为检索增强型大语言模型提供多任务嵌入器
A Multi-Task Embedder For Retrieval Augmented LLMs
AI总结：
LLM-Embedder通过引入排名感知奖励和分级蒸馏，统一多任务检索增强，提升大语言模型在多种下游任务中的性能。
摘要：
大语言模型在知识、记忆和行动方面存在固有局限性。检索增强是一种关键机制，通过引入外部信息来弥补这些不足。然而，现有检索方法面临两个紧迫问题。一方面，通用检索器未被适当地优化以支持检索增强，因此效果有限；另一方面，任务特定检索器在目标检索增强场景中表现优异，但缺乏处理多样化场景的通用性。在本工作中，我们提出了LLM-Embedder，以统一支持多种检索增强场景。我们的方法有三个技术贡献。首先，我们引入了一种新的奖励公式，即排名感知奖励。它利用从LLM中采样的N个输出中目标输出的排名位置，从而实现对LLM反馈的细粒度和鲁棒计算。其次，我们设计了一种新的蒸馏目标，称为分级蒸馏。它结合了奖励的绝对值和相对顺序，以更充分地利用LLM的反馈。第三，我们系统地优化了多任务学习，将多种检索功能统一到一个模型中。在我们的实验中，LLM-Embedder在各种下游任务中显著提升了LLM的性能，并在一般性和任务特定检索器上取得了显著优势。
英文摘要：
LLMs confront inherent limitations in terms of its knowledge, memory, and action. The retrieval augmentation stands as a vital mechanism to address these limitations, which brings in useful information from external sources to augment the LLM. However, existing retrieval methods encounter two pressing issues. On one hand, the general retrievers are not properly optimized for retrieval augmentation hence exhibit limited effectiveness; on the other hand, the task-specific retrievers excel in the targeted retrieval augmentation scenario, while lack the versatility to handle diverse scenarios. In this work, we propose \textbf{LLM-Embedder} for the unified support of diverse retrieval augmentation scenarios. Our method presents three technical contributions. Firstly, we introduce a new \textit{reward formulation}, namely {rank-aware reward}. It exploits the ranking position of the desired output among $N$ sampled outputs from the LLM, which leads to fine-grained and robust computation of reward from the LLM's feedback. Secondly, we design a novel \textit{distillation objective}, called graded distillation. It incorporates both the absolute value and the relative order of the reward for more sufficient utilization of the LLM's feedback. Thirdly, we systematically optimize the \textit{multi-task learning}, which effectively unifies the multiple retrieval functionalities into one model. In our experiment, LLM-Embedder notably improves the LLM's performances in various downstream tasks, and outperforms both general and task-specific retrievers with a substantial advantage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
