7 citations · 12 across the 10 of their papers we have counts for
10 papers
Tool-MCoT: Tool Augmented Multimodal Chain-of-Thought for Content Safety Moderation
Shutong Zhang, Dylan Zhou, Yinxiao Liu +3
The growth of online platforms and user content requires strong content moderation systems that can handle complex inputs from various media types. While large language models (LLM…
Dual-Head Reasoning Distillation: Improving Classifier Accuracy with Train-Time-Only Reasoning
Jillian Xu, Dylan Zhou, Vinay Shukla +6
Chain-of-Thought (CoT) prompting often improves classification accuracy, but it introduces a significant throughput penalty with rationale generation (Wei et al., 2022; Cheng and V…
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
Congmin Zheng, Jiachen Zhu, Jianghao Lin +6
Process Reward Models (PRMs) play a central role in evaluating and guiding multi-step reasoning in large language models (LLMs), especially for mathematical problem solving. Howeve…
Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning
Shenao Zhang, Yaqing Wang, Yinxiao Liu +5
Large Language Models (LLMs) trained via Reinforcement Learning (RL) have exhibited strong reasoning capabilities and emergent reflective behaviors, such as rethinking and error co…
Improve Mathematical Reasoning in Language Models by Automated Process Supervision
Liangchen Luo, Yinxiao Liu, Rosanne Liu +9
Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a significant hurdle for even the most advanced large language models (LLMs). V…
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection
Yun Zhu, Jia-Chen Gu, Caitlin Sikora +8
Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external contexts. However, the input length grows linea…
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation
Meng Cao, Lei Shu, Lei Yu +4
Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of thes…
Fusion-Eval: Integrating Assistant Evaluators with LLMs
Lei Shu, Nevan Wichers, Liangchen Luo +4
Evaluating natural language systems poses significant challenges, particularly in the realms of natural language understanding and high-level reasoning. In this paper, we introduce…
Critique Ability of Large Language Models
Liangchen Luo, Zi Lin, Yinxiao Liu +4
Critical thinking is essential for rational decision-making and problem-solving. This skill hinges on the ability to provide precise and reasoned critiques and is a hallmark of hum…
Towards an On-device Agent for Text Rewriting
Yun Zhu, Yinxiao Liu, Felix Stahlberg +7
Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting. Nonetheless, the large sizes of these models make them impractical for on-device inferenc…