Learning to Filter Context for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2311.08377v1
• Date: Tue, 14 Nov 2023 18:41:54 GMT
• Title: Learning to Filter Context for Retrieval-Augmented Generation
• Authors: Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, Graham Neubig
• Abstract summary: Generation models are required to generate outputs given partially or entirely irrelevant passages. FILCO identifies useful context based on lexical and information-theoretic approaches. It trains context filtering models that can filter retrieved contexts at test time.
• Score: 75.18946584853316
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: On-the-fly retrieval of relevant knowledge has proven an essential element of reliable systems for tasks such as open-domain question answering and fact verification. However, because retrieval systems are not perfect, generation models are required to generate outputs given partially or entirely irrelevant passages. This can cause over- or under-reliance on context, and result in problems in the generated output such as hallucinations. To alleviate these problems, we propose FILCO, a method that improves the quality of the context provided to the generator by (1) identifying useful context based on lexical and information-theoretic approaches, and (2) training context filtering models that can filter retrieved contexts at test time. We experiment on six knowledge-intensive tasks with FLAN-T5 and LLaMa2, and demonstrate that our method outperforms existing approaches on extractive question answering (QA), complex multi-hop and long-form QA, fact verification, and dialog generation tasks. FILCO effectively improves the quality of context, whether or not it supports the canonical output.

Related papers

• Likelihood as a Performance Gauge for Retrieval-Augmented Generation [78.28197013467157]
  We show that likelihoods serve as an effective gauge for language model performance. We propose two methods that use question likelihood as a gauge for selecting and constructing prompts that lead to better performance.
  arXiv  Detail & Related papers  (2024-11-12T13:14:09Z)
• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• Think-then-Act: A Dual-Angle Evaluated Retrieval-Augmented Generation [3.2134014920850364]
  Large language models (LLMs) often face challenges such as temporal misalignment and generating hallucinatory content. We propose a dual-angle evaluated retrieval-augmented generation framework textitThink-then-Act'
  arXiv  Detail & Related papers  (2024-06-18T20:51:34Z)
• Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding [9.2433070542025]
  Large language models (LLMs) tend to inadequately integrate input context during text generation. We introduce a novel approach integrating contrastive decoding with adversarial irrelevant passages as negative samples.
  arXiv  Detail & Related papers  (2024-05-04T20:38:41Z)
• BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering [58.403898834018285]
  BlendFilter is a novel approach that elevates retrieval-augmented Large Language Models by integrating query generation blending with knowledge filtering. We conduct extensive experiments on three open-domain question answering benchmarks, and the findings clearly indicate that our innovative BlendFilter surpasses state-of-the-art baselines significantly.
  arXiv  Detail & Related papers  (2024-02-16T23:28:02Z)
• Grounding Language Model with Chunking-Free In-Context Retrieval [27.316315081648572]
  This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems.
  arXiv  Detail & Related papers  (2024-02-15T07:22:04Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Making Retrieval-Augmented Language Models Robust to Irrelevant Context [55.564789967211844]
  An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant. Recent work has shown that retrieval augmentation can sometimes have a negative effect on performance.
  arXiv  Detail & Related papers  (2023-10-02T18:52:35Z)

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.