CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering

• URL: http://arxiv.org/abs/2401.13170v4
• Date: Sun, 30 Jun 2024 02:09:46 GMT
• Title: CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering
• Authors: Zongxia Li, Ishani Mondal, Yijun Liang, Huy Nghiem, Jordan Boyd-Graber,
• Abstract summary: Question answering (QA) can only make progress if we know if an answer is correct. Current evaluation metrics to determine answer equivalence (AE) often do not align with human judgments.
• Score: 14.366087533102656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current evaluation metrics to determine answer equivalence (AE) often do not align with human judgments, particularly more verbose, free-form answers from large language models (LLM). There are two challenges: a lack of data and that models are too big: LLM-based scorers can correlate better with human judges, but this task has only been tested on limited QA datasets, and even when available, update of the model is limited because LLMs are large and often expensive. We rectify both of these issues by providing clear and consistent guidelines for evaluating AE in machine QA adopted from professional human QA contests. We also introduce a combination of standard evaluation and a more efficient, robust, and lightweight discriminate AE classifier-based matching method (CFMatch, smaller than 1 MB), trained and validated to more accurately evaluate answer correctness in accordance with adopted expert AE rules that are more aligned with human judgments.

Related papers

• JudgeBench: A Benchmark for Evaluating LLM-based Judges [61.048125269475854]
  JudgeBench is a benchmark for evaluating LLM-based judges on challenging response pairs spanning knowledge, reasoning, math, and coding. Our comprehensive evaluation on a collection of prompted judges, fine-tuned judges, multi-agent judges, and reward models shows that JudgeBench poses a significantly greater challenge than previous benchmarks.
  arXiv  Detail & Related papers  (2024-10-16T17:58:19Z)
• LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs [61.57691505683534]
  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.
  arXiv  Detail & Related papers  (2024-09-23T06:42:21Z)
• Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions [25.158868133182025]
  We present a method for evaluating the output of generative large language models (LLMs) Our scoring method correlates with the preferences of human experts. We validate it by investigating the well-known fact that the quality of generated answers improves with the size of the model.
  arXiv  Detail & Related papers  (2024-08-19T09:27:45Z)
• RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering [61.19126689470398]
  Long-form RobustQA (LFRQA) is a new dataset covering 26K queries and large corpora across seven different domains. We show via experiments that RAG-QA Arena and human judgments on answer quality are highly correlated. Only 41.3% of the most competitive LLM's answers are preferred to LFRQA's answers, demonstrating RAG-QA Arena as a challenging evaluation platform for future research.
  arXiv  Detail & Related papers  (2024-07-19T03:02:51Z)
• On scalable oversight with weak LLMs judging strong LLMs [67.8628575615614]
  We study debate, where two AI's compete to convince a judge; consultancy, where a single AI tries to convince a judge that asks questions. We use large language models (LLMs) as both AI agents and as stand-ins for human judges, taking the judge models to be weaker than agent models.
  arXiv  Detail & Related papers  (2024-07-05T16:29:15Z)
• 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)
• 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)
• Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation [11.733609600774306]
  Question answering systems are typically evaluated against manually annotated finite sets of one or more answers. This leads to a coverage limitation that results in underestimating the true performance of systems. We present the first systematic conceptual and data-driven analysis to examine the shortcomings of token-level equivalence measures.
  arXiv  Detail & Related papers  (2022-02-15T18:53:58Z)
• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs [62.71505254770827]
  We propose a conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts. Our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
  arXiv  Detail & Related papers  (2020-05-28T08:26:06Z)

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.