The paper adds Type II error metrics to the evaluation of relevance judgment sets and shows that balanced accuracy and Matthews correlation can summarize qrels' discriminative power in one number.
Report on the 1st Workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) at SIGIR 2024
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abstract
The first edition of the workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) took place in July 2024, co-located with the ACM SIGIR Conference 2024 in the USA (SIGIR 2024). The aim was to bring information retrieval researchers together around the topic of LLMs for evaluation in information retrieval that gathered attention with the advancement of large language models and generative AI. Given the novelty of the topic, the workshop was focused around multi-sided discussions, namely panels and poster sessions of the accepted proceedings papers.
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Measuring Hypothesis Testing Errors in the Evaluation of Retrieval Systems
The paper adds Type II error metrics to the evaluation of relevance judgment sets and shows that balanced accuracy and Matthews correlation can summarize qrels' discriminative power in one number.