In-context learning can improve the performances of knowledge-rich tasks such as question
answering. In such scenarios, in-context examples trigger a language model (LM) to surface …

Natural language processing has seen rapid progress over the past decade. Due to the
speed of developments, some practices get established without proper evaluation …

Resolving knowledge conflicts is a crucial challenge in Question Answering (QA) tasks, as
the internet contains numerous conflicting facts and opinions. While some research has …

Different entities with the same name can be difficult to distinguish. Handling confusing entity
mentions is a crucial skill for language models (LMs). For example, given the question" …

The retrieval augmented generation (RAG) framework addresses an ambiguity in user
queries in QA systems by retrieving passages that cover all plausible interpretations and …

In this work, we tackle the challenge of disambiguating queries in retrieval-augmented
generation (RAG) to diverse yet answerable interpretations. State-of-the-arts follow a …