3 citations · 5 across the 9 of their papers we have counts for
10 papers
You Shouldn't Have Asked: A Pragmatics-Inspired Taxonomy for Evaluating LLM Refusals
Ruoxuan Li, Pinqiao Wang, Sheng Li +1
Refusals are often treated as face-threatening acts in pragmatics because they can challenge the requester's socially claimed self-image. Large language models (LLMs) are increasin…
ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Models
Tianlong Wang, Pinqiao Wang, Weili Shi +1
Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on specific r…
IndustryNav: Evaluating Spatial Reasoning and Safety of Embodied Agents in Dynamic Industrial Navigation
Yifan Li, Lichi Li, Anh Dao +15
Visual large language models (VLLMs) are increasingly used as embodied agents, yet industrial warehouses demand more than reaching a goal: agents must navigate safely around moving…
BEACON: Bayesian Optimal Stopping for Efficient LLM Sampling
Guangya Wan, Zixin Stephen Xu, Sasa Zorc +4
Sampling multiple responses is a common way to improve LLM output quality, but it comes at the cost of additional computation. The key challenge is deciding when to stop generating…
COMPASS: Enhancing Agent Long-Horizon Reasoning with Evolving Context
Guangya Wan, Mingyang Ling, Xiaoqi Ren +3
Long-horizon tasks that require sustained reasoning and multiple tool interactions remain challenging for LLM agents: small errors compound across steps, and even state-of-the-art…
Memory in Large Language Models: Mechanisms, Evaluation and Evolution
Dianxing Zhang, Wendong Li, Kani Song +4
Under a unified operational definition, we define LLM memory as a persistent state written during pretraining, finetuning, or inference that can later be addressed and that stably…
Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American English
Runtao Zhou, Guangya Wan, Saadia Gabriel +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning tasks, leading to their widespread deployment. However, recent studies have highlighted concerni…
Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling
Guangya Wan, Yuqi Wu, Jie Chen +1
Self-Consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths,but it lacks a systematic approach to determine the optimal number of…
Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning
Guangya Wan, Yuqi Wu, Hao Wang +3
Large Language Models (LLMs) have shown impressive reasoning capabilities, yet existing prompting methods face a critical trade-off: simple approaches often struggle with complex t…
Through the Theory of Mind's Eye: Reading Minds with Multimodal Video Large Language Models
Zhawnen Chen, Tianchun Wang, Yizhou Wang +4
Can large multimodal models have a human-like ability for emotional and social reasoning, and if so, how does it work? Recent research has discovered emergent theory-of-mind (ToM)…