核心概念
Unsupervised alignment method RLCF significantly improves LLM responses' distinctiveness in Information Retrieval tasks.
摘要
The content discusses the proposal of an unsupervised alignment method, Reinforcement Learning from Contrastive Feedback (RLCF), to enhance Large Language Models (LLMs) for Information Retrieval. It addresses the limitations of existing alignment methods and demonstrates the effectiveness of RLCF through extensive experiments. The document is structured into sections covering Introduction, Related Work, Reinforcement Learning from Contrastive Feedback, Experimental Setup, Experimental Results, Efficiency Analysis, and a Case Study.
Introduction
Large Language Models (LLMs) have shown promise in Information Retrieval (IR) tasks.
Off-the-shelf LLMs may lack distinctiveness in responses for IR tasks.
Proposed unsupervised alignment method RLCF aims to enhance LLM responses in IR.
Related Work
Existing alignment methods like RLHF, RLAIF, and RLCD have limitations in improving distinctiveness.
RLCF introduces group-wise contrastive feedback to address the shortcomings of point-wise feedback.
Reinforcement Learning from Contrastive Feedback
RLCF constructs contrastive feedback based on similar document groups.
Utilizes Proximal Policy Optimization to optimize LLMs with group-wise feedback.
Outperforms existing alignment methods in generating distinctive responses.
Experimental Setup
Evaluates RLCF on various IR tasks like document summarization, document expansion, and data augmentation.
Utilizes different scales of LLMs and languages for evaluation.
Implements experiments with PyTorch, Huggingface, and DeepSpeed with ZeRO stage 2.
Experimental Results
RLCF significantly improves document summarization with higher Rouge-diff scores.
Document expansion for sparse retrieval shows improvement with RLCF.
Data augmentation for dense retrieval demonstrates consistent performance enhancement with RLCF.
Efficiency Analysis
RLCF shows efficient group-wise feedback computation compared to point-wise methods.
Inference times and GPU memory usage are significantly lower for RLCF.
Case Study
Illustrates the effectiveness of RLCF in generating distinctive queries for data augmentation.
Shows the improvement in query distinctiveness with RLCF in dense retrieval tasks.
統計資料
RLCF optimization significantly improves the Rouge-diff on document summarization tasks.
RLCF outperforms other alignment methods in document expansion for sparse retrieval.
RLCF consistently improves data augmentation for dense retrieval across various datasets.
引述
"RLCF optimization significantly improves the Rouge-diff on the test set."
"RLCF-optimized LLMs contain more distinctive information than those produced by vanilla LLMs."
"RLCF-optimized LLMs consistently outperform other alignment methods in data augmentation for dense retrieval."