6 papers
LVLMs and Humans Ground Differently in Referential Communication
Peter Zeng, Weiling Li, Amie J. Paige +6
For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical. But this ability to collaborate remains limited by a c…
Measuring Iterative Temporal Reasoning with Time Puzzles
Zhengxiang Wang, Zeyu Dong
Tool use, such as web search, has become a standard capability even in freely available large language models (LLMs). However, existing benchmarks evaluate temporal reasoning mainl…
LVLMs are Bad at Overhearing Human Referential Communication
Zhengxiang Wang, Weiling Li, Panagiotis Kaliosis +2
During spontaneous conversations, speakers collaborate on novel referring expressions, which they can then re-use in subsequent conversations. Understanding such referring expressi…
Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors
Zhengxiang Wang, Nafis Irtiza Tripto, Solha Park +2
As large language models (LLMs) become increasingly integrated into personal writing tools, a critical question arises: can LLMs faithfully imitate an individual's writing style fr…
Evaluating LLMs with Multiple Problems at once
Zhengxiang Wang, Jordan Kodner, Owen Rambow
This paper shows the benefits and fruitfulness of evaluating LLMs with multiple problems at once, a paradigm we call multi-problem evaluation (MPE). Unlike conventional single-prob…
LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing
Zhengxiang Wang, Veronika Makarova, Zhi Li +2
The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multipl…