DAIS: Dependency-Aware Intermediate QA Supervision for Complex Reasoning

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Yu Wang, Ming Fan, Xicheng Zhang, Zhiyong Li, Zhihu Wang, Caiyue Xu, Dahai Hu, Ting Liu
• Venue: arXiv: cs.CL
• Published: 2026-07-21
• Collected: 2026-07-22
• URL: http://arxiv.org/abs/2607.19088v1
• PDF: https://arxiv.org/pdf/2607.19088v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces Dependency-Aware Intermediate QA Supervision (DAIS), a framework that improves the accuracy of answering complex questions by providing intermediate rationales and conditioning local answers on previous states needed for a decision. The framework was tested across several question answering datasets and outperformed other baselines, particularly in policy-compliance benchmarks.

What the Document Says

• The paper presents Dependency-Aware Intermediate QA Supervision (DAIS).
• DAIS converts teacher rationales into stage-level QA records.
• Each intermediate record predicts a local answer conditioned on the previous states needed for that decision.
• The final-answer record keeps the original task format.
• Evaluation uses only the original input and optional context.
• DAIS was tested across GDPR, AIACT, MedQA, FOLIO, and multiple Qwen backbones.
• DAIS improves average final-answer accuracy over answer-only, flat chain-of-thought, and independent-QA baselines.
• On policy-compliance benchmarks, DAIS achieves a largest gain of 5.6% and an average gain of 4.2% over the strongest non-DAIS baseline.
• Controlled ablations show that valid previous-state conditioning contributes beyond longer targets or additional intermediate text.

Key Concepts Mentioned

• Dependency-Aware Intermediate QA Supervision (DAIS)
• Chain-of-thought supervision
• QA records
• local answers
• previous states
• evaluation
• GDPR
• AIACT
• MedQA
• FOLIO
• Qwen backbones
• policy-compliance benchmarks

Claims Made by the Document

• DAIS improves final-answer accuracy over other baselines.
• Dependency-conditioned intermediate QA is a useful auxiliary supervision signal.

Evidence Used

• Improved average final-answer accuracy in experiments.
• Results from controlled ablations.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Chain-of-thought (CoT) supervision exposes intermediate rationales, but flat rationale targets usually optimize a single reasoning sequence and provide limited supervision on how local conclusions should support later decisions. We introduce Dependency-Aware Intermediate QA Supervision (DAIS), a training-time framework that converts filtered teacher rationales into stage-level QA records. Each intermediate record predicts a local answer conditioned on the previous states needed for that decision, while the final-answer record keeps the original task format; evaluation therefore uses only the original input and optional context. Across GDPR, AIACT, MedQA, and FOLIO with multiple Qwen backbones, DAIS improves average final-answer accuracy over answer-only, flat chain-of-thought, and independent-QA baselines. On policy-compliance benchmarks, it achieves a largest gain of 5.6% and an average gain of 4.2% over the strongest non-DAIS baseline. Controlled ablations show that valid previous-state conditioning contributes beyond longer targets or additional intermediate text, supporting dependency-conditioned intermediate QA as a lightweight auxiliary supervision signal for standard final-answer inference.