Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions

• URL: http://arxiv.org/abs/2408.09831v1
• Date: Mon, 19 Aug 2024 09:27:45 GMT
• Title: Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions
• Authors: Sebastian Heineking, Jonas Probst, Daniel Steinbach, Martin Potthast, Harrisen Scells,
• Abstract summary: 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.
• Score: 25.158868133182025
• License:
• Abstract: Evaluating the output of generative large language models (LLMs) is challenging and difficult to scale. Most evaluations of LLMs focus on tasks such as single-choice question-answering or text classification. These tasks are not suitable for assessing open-ended question-answering capabilities, which are critical in domains where expertise is required, such as health, and where misleading or incorrect answers can have a significant impact on a user's health. Using human experts to evaluate the quality of LLM answers is generally considered the gold standard, but expert annotation is costly and slow. We present a method for evaluating LLM answers that uses ranking signals as a substitute for explicit relevance judgements. 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 as well as with more sophisticated prompting strategies.

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