6 citations · 10 across the 10 of their papers we have counts for
10 papers
BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks
Jin Huang, Yutong Xie, Wanli Song +5
Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics. While these models show promise in tasks such as survey…
IRIS: time-structured manifold projections
Brian Ondov, Chia-Hsuan Chang, Weipeng Zhou +6
High-dimensional biomedical data, such as cell-by-gene matrices, are increasingly generated temporally. However, Manifold Learning algorithms, like t-SNE and UMAP, cannot incorpora…
FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
Xingjian Zhang, Sophia Moylan, Ziyang Xiong +3
Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems.…
Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks
Xinhe Wang, Jin Huang, Xingjian Zhang +2
Reasoning benchmarks such as the Abstraction and Reasoning Corpus (ARC) and ARC-AGI are widely used to assess progress in artificial intelligence and are often interpreted as probe…
Through the Judge's Eyes: Inferred Thinking Traces Improve Reliability of LLM Raters
Xingjian Zhang, Tianhong Gao, Suliang Jin +4
Large language models (LLMs) are increasingly used as raters for evaluation tasks. However, their reliability is often limited for subjective tasks, when human judgments involve su…
Be.FM: Open Foundation Models for Human Behavior
Yutong Xie, Zhuoheng Li, Xiyuan Wang +10
Despite their success in numerous fields, the potential of foundation models for modeling and understanding human behavior remains largely unexplored. We introduce Be.FM, one of th…
MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows
Xingjian Zhang, Yutong Xie, Jin Huang +8
Scientific innovation relies on detailed workflows, which include critical steps such as analyzing literature, generating ideas, validating these ideas, interpreting results, and i…
DCA-Bench: A Benchmark for Dataset Curation Agents
Benhao Huang, Yingzhuo Yu, Jin Huang +2
The quality of datasets plays an increasingly crucial role in the research and development of modern artificial intelligence (AI). Despite the proliferation of open dataset platfor…
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
Jin Huang, Xingjian Zhang, Qiaozhu Mei +1
Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies de…
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
Weng Lam Tam, Xiao Liu, Kaixuan Ji +6
Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved comparable performance to fine-tuning of the full parameter set on both languag…