Despite their impressive performance on diverse tasks, large language models (LMs) still
struggle with tasks requiring rich world knowledge, implying the limitations of relying solely …

Lexical matching remains the de facto evaluation method for open-domain question
answering (QA). Unfortunately, lexical matching fails completely when a plausible candidate …

On-the-fly retrieval of relevant knowledge has proven an essential element of reliable
systems for tasks such as open-domain question answering and fact verification. However …

Retrieval-augmented generation models offer many benefits over standalone language
models: besides a textual answer to a given query they provide provenance items retrieved …

Open-domain question answering (QA) systems are often built with retrieval modules.
However, retrieving passages from a given source is known to suffer from insufficient …

Instruction-tuned large language models (LLMs) have been shown to be viable surrogates
for the widely used, albeit overly rigid, lexical matching metrics in evaluating question …

Information seeking users often pose questions with false presuppositions, especially when
asking about unfamiliar topics. Most existing question answering (QA) datasets, in contrast …

For knowledge intensive NLP tasks, it has been widely accepted that accessing more
information is a contributing factor to improvements in the model's end-to-end performance …

Query-focused summarization (QFS) is a fundamental task in natural language processing
with broad applications, including search engines and report generation. However …

Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of
generating answers by simultaneously referring to multiple passages. Although …