11 citations · 26 across the 18 of their papers we have counts for
5 papers · 2 filters
LitLLMs, LLMs for Literature Review: Are we there yet?
Shubham Agarwal, Gaurav Sahu, Abhay Puri +5
Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially due to the recent influx of research paper…
IntentGPT: Few-shot Intent Discovery with Large Language Models
Juan A. Rodriguez, Nicholas Botzer, David Vazquez +3
In today's digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to virtual assistants. In these dialogues, it is impor…
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
Joao Monteiro, Pierre-Andre Noel, Etienne Marcotte +6
Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includes encyclopedic documents that harbor a…
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
João Monteiro, Étienne Marcotte, Pierre-André Noël +5
In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference information. Just-in-time processing of a context…
LitLLM: A Toolkit for Scientific Literature Review
Shubham Agarwal, Gaurav Sahu, Abhay Puri +5
Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work. It is a tedious task which makes an aut…