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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…
SiRA: Sparse Mixture of Low Rank Adaptation
Yun Zhu, Nevan Wichers, Chu-Cheng Lin +8
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks. Most previous works considers adding the dense trainable parameters…
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…