13 citations · 29 across the 6 of their papers we have counts for
6 papers
Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth
Gaurav Sahu, Laurent Charlin, Christopher Pal
We study large-scale literature search from two complementary angles: improving the retrieval pipeline, and stress-testing the human reference list as an evaluation target. First,…
ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review
Gaurav Sahu, Hugo Larochelle, Laurent Charlin +1
Peer review is the cornerstone of scientific publishing, yet it suffers from inconsistencies, reviewer subjectivity, and scalability challenges. We introduce ReviewerToo, a modular…
AInstein: Can LLMs Solve Research Problems From Parametric Memory Alone?
Shambhavi Mishra, Gaurav Sahu, Marco Pedersoli +3
Can large language models solve AI research problems using only their parametric knowledge, without fine-tuning, retrieval, or other external aids? We introduce AInstein, a framewo…
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…
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…
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Yao Lu, Yue Dong, Laurent Charlin
Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset…