answers sorted by descending quality.
• Score: 61.57691505683534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-Factoid (NF) Question Answering (QA) is challenging to evaluate due to diverse potential answers and no objective criterion. The commonly used automatic evaluation metrics like ROUGE or BERTScore cannot accurately measure semantic similarities or answers from different perspectives. Recently, Large Language Models (LLMs) have been resorted to for NFQA evaluation due to their compelling performance on various NLP tasks. Common approaches include pointwise scoring of each candidate answer and pairwise comparisons between answers. Inspired by the evolution from pointwise to pairwise to listwise in learning-to-rank methods, we propose a novel listwise NFQA evaluation approach, that utilizes LLMs to rank candidate answers in a list of reference answers sorted by descending quality. Moreover, for NF questions that do not have multi-grade or any golden answers, we leverage LLMs to generate the reference answer list of various quality to facilitate the listwise evaluation. Extensive experimental results on three NFQA datasets, i.e., ANTIQUE, the TREC-DL-NF, and WebGLM show that our method has significantly higher correlations with human annotations compared to automatic scores and common pointwise and pairwise approaches.

Related papers

• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs [53.6200736559742]
  AGENT-CQ consists of two stages: a generation stage and an evaluation stage. CrowdLLM simulates human crowdsourcing judgments to assess generated questions and answers. Experiments on the ClariQ dataset demonstrate CrowdLLM's effectiveness in evaluating question and answer quality.
  arXiv  Detail & Related papers  (2024-10-25T17:06:27Z)
• RAG-ConfusionQA: A Benchmark for Evaluating LLMs on Confusing Questions [52.33835101586687]
  Conversational AI agents use Retrieval Augmented Generation (RAG) to provide verifiable document-grounded responses to user inquiries. This paper presents a novel synthetic data generation method to efficiently create a diverse set of context-grounded confusing questions from a given document corpus.
  arXiv  Detail & Related papers  (2024-10-18T16:11:29Z)
• AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses [26.850344968677582]
  We propose a method that leverages large language models to evaluate answers to open-ended questions. We conducted experiments on four datasets using both ChatGPT-3.5-turbo and GPT-4. Our results indicate that our approach more closely aligns with human judgment compared to the four baselines.
  arXiv  Detail & Related papers  (2024-10-02T05:22:07Z)
• Accurate and Nuanced Open-QA Evaluation Through Textual Entailment [4.762213968673381]
  We propose to study the entailment relations of answers to identify more informative and more general system answers. The entailment-based evaluation we propose allows the assignment of bonus or partial marks by quantifying the inference gap between answers.
  arXiv  Detail & Related papers  (2024-05-26T21:33:27Z)
• 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)
• PEDANTS: Cheap but Effective and Interpretable Answer Equivalence [10.367359022491181]
  We provide rubrics and datasets for evaluating machine QA adopted from the Trivia community. We also propose an efficient, and interpretable QA evaluation that is more stable than an exact match and neural methods(BERTScore)
  arXiv  Detail & Related papers  (2024-02-17T01:56:19Z)
• SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References [73.67707138779245]
  We propose a new evaluation metric: SQuArE (Sentence-level QUestion AnsweRing Evaluation) We evaluate SQuArE on both sentence-level extractive (Answer Selection) and generative (GenQA) QA systems.
  arXiv  Detail & Related papers  (2023-09-21T16:51:30Z)
• Evaluating Open-Domain Question Answering in the Era of Large Language Models [9.144650595481377]
  Lexical matching remains the de facto evaluation method for open-domain question answering (QA) Recent success of large language models (LLMs) for QA aggravates lexical matching failures since candidate answers become longer. Without accurate evaluation, the true progress in open-domain QA remains unknown.
  arXiv  Detail & Related papers  (2023-05-11T17:14:33Z)

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.