Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need

• URL: http://arxiv.org/abs/2406.18064v3
• Date: Thu, 07 Nov 2024 04:03:04 GMT
• Title: Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need
• Authors: Yang Wang, Alberto Garcia Hernandez, Roman Kyslyi, Nicholas Kersting,
• Abstract summary: We present a comprehensive study of answer quality evaluation in Retrieval-Augmented Generation (RAG) applications using vRAG-Eval. We map the grading of quality aspects into a binary score, indicating an accept or reject decision. This approach suits factual business contexts where a clear decision opinion is essential.
• Score: 3.3624592634336814
• License:
• Abstract: We present a comprehensive study of answer quality evaluation in Retrieval-Augmented Generation (RAG) applications using vRAG-Eval, a novel grading system that is designed to assess correctness, completeness, and honesty. We further map the grading of quality aspects aforementioned into a binary score, indicating an accept or reject decision, mirroring the intuitive "thumbs-up" or "thumbs-down" gesture commonly used in chat applications. This approach suits factual business contexts where a clear decision opinion is essential. Our assessment applies vRAG-Eval to two Large Language Models (LLMs), evaluating the quality of answers generated by a vanilla RAG application. We compare these evaluations with human expert judgments and find a substantial alignment between GPT-4's assessments and those of human experts, reaching 83% agreement on accept or reject decisions. This study highlights the potential of LLMs as reliable evaluators in closed-domain, closed-ended settings, particularly when human evaluations require significant resources.

Related papers

• Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage [74.70255719194819]
  We introduce a novel framework based on sub-question coverage, which measures how well a RAG system addresses different facets of a question. We use this framework to evaluate three commercial generative answer engines: You.com, Perplexity AI, and Bing Chat. We find that while all answer engines cover core sub-questions more often than background or follow-up ones, they still miss around 50% of core sub-questions.
  arXiv  Detail & Related papers  (2024-10-20T22:59:34Z)
• MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generation [0.4857223913212445]
  We propose a novel system, MIRROR, to automate the evaluation process for questions generated by automated question generation systems. We observed that the scores of human evaluation metrics, namely relevance, appropriateness, novelty, complexity, and grammaticality, improved when using the feedback-based approach called MIRROR.
  arXiv  Detail & Related papers  (2024-10-16T12:24:42Z)
• 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)
• 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)
• Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models [92.66784679667441]
  Prometheus 2 is a more powerful evaluator LM that closely mirrors human and GPT-4 judgements. It is capable of processing both direct assessment and pairwise ranking formats grouped with a user-defined evaluation criteria. On four direct assessment benchmarks and four pairwise ranking benchmarks, Prometheus 2 scores the highest correlation and agreement with humans and proprietary LM judges.
  arXiv  Detail & Related papers  (2024-05-02T17:59:35Z)
• HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition [92.17397504834825]
  HD-Eval is a framework that iteratively aligns large language models evaluators with human preference. HD-Eval inherits the essence from the evaluation mindset of human experts and enhances the alignment of LLM-based evaluators. Extensive experiments on three evaluation domains demonstrate the superiority of HD-Eval in further aligning state-of-the-art evaluators.
  arXiv  Detail & Related papers  (2024-02-24T08:01:32Z)
• Style Over Substance: Evaluation Biases for Large Language Models [17.13064447978519]
  This study investigates the behavior of crowd-sourced and expert annotators, as well as large language models (LLMs) Our findings reveal a concerning bias in the evaluation process, as answers with factual errors are rated more favorably than answers that are too short or contained grammatical errors. We propose independently evaluating machine-generated text across multiple dimensions, rather than merging all the evaluation aspects into a single score.
  arXiv  Detail & Related papers  (2023-07-06T14:42:01Z)
• A Critical Evaluation of Evaluations for Long-form Question Answering [48.51361567469683]
  Long-form question answering (LFQA) enables answering a wide range of questions, but its flexibility poses enormous challenges for evaluation. We perform the first targeted study of the evaluation of long-form answers, covering both human and automatic evaluation practices.
  arXiv  Detail & Related papers  (2023-05-29T16:54:24Z)
• Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response [56.25966921370483]
  There are challenges in using reference-free evaluators based on large language models. Reference-free evaluators are more suitable for open-ended examples with different semantics responses. There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
  arXiv  Detail & Related papers  (2023-05-24T02:52:48Z)
• Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation [136.16507050034755]
  Existing human evaluation studies for summarization either exhibit a low inter-annotator agreement or have insufficient scale. We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which is based on fine-grained semantic units. We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of 22,000 summary-level annotations over 28 top-performing systems.
  arXiv  Detail & Related papers  (2022-12-15T17:26:05Z)

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.