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Augmenting Research Ideation with Data: An Empirical Investigation in Social Science

Xiao Liu, Xinyi Dong, Xinyang Gao, Yansong Feng, Xun Pang · May 27, 2025 · Citations: 0

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Data freshness

Extraction: Fresh

Check recency before relying on this page for active eval decisions. Use stale pages as context and verify against current hub results.

Metadata refreshed

Feb 28, 2026, 8:59 PM

Recent

Extraction refreshed

Mar 8, 2026, 7:01 AM

Fresh

Extraction source

Runtime deterministic fallback

Confidence 0.15

Abstract

Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness. In this paper, we investigate whether augmenting LLMs with relevant data during the ideation process can improve idea quality. Our framework integrates data at two stages: (1) incorporating metadata during idea generation to guide models toward more feasible concepts, and (2) introducing an automated preliminary validation step during idea selection to assess the empirical plausibility of hypotheses within ideas. We evaluate our approach in the social science domain, with a specific focus on climate negotiation topics. Expert evaluation shows that metadata improves the feasibility of generated ideas by 20%, while automated validation improves the overall quality of selected ideas by 7%. Beyond assessing the quality of LLM-generated ideas, we conduct a human study to examine whether these ideas, augmented with related data and preliminary validation, can inspire researchers in their own ideation. Participants report that the LLM-generated ideas and validation are highly useful, and the ideas they propose with such support are proven to be of higher quality than those proposed without assistance. Our findings highlight the potential of data-augmented research ideation and underscore the practical value of LLM-assisted ideation in real-world academic settings.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Extraction flags indicate low-signal or possible false-positive protocol mapping.
  • Extraction confidence is 0.15 (below strong-reference threshold).
  • No explicit evaluation mode was extracted from available metadata.
  • No benchmark/dataset or metric anchors were extracted.
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HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit Score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

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Field Provenance & Confidence

Each key protocol field shows extraction state, confidence band, and data source so you can decide whether to trust it directly or validate from full text.

Human Feedback Types

missing

None explicit

Confidence: Low Source: Runtime deterministic fallback missing

No explicit feedback protocol extracted.

Evidence snippet: Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness.

Evaluation Modes

missing

None explicit

Confidence: Low Source: Runtime deterministic fallback missing

Validate eval design from full paper text.

Evidence snippet: Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness.

Quality Controls

missing

Not reported

Confidence: Low Source: Runtime deterministic fallback missing

No explicit QC controls found.

Evidence snippet: Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Source: Runtime deterministic fallback missing

No benchmark anchors detected.

Evidence snippet: Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness.

Reported Metrics

missing

Not extracted

Confidence: Low Source: Runtime deterministic fallback missing

No metric anchors detected.

Evidence snippet: Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness.

Rater Population

partial

Domain Experts

Confidence: Low Source: Runtime deterministic fallback evidenced

Helpful for staffing comparability.

Evidence snippet: Expert evaluation shows that metadata improves the feasibility of generated ideas by 20%, while automated validation improves the overall quality of selected ideas by 7%.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Unit of annotation: Unknown
  • Expertise required: General
  • Extraction source: Runtime deterministic fallback

Evaluation Lens

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Confidence: 0.15
  • Flags: low_signal, possible_false_positive, runtime_fallback_extraction

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Deterministic synthesis

We evaluate our approach in the social science domain, with a specific focus on climate negotiation topics. HFEPX protocol signal is limited in abstract-level metadata, so treat it as adjacent context. Updated from current HFEPX corpus.

Generated Mar 8, 2026, 7:01 AM · Grounded in abstract + metadata only

Key Takeaways

  • We evaluate our approach in the social science domain, with a specific focus on climate negotiation topics.
  • Expert evaluation shows that metadata improves the feasibility of generated ideas by 20%, while automated validation improves the overall quality of selected ideas by 7%.

Researcher Actions

  • Treat this as method context, then pivot to protocol-specific HFEPX hubs.
  • Identify benchmark choices from full text before operationalizing conclusions.
  • Verify metric definitions before comparing against your eval pipeline.

Caveats

  • Generated from title, abstract, and extracted metadata only; full-paper implementation details are not parsed.
  • Low-signal flag detected: protocol relevance may be indirect.

Research Summary

Contribution Summary

  • We evaluate our approach in the social science domain, with a specific focus on climate negotiation topics.
  • Expert evaluation shows that metadata improves the feasibility of generated ideas by 20%, while automated validation improves the overall quality of selected ideas by 7%.
  • Beyond assessing the quality of LLM-generated ideas, we conduct a human study to examine whether these ideas, augmented with related data and preliminary validation, can inspire researchers in their own ideation.

Why It Matters For Eval

  • Expert evaluation shows that metadata improves the feasibility of generated ideas by 20%, while automated validation improves the overall quality of selected ideas by 7%.
  • Beyond assessing the quality of LLM-generated ideas, we conduct a human study to examine whether these ideas, augmented with related data and preliminary validation, can inspire researchers in their own ideation.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Category-Adjacent Papers (Broader Context)

These papers are nearby in arXiv category and useful for broader context, but not necessarily protocol-matched to this paper.

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