Papers with WebQuestions
ReflectiveRAG: Rethinking Adaptivity in Retrieval-Augmented Generation (2026.eacl-industry)
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| Challenge: | Existing methods for retrieval-augmented generation (RAG) fail to assess evidence sufficiency, detect subtle mismatches or reduce redundancy. |
| Approach: | They propose a lightweight yet reasoning-driven architecture that enhances factual grounding . ReflectiveRAG employs self-reflective retrieval and Contrastive noise removal . |
| Outcome: | a new architecture improves factual grounding by using self-reflective retrieval and Contrastive noise removal. |
NEST: Nested Evidence Survival for Retrieval (2026.acl-industry)
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| Challenge: | Existing approaches to retrieval-augmented generation (RAG) rely on rigid heuristics or computational overhead. |
| Approach: | They propose a lightweight, training-free RAG framework that separates recall amplification from precision selection. |
| Outcome: | Evaluated on WebQuestions, HotpotQA and internalQA benchmarks, NEST outperforms strong adaptive RAG baselines. |
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2022.findings-naacl)
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Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Scott Yih
| Challenge: | a recent study aims to answer factual questions using a structured knowledge base (KBQA). |
| Approach: | They propose a unifying approach that homogenizes all knowledge sources by reducing them to text . they demonstrate that UniK-QA is a simple and yet effective way to combine heterogeneous sources of knowledge. |
| Outcome: | The proposed approach improves state-of-the-art results on knowledge-base QA tasks by 11 points compared to graph-based methods. |
A State-transition Framework to Answer Complex Questions over Knowledge Base (D18-1)
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| Challenge: | Existing methods for complex question answering have some limitations . existing methods employ predefined patterns or templates to understand complex questions. |
| Approach: | They propose a state transition-based approach to translate a natural language question to a semantic query graph. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on several benchmarks with two knowledge bases. |
Relation-Guided Pre-Training for Open-Domain Question Answering (2021.findings-emnlp)
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| Challenge: | Existing QA datasets are imbalanced in some types of relations, which hurts generalization performance over long-tail questions. |
| Approach: | They propose a relation-guided pre-training framework to infer latent relations from a QA dataset . they then propose RGPT-QA to conduct extractive QA to get the target answer entity . |
| Outcome: | The proposed framework improves Exact Match accuracy on natural questions, TriviaQA, and WebQuestions. |
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata (2023.emnlp-main)
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| Challenge: | Large language models can answer many questions correctly, but can also hallucinate and give wrong answers. |
| Approach: | They propose a question-answering benchmark for Wikidata that uses SPARQL to ground large language models. |
| Outcome: | The proposed method outperforms the state-of-the-art for QALD-7 by 3.6% in F1 score. |
LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing KBQG models focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph. |
| Approach: | They propose a controlled generation framework for Question Generation over Knowledge Bases that generates questions with out-of-vocabulary (OOV) predicates. |
| Outcome: | The proposed framework outperforms existing methods significantly on three widely-used benchmark datasets SimpleQuestion, PathQuestions, and WebQuestIONS. |
The Price of Format: Diversity Collapse in LLMs (2025.findings-emnlp)
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| Challenge: | Instruction-tuned large language models employ structured templates to enforce format consistency during inference. |
| Approach: | They fine-tune instruction-tuning large language models with structured templates and evaluate their results across three axes: downstream task performance, alignment behavior, and output diversity. |
| Outcome: | The proposed model generates semantically similar outputs even under high temperature sampling and structural tokens in templates significantly constrain the model’s output space. |