TDR: Task-Decoupled Retrieval with Fine-Grained LLM Feedback for In-Context Learning
This addresses the challenge of improving example retrieval for in-context learning in LLMs, which is incremental as it builds on prior retrieval methods.
The paper tackles the problem of retrieving high-quality examples for in-context learning in LLMs by proposing TDR, a framework that decouples examples from different tasks and uses fine-grained LLM feedback to train the retriever, achieving state-of-the-art performance across 30 NLP tasks.
In-context learning (ICL) has become a classic approach for enabling LLMs to handle various tasks based on a few input-output examples. The effectiveness of ICL heavily relies on the quality of these examples, and previous works which focused on enhancing example retrieval capabilities have achieved impressive performances. However, two challenges remain in retrieving high-quality examples: (1) Difficulty in distinguishing cross-task data distributions, (2) Difficulty in making the fine-grained connection between retriever output and feedback from LLMs. In this paper, we propose a novel framework called TDR. TDR decouples the ICL examples from different tasks, which enables the retrieval module to retrieve examples specific to the target task within a multi-task dataset. Furthermore, TDR models fine-grained feedback from LLMs to supervise and guide the training of the retrieval module, which helps to retrieve high-quality examples. We conducted extensive experiments on a suite of 30 NLP tasks, the results demonstrate that TDR consistently improved results across all datasets and achieves state-of-the-art performance. Meanwhile, our approach is a plug-and-play method, which can be easily combined with various LLMs to improve example retrieval abilities for ICL. The code is available at https://github.com/Nnn-s/TDR.