Learning to Retrieve Iteratively for In-Context Learning

• URL: http://arxiv.org/abs/2406.14739v1
• Date: Thu, 20 Jun 2024 21:07:55 GMT
• Title: Learning to Retrieve Iteratively for In-Context Learning
• Authors: Yunmo Chen, Tongfei Chen, Harsh Jhamtani, Patrick Xia, Richard Shin, Jason Eisner, Benjamin Van Durme,
• Abstract summary: 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.
• Score: 56.40100968649039
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce iterative retrieval, a novel framework that empowers retrievers to make iterative decisions through policy optimization. Finding an optimal portfolio of retrieved items is a combinatorial optimization problem, generally considered NP-hard. This approach provides a learned approximation to such a solution, meeting specific task requirements under a given family of large language models (LLMs). We propose a training procedure based on reinforcement learning, incorporating feedback from LLMs. We instantiate an iterative retriever for composing in-context learning (ICL) exemplars and apply it to various semantic parsing tasks that demand synthesized programs as outputs. By adding only 4M additional parameters for state encoding, we convert an off-the-shelf dense retriever into a stateful iterative retriever, outperforming previous methods in selecting ICL exemplars on semantic parsing datasets such as CalFlow, TreeDST, and MTOP. Additionally, the trained iterative retriever generalizes across different inference LLMs beyond the one used during training.

Related papers

• MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity [30.346398341996476]
  We propose a reinforcement learning-based framework that dynamically selects the most suitable retrieval strategy based on query complexity. Our method achieves new state of the art results on multiple single-hop and multi-hop datasets while reducing retrieval costs.
  arXiv  Detail & Related papers  (2024-12-02T14:55:02Z)
• ICLERB: In-Context Learning Embedding and Reranker Benchmark [45.40331863265474]
  In-Context Learning (ICL) enables Large Language Models to perform new tasks by conditioning on prompts with relevant information. Traditional retrieval methods focus on semantic relevance, treating retrieval as a search problem. We propose reframing retrieval for ICL as a recommendation problem, aiming to select documents that maximize utility in ICL tasks.
  arXiv  Detail & Related papers  (2024-11-28T06:28:45Z)
• 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)
• PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking [0.0]
  PRefLexOR combines preference optimization with concepts from Reinforcement Learning to enable models to self-teach. We focus on applications in biological materials science and demonstrate the method in a variety of case studies.
  arXiv  Detail & Related papers  (2024-10-16T08:46:26Z)
• 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)
• Learning to Ask Conversational Questions by Optimizing Levenshtein Distance [83.53855889592734]
  We introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimize the minimum Levenshtein distance (MLD) through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-30T08:44:19Z)
• Multi-layer Optimizations for End-to-End Data Analytics [71.05611866288196]
  We introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language. We show that a Scala implementation of IFAQ can outperform mlpack, Scikit, and specialization by several orders of magnitude for linear regression and regression tree models over several relational datasets.
  arXiv  Detail & Related papers  (2020-01-10T16:14:44Z)

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.