4 citations · 19 across the 13 of their papers we have counts for
14 papers
WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning
Yuheng Zha, Yilei Wang, Qiyue Gao +5
Humans often solve spatial problems by mentally simulating visual transformations. In contrast, conventional vision-language models (VLMs) reason primarily through language. We inv…
Reinforcement Learning without Ground-Truth Solutions can Improve LLMs
Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang +6
Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth answers to assign rewards, limiting their applicability to tasks where the gr…
CocoaBench: Evaluating Unified Digital Agents in the Wild
CocoaBench Team, Shibo Hao, Zhining Zhang +29
LLM agents now perform strongly in software engineering, deep research, GUI automation, and various other applications, while recent agent scaffolds and models are increasingly int…
Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis
Prasiddha Bhandari, Kanchan Poudel, Nishant Luitel +6
Blind Sweep Obstetric Ultrasound (BSOU) enables scalable fetal imaging in low-resource settings by allowing minimally trained operators to acquire standardized sweep videos for aut…
Math Natural Language Inference: this should be easy!
Valeria de Paiva, Qiyue Gao, Hai Hu +4
We ask whether contemporary LLMs are able to perform natural language inference (NLI) tasks on mathematical texts. We call this the Math NLI problem. We construct a corpus of Math…
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation
Qiyue Gao, Xinyu Pi, Kevin Liu +21
Internal world models (WMs) enable agents to understand the world's state and predict transitions, serving as the basis for advanced deliberative reasoning. Recent large Vision-Lan…
Pandora: Towards General World Model with Natural Language Actions and Video States
Jiannan Xiang, Guangyi Liu, Yi Gu +10
World models simulate future states of the world in response to different actions. They facilitate interactive content creation and provides a foundation for grounded, long-horizon…
LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
Shibo Hao, Yi Gu, Haotian Luo +9
Generating accurate step-by-step reasoning is essential for Large Language Models (LLMs) to address complex problems and enhance robustness and interpretability. Despite the flux o…
Extracting Mathematical Concepts with Large Language Models
Valeria de Paiva, Qiyue Gao, Pavel Kovalev +1
We extract mathematical concepts from mathematical text using generative large language models (LLMs) like ChatGPT, contributing to the field of automatic term extraction (ATE) and…
DISCO: Distilling Counterfactuals with Large Language Models
Zeming Chen, Qiyue Gao, Antoine Bosselut +2
Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, high-quality counterfactual dat…
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding
Zeming Chen, Qiyue Gao
In the age of large transformer language models, linguistic evaluation play an important role in diagnosing models' abilities and limitations on natural language understanding. How…
Probing Linguistic Information For Logical Inference In Pre-trained Language Models
Zeming Chen, Qiyue Gao
Progress in pre-trained language models has led to a surge of impressive results on downstream tasks for natural language understanding. Recent work on probing pre-trained language…
NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning
Zeming Chen, Qiyue Gao, Lawrence S. Moss
Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI). And at this time, symbolic approaches to NLI are recei…
Monotonicity Marking from Universal Dependency Trees
Zeming Chen, Qiyue Gao
Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics. However, there is hardly any work that connects dependency parsi…