3 citations · 11 across the 11 of their papers we have counts for
4 papers · 1 filter
AAAR-1.0: Assessing AI's Potential to Assist Research
Renze Lou, Hanzi Xu, Sijia Wang +15
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, a…
LLMs' Classification Performance is Overclaimed
Hanzi Xu, Renze Lou, Jiangshu Du +6
In many classification tasks designed for AI or human to solve, gold labels are typically included within the label space by default, often posed as "which of the following is corr…
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Jiangshu Du, Yibo Wang, Wenting Zhao +37
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…
FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability
Congying Xia, Chen Xing, Jiangshu Du +5
This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capabi…