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HFEPX · Eval paper review

When Looks Do Not Lie: Discourse Structure Guided In-Context Learning for Faithful Diagram Generation

Evanfiya Logacheva, Arto Hellas, Tsvetomila Mihaylova, Juha Sorva +2 more

Published

Jan 28, 2026

Citations

0

Trust level

Moderate

Usefulness score

67/100 (Medium)

Extraction confidence

70% (Moderate)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Aug 21, 2026

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Tags

General Human Eval Rubric Rating

Should you rely on this paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Use this for comparison and orientation, not as your only source.

Best use

Secondary protocol comparison source

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

No major weakness surfaced.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
67/100
Moderate-confidence candidate

Useful as a secondary reference; validate protocol details against neighboring papers.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination. We introduce a novel method for ICL diagram generation based on Rhetorical Structure Theory, which improves diagram faithfulness to its source text context. We find that ICL performance depends on task distribution and models' reasoning ability, with higher reasoning allowing better quality and performance for an out-of-distribution task. We perform an expert evaluation of 150 generated diagrams and analyze our findings using Bayesian GLMMs. Additionally, we use our evaluation rubric and samples from the data set for automated diagram evaluation, achieving statistically significant agreement with human evaluation.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Rubric Rating

Directly usable for protocol triage.

"GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination."

Evaluation Modes

strong

Human Eval

Includes extracted eval setup.

"GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination."

Quality Controls

missing

Not reported

No explicit QC controls found.

"GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination."

Reported Metrics

strong

Agreement, Faithfulness

Useful for evaluation criteria comparison.

"We introduce a novel method for ICL diagram generation based on Rhetorical Structure Theory, which improves diagram faithfulness to its source text context."

Rater Population

strong

Domain Experts

Helpful for staffing comparability.

"We perform an expert evaluation of 150 generated diagrams and analyze our findings using Bayesian GLMMs."

Benchmarks and datasets

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

Reported metrics

agreementfaithfulness
Human feedback details
Uses human feedback
Yes
Feedback types
Rubric Rating
Rater population
Domain Experts
Unit of annotation
Multi Dim Rubric
Expertise required
General
Evaluation details
Evaluation modes
Human Eval
Agentic eval
None
Quality controls
Not reported
Evidence quality
Moderate
Use this page as
Secondary protocol comparison source

Research brief

Metadata summary

GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination.
  • We introduce a novel method for ICL diagram generation based on Rhetorical Structure Theory, which improves diagram faithfulness to its source text context.
  • We find that ICL performance depends on task distribution and models' reasoning ability, with higher reasoning allowing better quality and performance for an out-of-distribution task.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Human evaluation) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Rubric rating evaluation Expert verification evaluation Agreement / Kappa

Contribution summary

  • We introduce a novel method for ICL diagram generation based on Rhetorical Structure Theory, which improves diagram faithfulness to its source text context.
  • We perform an expert evaluation of 150 generated diagrams and analyze our findings using Bayesian GLMMs.
  • Additionally, we use our evaluation rubric and samples from the data set for automated diagram evaluation, achieving statistically significant agreement with human evaluation.

Why it matters for eval

  • We perform an expert evaluation of 150 generated diagrams and analyze our findings using Bayesian GLMMs.
  • Additionally, we use our evaluation rubric and samples from the data set for automated diagram evaluation, achieving statistically significant agreement with human evaluation.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Rubric Rating

  • Evaluation mode is explicit

    Detected: Human Eval

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: agreement, faithfulness