3 citations · 11 across the 11 of their papers we have counts for
13 papers
Searching for What Matters: Open-Ended Discovery of Multiple Hypotheses with Agentic Data Exploration
Sanchit Kabra, Nurendra Choudhary, Jiangshu Du +3
Discovering what drives an outcome from data is central to science and often requires deciding what is worth investigating before a specific hypothesis is known. Yet most existing…
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…
AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
Mingdai Yang, Nurendra Choudhary, Jiangshu Du +4
Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-ex…
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…
MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms
Yibo Wang, Congying Xia, Wenting Zhao +5
Unit test generation has become a promising and important Large Language Model (LLM) use case. However, existing evaluation benchmarks for LLM unit test generation focus on functio…
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…
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning
Wenting Zhao, Ye Liu, Yao Wan +7
Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine eleme…
All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm
Jiangshu Du, Congying Xia, Wenpeng Yin +2
In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios. However, existing few-shot intent de…
Learning to Select from Multiple Options
Jiangshu Du, Wenpeng Yin, Congying Xia +1
Many NLP tasks can be regarded as a selection problem from a set of options, such as classification tasks, multi-choice question answering, etc. Textual entailment (TE) has been sh…
Cross-lingual COVID-19 Fake News Detection
Jiangshu Du, Yingtong Dou, Congying Xia +3
The COVID-19 pandemic poses a great threat to global public health. Meanwhile, there is massive misinformation associated with the pandemic which advocates unfounded or unscientifi…