6 papers · 1 filter
Judgment-Grounded Expansion for Peer Review Generation
Sheng Lu, Lizhen Qu, Iryna Gurevych
Automatic review generation is a promising direction for accelerating scientific progress. While most work adopts an end-to-end setup, its fully automated nature makes it less suit…
Identifying Aspects in Peer Reviews
Sheng Lu, Ilia Kuznetsov, Iryna Gurevych
Peer review is central to academic publishing, but the growing volume of submissions is straining the process. This motivates the development of computational approaches to support…
Towards Contamination Resistant Benchmarks
Rahmatullah Musawi, Sheng Lu
The rapid development of large language models (LLMs) has transformed the landscape of natural language processing. Evaluating LLMs properly is crucial for understanding their pote…
Are Emergent Abilities in Large Language Models just In-Context Learning?
Sheng Lu, Irina Bigoulaeva, Rachneet Sachdeva +2
Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been speci…
How are Prompts Different in Terms of Sensitivity?
Sheng Lu, Hendrik Schuff, Iryna Gurevych
In-context learning (ICL) has become one of the most popular learning paradigms. While there is a growing body of literature focusing on prompt engineering, there is a lack of syst…
What Can Natural Language Processing Do for Peer Review?
Ilia Kuznetsov, Osama Mohammed Afzal, Koen Dercksen +21
The number of scientific articles produced every year is growing rapidly. Providing quality control over them is crucial for scientists and, ultimately, for the public good. In mod…