3 citations · 6 across the 6 of their papers we have counts for
9 papers
Scaling Reward Modeling without Human Supervision
Jingxuan Fan, Yueying Li, Zhenting Qi +4
Learning from feedback is an instrumental process for advancing the capabilities and safety of frontier models, yet its effectiveness is often constrained by cost and scalability.…
P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains
Simeng Han, Aaron Yu, Rui Shen +13
Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales, which are not sufficie…
Quantifying Generalization Complexity for Large Language Models
Zhenting Qi, Hongyin Luo, Xuliang Huang +5
While large language models (LLMs) have shown exceptional capabilities in understanding complex queries and performing sophisticated tasks, their generalization abilities are often…
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
Zhenting Qi, Mingyuan Ma, Jiahang Xu +3
This paper introduces rStar, a self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superio…
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems
Zhenting Qi, Hanlin Zhang, Eric Xing +2
Retrieval-Augmented Generation (RAG) improves pre-trained models by incorporating external knowledge at test time to enable customized adaptation. We study the risk of datastore le…
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
Yilun Zhao, Chen Zhao, Linyong Nan +5
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task…