1 citations · 3 across the 6 of their papers we have counts for
9 papers · 1 filter
ScaleFormer: Span Representation Cumulation for Long-Context Transformer
Jiangshu Du, Wenpeng Yin, Philip Yu
The quadratic complexity of standard self-attention severely limits the application of Transformer-based models to long-context tasks. While efficient Transformer variants exist, t…
Could AI Trace and Explain the Origins of AI-Generated Images and Text?
Hongchao Fang, Yixin Liu, Jiangshu Du +7
AI-generated content is becoming increasingly prevalent in the real world, leading to serious ethical and societal concerns. For instance, adversaries might exploit large multimoda…
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…