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arxiv: 2508.20567 · v2 · submitted 2025-08-28 · 💻 cs.CL

KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling

Yangfan Wang , Jie Liu , Chen Tang , Lian Yan , Jingchi Jiang This is my paper

Pith reviewed 2026-05-18 21:08 UTC · model grok-4.3

classification 💻 cs.CL
keywords multi-hop question generationknowledge composition samplingdata augmentationcontrastive lossHotpotQA2WikiMultihopQAquestion diversity
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The pith

KCS models knowledge composition selection as a sentence-level conditional prediction task to generate more diverse multi-hop questions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes Knowledge Composition Sampling (KCS) to address data sparsity in multi-hop question answering by diversifying generated questions through varied knowledge compositions. It treats selecting relevant sentences as a conditional prediction problem solved with a probabilistic contrastive loss and stochastic decoding during inference. This approach improves selection accuracy by 3.9 percent over baselines and, when used for data augmentation, yields gains on HotpotQA and 2WikiMultihopQA. A sympathetic reader would care because richer training data could help language models avoid spurious patterns when learning complex reasoning.

Core claim

KCS models the knowledge composition selection as a sentence-level conditional prediction task and utilizes a probabilistic contrastive loss to predict the next most relevant piece of knowledge. During inference, a stochastic decoding strategy balances accuracy and diversity, resulting in higher accuracy for composition selection and better performance when the generated questions augment training data for multi-hop QA.

What carries the argument

Knowledge Composition Sampling (KCS), which frames knowledge composition selection as a sentence-level conditional prediction task using a probabilistic contrastive loss to predict the next relevant knowledge piece.

If this is right

  • Generated multi-hop questions gain diversity while preserving relevance through stochastic decoding.
  • Data augmentation with KCS questions improves QA model performance on HotpotQA and 2WikiMultihopQA.
  • Knowledge composition selection accuracy rises by 3.9% compared to prior methods.
  • Language models encounter fewer spurious patterns in the augmented training data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • KCS-style sampling might extend to other knowledge-intensive tasks such as multi-document summarization.
  • Testing the method on additional multi-hop datasets could reveal whether gains hold beyond the two reported benchmarks.
  • Pairing KCS with expression-variation techniques could compound improvements in question quality.

Load-bearing premise

That framing knowledge composition selection as a sentence-level conditional prediction task with probabilistic contrastive loss will produce diverse yet accurate multi-hop questions that improve downstream QA without adding harmful noise or reducing answerability.

What would settle it

Re-running the data augmentation experiments on HotpotQA and 2WikiMultihopQA and finding no improvement or a drop in QA accuracy would show that the generated questions do not deliver the claimed benefits.

read the original abstract

Multi-hop question answering faces substantial challenges due to data sparsity, which increases the likelihood of language models learning spurious patterns. To address this issue, prior research has focused on diversifying question generation through content planning and varied expression. However, these approaches often emphasize generating simple questions and neglect the integration of essential knowledge, such as relevant sentences within documents. This paper introduces the Knowledge Composition Sampling (KCS), an innovative framework designed to expand the diversity of generated multi-hop questions by sampling varied knowledge compositions within a given context. KCS models the knowledge composition selection as a sentence-level conditional prediction task and utilizes a probabilistic contrastive loss to predict the next most relevant piece of knowledge. During inference, we employ a stochastic decoding strategy to effectively balance accuracy and diversity. Compared to competitive baselines, our KCS improves the overall accuracy of knowledge composition selection by 3.9%, and its application for data augmentation yields improvements on HotpotQA and 2WikiMultihopQA datasets. Our code is available at: https://github.com/yangfanww/kcs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes Knowledge Composition Sampling (KCS) to address data sparsity in multi-hop question answering by diversifying generated questions through sampling of varied knowledge compositions from context. KCS frames knowledge composition selection as a sentence-level conditional prediction task trained with a probabilistic contrastive loss and applies stochastic decoding at inference to trade off accuracy and diversity. The central empirical claims are a 3.9% accuracy improvement in knowledge composition selection over competitive baselines and downstream QA gains on HotpotQA and 2WikiMultihopQA when the generated questions are used for data augmentation.

