Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as “is aspartame linked to cancer”.
To resolve these ambiguous queries, one must search through a large range of websites and considerwhich, if any, of this evidence do I find convincing?In this work, we study how LLMs answer this question.
In particular, we constructConflictingQA, a dataset that pairs controversial queries with a series of real-world evidence documents that contain different facts (e.g., quantitative results) , argument styles (e.g., appeals to authority) , and answers (YesorNo) . We use this dataset to perform sensitivity and counterfactual analyses to explore which text features most affect LLM predictions. Overall, we find that current models rely heavily on therelevanceof a website to the query, while largely ignoringstylisticfeatures that humans find important such as whether a text contains scientific references or is written with a neutral tone.
Taken together, these results highlight the importance of RAG corpus quality (e.g., the need to filter misinformation) , and possibly even a shift in how LLMs are trained to better align with human judgements.