mlsanityData to 7 Oct 2026

DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text

arXiv 2310.20170 ↗First posted 31 Oct 2023cs.CLcited by 5

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when solely relying on their internal knowledge, especially when answering questions that require less commonly known information. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge. Nonetheless, recent approaches have primarily emphasized retrieval from unstructured text corpora, owing to its seamless integration into prompts. When using structured data such as knowledge graphs, most methods simplify it into natural text, neglecting the underlying structures. Moreover, a significant gap in the current landscape is the absence of a realistic benchmark for evaluating the effectiveness of grounding LLMs on heterogeneous knowledge sources (e.g., knowledge base and text). To fill this gap, we have curated a comprehensive dataset that poses two unique challenges: (1) Two-hop multi-source questions that require retrieving information from both open-domain structured and unstructured knowledge sources; retrieving information from structured knowledge sources is a critical component in correctly answering the questions. (2) The generation of symbolic queries (e.g., SPARQL for Wikidata) is a key requirement, which adds another layer of challenge. Our dataset is created using a combination of automatic generation through predefined reasoning chains and human annotation. We also introduce a novel approach that leverages multiple retrieval tools, including text passage retrieval and symbolic language-assisted retrieval. Our model outperforms previous approaches by a significant margin, demonstrating its effectiveness in addressing the above-mentioned reasoning challenges.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 18 of its 59 references that are in the map. First posted on arXiv 31 Oct 2023.

Builds on

18 of 59 references.

References · 18

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  4. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  5. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  6. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  7. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  8. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  9. 2018The Web as a Knowledge-base for Answering Complex Questionscited by 409
  10. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  11. 2022Teaching language models to support answers with verified quotescited by 290
  12. 2023Hypothesis Search: Inductive Reasoning with Language Modelscited by 119
6 more
  1. 2023Attention Sorting Combats Recency Bias In Long Context Language Modelscited by 89
  2. 2023ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Modelscited by 43
  3. 2022Language Models as Inductive Reasonerscited by 32
  4. 2023CompMix: A Benchmark for Heterogeneous Question Answeringcited by 12
  5. 2023XGen-7B Technical Reportcited by 11
  6. 2023HPE:Answering Complex Questions over Text by Hybrid Question Parsing and Executioncited by 5

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  2. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  3. 2025AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corporacited by 23
  4. 2024Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Modelscited by 7
  5. 2025DAMR: Efficient and Adaptive Context-Aware Knowledge Graph Question Answering with LLM-Guided MCTScited by 6

Similar, not cited

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  1. 2025Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs
  2. 2026How Much Reasoning Do Retrieval-Augmented Models Add beyond LLMs? A Benchmarking Framework for Multi-Hop Inference over Hybrid Knowledge
  3. 2026Beyond Questions: Evaluating LLM's Knowledge Expression
  4. 2024Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
  5. 2024Hierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization
  6. 2024MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
  7. 2025RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
  8. 2026A Human-in-the-Loop, LLM-Centered Architecture for Knowledge-Graph Question Answering