Large Language Models (LLMs) are transforming scholarly tasks like search and summarization, but their reliability remains uncertain. Current evaluation metrics...

With the rise of large language models (LLMs), they have become instrumental in applications such as Retrieval-Augmented Generation (RAG). Yet evaluating these ...

The paradigm of automated program generation is shifting from one-shot generation to inference-time search, where Large Language Models (LLMs) function as seman...

Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining...

How do large language models (LLMs) know what they know? Answering this question has been difficult because pre-training data is often a 'black box' -- unknown ...

BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 4th Ba...

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by conditioning generation on retrieved external documents, but the effect of retriev...

Large Language Models (LLMs) play a critical role in how humans access information. While their core use relies on comprehending written requests, our understan...

In this study, the output of large language models (LLM) is considered an information source generating an unlimited sequence of symbols drawn from a finite alp...

Large language models are being deployed in complex socio-technical systems, which exposes limits in current alignment practice. We take the position that the d...

Large language models (LLMs) offer substantial promise for automating clinical text summarization, yet maintaining factual consistency remains challenging due t...

Large Language Models (LLMs) have made significant progress in reasoning tasks across various domains such as mathematics and coding. However, their performance...