LLMs Can Patch Up Missing Relevance Judgments In Evaluation
LLMs can "patch up" missing relevance judgments in IR system evaluation, improving robustness & reliability by predicting missing query-document pairs with high accuracy.
This is a Plain English Papers summary of a research paper called LLMs Can Patch Up Missing Relevance Judgments in Evaluation. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter. Overview This paper explores how large language models (LLMs) can be used to patch up missing relevance judgments in the evaluation of information retrieval (IR) systems. The researchers propose a novel method that leverages LLMs to generate relevance judgments for query-document pairs that are missing from standard IR evaluation datasets. The pap...