Language models contain ranking-based knowledge and are powerful solvers of in-context
ranking tasks. For instance, they may have parametric knowledge about the ordering of …

Neural document ranking models perform impressively well due to superior language
understanding gained from pre-training tasks. However, due to their complexity and large …

A key problem in information retrieval is understanding the latent intention of a user's under-
specified query. Retrieval models that are able to correctly uncover the query intent often …

Ranking documents using Large Language Models (LLMs) by directly feeding the query and
candidate documents into the prompt is an interesting and practical problem. However …

Large language models are often ranked according to their level of alignment with human
preferences--a model is better than other models if its outputs are more frequently preferred …

Large language models (LLMs) exhibit positional bias in how they use context, which
especially complicates listwise ranking. To address this, we propose permutation self …

Answering multiple-choice questions in a setting in which no supporting documents are
explicitly provided continues to stand as a core problem in natural language processing. The …

Text ranking is a critical task in information retrieval. Recent advances in pre-trained
language models (PLMs), especially large language models (LLMs), present new …

Large Language Models (LLMs) have been revolutionizing a myriad of natural language
processing tasks with their diverse zero-shot capabilities. Indeed, existing work has shown …

Comparative reasoning plays a crucial role in text preference prediction; however, large
language models (LLMs) often demonstrate inconsistencies in their reasoning. While …