1 citations · 1 across the 5 of their papers we have counts for
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In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback
Mingye Zhu, Yi Liu, Zheren Fu +2
Training Large Language Models (LLMs) for chain-of-thought reasoning presents a significant challenge: supervised fine-tuning on a single "golden" rationale hurts generalization as…
Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models
Yi Liu, Dianqing Liu, Mingye Zhu +3
The widespread adoption of large language models (LLMs) across industries has increased the demand for high-quality and customizable outputs. However, traditional alignment methods…
Leveraging Robust Optimization for LLM Alignment under Distribution Shifts
Mingye Zhu, Yi Liu, Zheren Fu +2
Preference alignment methods are increasingly critical for steering large language models (LLMs) to generate outputs consistent with human values. While recent approaches often rel…
On-the-fly Preference Alignment via Principle-Guided Decoding
Mingye Zhu, Yi Liu, Lei Zhang +2
With the rapidly expanding landscape of large language models, aligning model generations with human values and preferences is becoming increasingly important. Popular alignment me…