Significance. If the reported gains are robust, KCS could offer a practical way to generate more diverse, knowledge-grounded training data for multi-hop QA models, helping reduce reliance on spurious patterns. The open release of code supports reproducibility and further experimentation in the area.

major comments (2)
  1. Abstract and results sections: the 3.9% accuracy improvement on knowledge composition selection is presented without specification of the exact baselines, number of runs, statistical significance tests, or error bars, which prevents verification of whether the gain is load-bearing for the central claim.
  2. Method description: modeling selection as sequential sentence-level conditional prediction with contrastive loss does not include analysis or ablations demonstrating that locally relevant sentences compose into globally coherent, answerable multi-hop questions rather than locally plausible but single-hop or noisy compositions.
minor comments (1)
  1. The abstract states that code is available at a GitHub link; ensure the repository contains the exact experimental configurations and random seeds used for the reported numbers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each of the major comments in detail below, indicating where revisions will be made to strengthen the paper.

read point-by-point responses

  1. Referee: Abstract and results sections: the 3.9% accuracy improvement on knowledge composition selection is presented without specification of the exact baselines, number of runs, statistical significance tests, or error bars, which prevents verification of whether the gain is load-bearing for the central claim.

    Authors: We agree that providing more details on the experimental setup is important for verifying the reported gains. In the revised version, we will explicitly list the competitive baselines used for the knowledge composition selection task, report results averaged over multiple runs (e.g., 5 runs with different random seeds), include statistical significance tests (such as t-tests with p-values), and add error bars to the relevant tables and figures in the results section. This will allow readers to better assess the robustness of the 3.9% improvement. revision: yes

  2. Referee: Method description: modeling selection as sequential sentence-level conditional prediction with contrastive loss does not include analysis or ablations demonstrating that locally relevant sentences compose into globally coherent, answerable multi-hop questions rather than locally plausible but single-hop or noisy compositions.

    Authors: Thank you for highlighting this aspect. Our design of the probabilistic contrastive loss aims to promote compositions that are globally useful by contrasting against negative samples that may be locally relevant but not leading to answerable questions. However, we recognize that direct ablations and analysis on coherence would enhance the paper. In the revision, we will add an analysis subsection with qualitative examples of selected knowledge compositions, showing their multi-hop nature, and include an ablation study comparing the full model to a version without the contrastive loss to demonstrate its role in ensuring global coherence. The downstream improvements in QA performance on HotpotQA and 2WikiMultihopQA serve as supporting evidence that the generated questions are effective for multi-hop reasoning. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework with independent experimental validation

full rationale

The paper defines KCS as a sentence-level conditional prediction model trained with probabilistic contrastive loss and stochastic decoding, then reports measured accuracy gains (3.9%) and downstream QA improvements on HotpotQA and 2WikiMultihopQA. These are standard empirical results obtained by running the defined procedure on held-out data; no equation reduces a claimed prediction to a fitted parameter by construction, and no load-bearing premise rests on self-citation chains or imported uniqueness theorems. The derivation chain is therefore self-contained and externally falsifiable.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard assumptions in neural sequence modeling and contrastive learning; no explicit free parameters or invented entities are detailed in the abstract, though the contrastive loss and stochastic decoding likely involve tunable hyperparameters typical of such models.

axioms (1)
  • domain assumption A probabilistic contrastive loss can effectively predict the next most relevant piece of knowledge in a sentence-level conditional setup.
    Invoked in the description of how KCS models knowledge composition selection.

pith-pipeline@v0.9.0 · 5718 in / 1324 out tokens · 29285 ms · 2026-05-18T21:08:34.211692+00:00 · methodology

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