Abstract Direct Preference Optimization (DPO) is a widely used offline preference
optimization algorithm that reparameterizes reward functions in reinforcement learning from …

Ranking documents using Large Language Models (LLMs) by directly feeding the query and
candidate documents into the prompt is an interesting and practical problem. However …

This paper studies post-training large language models (LLMs) using preference feedback
from a powerful oracle to help a model iteratively improve over itself. The typical approach …

Automatic side-by-side evaluation has emerged as a promising approach to evaluating the
quality of responses from large language models (LLMs). However, analyzing the results …

Recent studies have shown that large language models'(LLMs) mathematical problem-
solving capabilities can be enhanced by integrating external tools, such as code …

The recent surge of versatile large language models (LLMs) largely depends on aligning
increasingly capable foundation models with human intentions by preference learning …

Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial
factors to achieve success is aligning the LLM's output with human preferences. This …

Large language models (LLMs) have demonstrated remarkable performances in various
tasks. However, the performance of LLMs heavily depends on the input prompt, which has …

Diffusion models have emerged as the leading paradigm in generative modeling, excelling
in various applications. Despite their success, these models often misalign with human …

Reinforcement learning from human feedback (RLHF) plays a crucial role in aligning
language models with human preferences. While the significance of dataset quality is …