16 citations · 19 across the 13 of their papers we have counts for
16 papers
HERA: Harness-Environment Co-Evolution for Reliable Agentic Abstention
Han Luo, Bingbing Wen, Guang Yang +3
Large language model (LLM) agents are increasingly capable of acting in complex tool-use environments, yet they often fail to recognize when tasks are infeasible and no valid solut…
You're Hired: Strategic Model Selection for LLM Collaboration
Zongwan Cao, Ziyuan Yang, Shangbin Feng +5
While multi-agent and model collaboration algorithms gain traction to combine the strengths of diverse Large Language Models (LLMs), existing systems remain bottlenecked on pre-def…
Spurious Tool Use: When RL Agents Learn the Wrong Reason to Act
Yiwei Yang, Haoxiang Zhang, Bingbing Wen +6
Large language model (LLM) agents increasingly interleave natural language reasoning with external tools such as web search and code execution. These tool-use policies are often op…
Agentic Abstention: Do Agents Know When to Stop Instead of Act?
Han Luo, Bingbing Wen, Lucy Lu Wang
LLM agents are expected to act over multiple turns, using search, browsing interfaces, and terminal tools to complete user goals. Yet not every goal is well specified or achievable…
Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation
Katelyn Xiaoying Mei, Yi-Li Hsu, Minjoon Choi +5
Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these evaluations depend on transparent and well-…
STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes
Chenjun Xu, Zhennan Zhou, Zhan Su +3
Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking. This inefficiency is especially problematic in low-data fi…
MixAtlas: Uncertainty-aware Data Mixture Optimization for Multimodal LLM Midtraining
Bingbing Wen, Sirajul Salekin, Feiyang Kang +4
Domain reweighting can improve sample efficiency and downstream generalization, but data-mixture optimization for multimodal midtraining remains largely unexplored. Current multimo…
Clarify or Answer: Reinforcement Learning for Agentic VQA with Context Under-specification
Zongwan Cao, Bingbing Wen, Lucy Lu Wang
Real-world visual question answering (VQA) may be context-dependent: an image-question pair could be under-specified, such that the correct answer depends on external information t…
SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agents
Longjie Guo, Chenjie Yuan, Mingyuan Zhong +7
As LLM-based computer-use agents (CUAs) begin to autonomously interact with real-world interfaces, understanding their vulnerability to manipulative interface designs becomes incre…
Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
Chenjun Xu, Bingbing Wen, Bin Han +3
Psychology research has shown that humans are poor at estimating their performance on tasks, tending towards underconfidence on easy tasks and overconfidence on difficult tasks. We…
Escaping the SpuriVerse: Can Large Vision-Language Models Generalize Beyond Seen Spurious Correlations?
Yiwei Yang, Chung Peng Lee, Shangbin Feng +5
Finetuning can cause spurious correlations to arise between non-essential features and the target labels, but benchmarks to study these effects involve contrived settings and narro…
MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation
Jihan Yao, Yushi Hu, Yujie Yi +10
Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align with human evaluation, especially for complex tasks th…
Know Your Limits: A Survey of Abstention in Large Language Models
Bingbing Wen, Jihan Yao, Shangbin Feng +4
Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in LLM syst…
Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings
Robert Wolfe, Isaac Slaughter, Bin Han +8
The rapid proliferation of generative AI has raised questions about the competitiveness of lower-parameter, locally tunable, open-weight models relative to high-parameter, API-guar…
Characterizing LLM Abstention Behavior in Science QA with Context Perturbations
Bingbing Wen, Bill Howe, Lucy Lu Wang
The correct model response in the face of uncertainty is to abstain from answering a question so as not to mislead the user. In this work, we study the ability of LLMs to abstain f…
InfoVisDial: An Informative Visual Dialogue Dataset by Bridging Large Multimodal and Language Models
Bingbing Wen, Zhengyuan Yang, Jianfeng Wang +3
In this paper, we build a visual dialogue dataset, named InfoVisDial, which provides rich informative answers in each round even with external knowledge related to the visual conte…