Pith Number

pith:C75OY7FN

pith:2024:C75OY7FNMDYVQFC4XNHJNN525T

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MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries

Yixuan Tang, Yi Yang

Existing RAG systems are inadequate for answering multi-hop queries that require retrieving and reasoning over multiple pieces of evidence.

arxiv:2401.15391 v1 · 2024-01-27 · cs.CL

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Claims

C1strongest claim

existing RAG systems are inadequate in answering multi-hop queries, which require retrieving and reasoning over multiple pieces of supporting evidence.

C2weakest assumption

The multi-hop queries constructed from the English news article dataset accurately reflect the distribution and difficulty of real-world multi-hop queries that users would ask RAG systems.

C3one line summary

MultiHop-RAG is a new benchmark dataset demonstrating that existing retrieval-augmented generation systems perform poorly on multi-hop queries requiring retrieval and reasoning over multiple evidence pieces.

References

296 extracted · 296 resolved · 6 Pith anchors

[1] Anthropic. 2023. Claude 2.1 ( May version). https://api.anthropic.com/v1/messages. Claude 2.1 2023

[2] Akari Asai, Sewon Min, Zexuan Zhong, and Danqi Chen. 2023. Retrieval-based language models and applications. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics 2023

[3] Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aur 2022

[4] Harrison Chase. 2022. https://github.com/langchain-ai/langchain LangChain 2022

[5] Benchmarking large language models in retrieval-augmented generation.arXiv preprint arXiv:2309.01431 2023

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Cited by

25 papers in Pith

PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling

ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation

In-depth Analysis of Graph-based RAG in a Unified Framework

Toward Robust GraphRAG: Mitigating Retrieval Drift and Hallucination from Imperfect Knowledge Graphs

MeMo: Memory as a Model

Receipt and verification

First computed	2026-05-17T23:38:52.399353Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

17faec7cad60f158145cbb4e96b7baecd22c2be3149c940e2e25447e55f12948

Aliases

arxiv: 2401.15391 · arxiv_version: 2401.15391v1 · doi: 10.48550/arxiv.2401.15391 · pith_short_12: C75OY7FNMDYV · pith_short_16: C75OY7FNMDYVQFC4 · pith_short_8: C75OY7FN

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/C75OY7FNMDYVQFC4XNHJNN525T \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 17faec7cad60f158145cbb4e96b7baecd22c2be3149c940e2e25447e55f12948

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2024-01-27T11:41:48Z",
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