Pith. sign in

REVIEW 4 cited by

GenDec: A robust generative Question-decomposition method for Multi-hop reasoning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.11166 v1 pith:FPM2DSDA submitted 2024-02-17 cs.CL

Jian Wu , Linyi Yang , Yuliang Ji , Wenhao Huang , Börje F. Karlsson , Manabu Okumura This is my paper · ORCID
classification cs.CL
keywords reasoninggendecllmsmulti-hopabilityansweransweringmethod
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts. However, Existing large language models'(LLMs) reasoning ability in multi-hop question answering remains exploration, which is inadequate in answering multi-hop questions. Moreover, it is unclear whether LLMs follow a desired reasoning chain to reach the right final answer. In this paper, we propose a \textbf{gen}erative question \textbf{dec}omposition method (GenDec) from the perspective of explainable QA by generating independent and complete sub-questions based on incorporating additional extracted evidence for enhancing LLMs' reasoning ability in RAG. To demonstrate the impact, generalization, and robustness of Gendec, we conduct two experiments, the first is combining GenDec with small QA systems on paragraph retrieval and QA tasks. We secondly examine the reasoning capabilities of various state-of-the-art LLMs including GPT-4 and GPT-3.5 combined with GenDec. We experiment on the HotpotQA, 2WikihopMultiHopQA, MuSiQue, and PokeMQA datasets.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know"

    cs.CL 2026-02 conditional novelty 6.0 of 10

    Disagreement between direct and decomposed prompting is a training-free error signal that outperforms standard uncertainty baselines for closed-book QA abstention.

  2. POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval

    cs.IR 2025-05 conditional novelty 6.0 of 10

    POQD uses an LLM-based optimizer to search the query-decomposition prompt together with RAG generator training, improving multi-vector retrieval and QA accuracy over fixed decomposition baselines.

  3. ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering

    cs.CL 2025-05 conditional novelty 5.0 of 10

    A modular, verifier-driven RAG pipeline with iterative re-decomposition outperforms fine-tuned and agentic baselines on four multi-hop QA benchmarks.

  4. Question Decomposition for Retrieval-Augmented Generation

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Splitting multi-hop questions into subquestions and reranking the merged retrieval pool improves RAG evidence coverage and answer accuracy on MultiHop-RAG and HotpotQA.

Pith tools