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TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases

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arxiv 2210.12925 v1 pith:QC6Q3PY5 submitted 2022-10-24 cs.CL cs.AI

Yiheng Shu , Zhiwei Yu , Yuhan Li , Börje F. Karlsson , Tingting Ma , Yuzhong Qu , Chin-Yew Lin This is my paper · ORCID
classification cs.CLcs.AI
keywords tiaraeffectivenessformskbqaknowledgelogicalmulti-grainedplms
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Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios. However, KBQA remains challenging, especially regarding coverage and generalization settings. This is due to two main factors: i) understanding the semantics of both questions and relevant knowledge from the KB; ii) generating executable logical forms with both semantic and syntactic correctness. In this paper, we present a new KBQA model, TIARA, which addresses those issues by applying multi-grained retrieval to help the PLM focus on the most relevant KB contexts, viz., entities, exemplary logical forms, and schema items. Moreover, constrained decoding is used to control the output space and reduce generation errors. Experiments over important benchmarks demonstrate the effectiveness of our approach. TIARA outperforms previous SOTA, including those using PLMs or oracle entity annotations, by at least 4.1 and 1.1 F1 points on GrailQA and WebQuestionsSP, respectively.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering

    cs.CL 2025-07 reject novelty 6.0 of 10

    BYOKG-RAG combines LLM-generated entities, paths, queries, and candidate answers with multiple graph retrieval tools to answer questions over custom knowledge graphs without training data.

  2. Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering

    cs.CL 2025-05 conditional novelty 6.0 of 10

    RTSoG combines question decomposition, LLM-reward-guided Monte Carlo Tree Search with a self-critic stop signal, and weighted path stacking to achieve new state-of-the-art KGQA accuracy, though without code or error bars.

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