Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering

• URL: http://arxiv.org/abs/2503.01606v1
• Date: Mon, 03 Mar 2025 14:41:35 GMT
• Title: Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering
• Authors: Zhanghao Hu, Hanqi Yan, Qingling Zhu, Zhenyi Shen, Yulan He, Lin Gui,
• Abstract summary: Large language models have recently pushed open domain question answering to new frontiers.<n> prevailing retriever-reader pipelines often depend on multiple rounds of prompt level instructions.<n>We propose EmbQA, an embedding-level framework that enhances both the retriever and the reader.
• Score: 15.04887070246276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models have recently pushed open domain question answering (ODQA) to new frontiers. However, prevailing retriever-reader pipelines often depend on multiple rounds of prompt level instructions, leading to high computational overhead, instability, and suboptimal retrieval coverage. In this paper, we propose EmbQA, an embedding-level framework that alleviates these shortcomings by enhancing both the retriever and the reader. Specifically, we refine query representations via lightweight linear layers under an unsupervised contrastive learning objective, thereby reordering retrieved passages to highlight those most likely to contain correct answers. Additionally, we introduce an exploratory embedding that broadens the model's latent semantic space to diversify candidate generation and employs an entropy-based selection mechanism to choose the most confident answer automatically. Extensive experiments across three open-source LLMs, three retrieval methods, and four ODQA benchmarks demonstrate that EmbQA substantially outperforms recent baselines in both accuracy and efficiency.

Related papers

• Emulating Retrieval Augmented Generation via Prompt Engineering for Enhanced Long Context Comprehension in LLMs [23.960451986662996]
  This paper proposes a method that emulates Retrieval Augmented Generation (RAG) through specialized prompt engineering and chain-of-thought reasoning.<n>We evaluate our approach on selected tasks from BABILong, which interleaves standard bAbI QA problems with large amounts of distractor text.
  arXiv  Detail & Related papers  (2025-02-18T02:49:40Z)
• W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering [28.79851078451609]
  Large Language Models (LLMs) often struggle to generate factual answers relying solely on their internal (parametric) knowledge. To address this limitation, Retrieval-Augmented Generation (RAG) systems enhance LLMs by retrieving relevant information from external sources. We propose W-RAG by utilizing the ranking capabilities of LLMs to create weakly labeled data for training dense retrievers.
  arXiv  Detail & Related papers  (2024-08-15T22:34:44Z)
• QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs [5.09189220106765]
  We propose a simple yet efficient method called question and passage augmentation (QPaug) via large language models (LLMs) for open-domain question-answering tasks. Experimental results show that QPaug outperforms the previous state-of-the-art and achieves significant performance gain over existing RAG methods.
  arXiv  Detail & Related papers  (2024-06-20T12:59:27Z)
• Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers [66.55612528039894]
  AdaQR is a framework for training query rewriting models with limited rewrite annotations from seed datasets and completely no passage label. A novel approach is proposed to assess retriever's preference for these candidates by the probability of answers conditioned on the conversational query.
  arXiv  Detail & Related papers  (2024-06-16T16:09:05Z)
• SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs [85.54906813106683]
  We propose a simple yet effective framework to enhance open-domain question answering (ODQA) with large language models (LLMs) SuRe helps LLMs predict more accurate answers for a given question, which are well-supported by the summarized retrieval (SuRe) Experimental results on diverse ODQA benchmarks demonstrate the superiority of SuRe, with improvements of up to 4.6% in exact match (EM) and 4.0% in F1 score over standard prompting approaches.
  arXiv  Detail & Related papers  (2024-04-17T01:15:54Z)
• Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering [40.2758450304531]
  Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems. We propose a framework that formulates the ODQA process into three basic steps: query expansion, document selection, and answer generation. We introduce a novel prompt optimization algorithm to refine role-playing prompts and steer LLMs to produce higher-quality evidence and answers.
  arXiv  Detail & Related papers  (2024-03-08T11:09:13Z)
• Strong and Efficient Baselines for Open Domain Conversational Question Answering [2.773656427800412]
  We study the State-of-the-Art (SotA) Dense Passage Retrieval (DPR) retriever and Fusion-in-Decoder (FiD) reader pipeline. We propose and evaluate strong yet simple and efficient baselines, by introducing a fast reranking component between the retriever and the reader. Experiments on two ODConvQA tasks, namely TopiOCQA and OR-QuAC, show that our method improves the SotA results, while reducing reader's latency by 60%.
  arXiv  Detail & Related papers  (2023-10-23T08:48:14Z)
• Allies: Prompting Large Language Model with Beam Search [107.38790111856761]
  In this work, we propose a novel method called ALLIES. Given an input query, ALLIES leverages LLMs to iteratively generate new queries related to the original query. By iteratively refining and expanding the scope of the original query, ALLIES captures and utilizes hidden knowledge that may not be directly through retrieval.
  arXiv  Detail & Related papers  (2023-05-24T06:16:44Z)
• 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)
• Self-Prompting Large Language Models for Zero-Shot Open-Domain QA [67.08732962244301]
  Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models. We propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of Large Language Models.
  arXiv  Detail & Related papers  (2022-12-16T18:23:43Z)
• Generate rather than Retrieve: Large Language Models are Strong Context Generators [74.87021992611672]
  We present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.
  arXiv  Detail & Related papers  (2022-09-21T01:30:59Z)
• Tradeoffs in Sentence Selection Techniques for Open-Domain Question Answering [54.541952928070344]
  We describe two groups of models for sentence selection: QA-based approaches, which run a full-fledged QA system to identify answer candidates, and retrieval-based models, which find parts of each passage specifically related to each question. We show that very lightweight QA models can do well at this task, but retrieval-based models are faster still.
  arXiv  Detail & Related papers  (2020-09-18T23:39:15Z)

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.