Reinforcement learning from human feedback (RLHF) has become a pivot technique in
aligning large language models (LLMs) with human preferences. In RLHF practice …

Large Language Models (LLMs) trained on extensive textual corpora have emerged as
leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite …

Recently, GPT-4 has become the de facto evaluator for long-form text generated by large
language models (LLMs). However, for practitioners and researchers with large and custom …

The rapid development of Large Language Models (LLMs) has substantially expanded the
range of tasks they can address. In the field of Natural Language Processing (NLP) …

Self-correction is a highly desirable capability of large language models (LLMs), yet it has
consistently been found to be largely ineffective in modern LLMs. Current methods for …

Evaluation of Large Language Models (LLMs) is challenging because aligning to human
values requires the composition of multiple skills and the required set of skills varies …

As large language models improve, there is increasing interest in techniques that leverage
these models' capabilities to refine their own outputs. In this work, we introduce Shepherd, a …

Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations.
To address these, studies prefixed with" Self-" such as Self-Consistency, Self-Improve, and …

This survey presents an in-depth exploration of knowledge distillation (KD) techniques
within the realm of Large Language Models (LLMs), spotlighting the pivotal role of KD in …

The recent success of Large Language Models (LLMs) has catalyzed an increasing interest
in their self-correction capabilities. This paper presents a comprehensive investigation into …