Bridging the Preference Gap between Retrievers and LLMs

• URL: http://arxiv.org/abs/2401.06954v2
• Date: Tue, 20 Feb 2024 21:11:23 GMT
• Title: Bridging the Preference Gap between Retrievers and LLMs
• Authors: Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei and Michael Bendersky
• Abstract summary: Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks. Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information. However, the relationship between retrievers and LLMs in a RAG is still under-investigated.
• Score: 32.342245642909404
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM. However, the relationship between retrievers and LLMs in a RAG is still under-investigated. Most existing work treats the retriever and the LLM as independent components and leaves a gap between retrieving human-"friendly" information and assembling a LLM-"friendly" context. In this work, we examine a novel bridge mechanism. We validate the ranking and selection assumptions of retrievers in the context of RAG and propose a framework that chains together supervised and reinforcement learning to train a bridge model that optimizes the connection between the retriever and the LLM. Empirical results demonstrate the effectiveness of our method in both question-answering and personalized generation tasks.

Related papers

• Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation [43.630437906898635]
  We propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information.
  arXiv  Detail & Related papers  (2024-11-11T14:25:37Z)
• Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation [20.420575358183687]
  Retrieval-Augmented Generation (RAG) has proven to be an effective method for mitigating hallucination issues inherent in large language models (LLMs) Previous approaches typically train retrievers based on semantic similarity, lacking optimization for RAG. We propose a novel framework, FiGRet, which leverages the language capabilities of LLMs to construct examples from a more granular, information-centric perspective.
  arXiv  Detail & Related papers  (2024-11-06T14:42:39Z)
• MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents [28.419007116364668]
  MLLM agents demonstrate potential for complex embodied tasks by retrieving multimodal task-relevant trajectory data. Current retrieval methods primarily focus on surface-level similarities of textual or visual cues in trajectories, neglecting their effectiveness for the specific task at hand. We propose a novel method, MLLM as ReTriever (MART), which enhances the performance of embodied agents by utilizing interaction data to fine-tune an MLLM retriever.
  arXiv  Detail & Related papers  (2024-10-04T14:10:39Z)
• Learning to Retrieve Iteratively for In-Context Learning [56.40100968649039]
  iterative retrieval is a novel framework that empowers retrievers to make iterative decisions through policy optimization. We instantiate an iterative retriever for composing in-context learning exemplars and apply it to various semantic parsing tasks. By adding only 4M additional parameters for state encoding, we convert an off-the-shelf dense retriever into a stateful iterative retriever.
  arXiv  Detail & Related papers  (2024-06-20T21:07:55Z)
• R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation [11.890598082534577]
  Retrieval augmented generation (RAG) has been applied in many scenarios to augment large language models (LLMs) with external documents provided by retrievers. This paper proposes R$2$AG, a novel enhanced RAG framework that incorporates Retrieval information into Retrieval Augmented Generation.
  arXiv  Detail & Related papers  (2024-06-19T06:19:48Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)
• Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction [11.535892987373947]
  Relation extraction (RE) aims to identify relations between entities mentioned in texts. Large language models (LLMs) have demonstrated impressive in-context learning abilities in various tasks. LLMs suffer from poor performances compared to most supervised fine-tuned RE methods.
  arXiv  Detail & Related papers  (2024-04-27T07:12:52Z)
• Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models [79.32652077838046]
  We introduce Parrot, a solution aiming to enhance multi-turn instruction following for large language models (LLMs) First, we introduce an efficient but effective method for collecting multi-turn instructions that feature human-like queries, such as anaphora and ellipsis. Second, we propose a context-aware preference optimization strategy to further enhance LLMs for complex queries in multi-turn interaction.
  arXiv  Detail & Related papers  (2023-10-11T08:36:43Z)
• LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying [71.86163159193327]
  Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. We introduce LaGR, which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent.
  arXiv  Detail & Related papers  (2023-08-21T02:07:35Z)
• Query Rewriting for Retrieval-Augmented Large Language Models [139.242907155883]
  Large Language Models (LLMs) play powerful, black-box readers in the retrieve-then-read pipeline. This work introduces a new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs.
  arXiv  Detail & Related papers  (2023-05-23T17:27:50Z)
• Large Language Models are Strong Zero-Shot Retriever [89.16756291653371]
  We propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retriever (LameR), is built upon no other neural models but an LLM.
  arXiv  Detail & Related papers  (2023-04-27T14:45:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.