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HFEPX · Eval paper review

XISM: an eXploratory and Interactive Graph Tool to Visualize and Evaluate Semantic Map Models

Zhu Liu, Zhen Hu, Lei Dai, Yu Xuan +1 more

Published

Jul 5, 2025

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Feb 28, 2026

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Tags

Coding Demonstrations

Should you rely on this paper?

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

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

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

If you are doing eval pipeline work, start here

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

Abstract

Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology. However, existing construction methods either depend on labor-intensive expert reasoning or on fully automated systems lacking expert involvement, creating a tension between scalability and interpretability. We introduce \textbf{XISM}, an interactive system that combines data-driven inference with expert knowledge. XISM generates candidate maps via a top-down procedure and allows users to iteratively refine edges in a visual interface, with real-time metric feedback. Experiments in three semantic domains and expert interviews show that XISM improves linguistic decision transparency and controllability in semantic-map construction while maintaining computational efficiency. XISM provides a collaborative approach for scalable and interpretable semantic-map building. The system\footnote{https://app.xism2025.xin/} , source code\footnote{https://github.com/hank317/XISM} , and demonstration video\footnote{https://youtu.be/m5laLhGn6Ys} are publicly available.

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

partial

Demonstrations

Directly usable for protocol triage.

"Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"However, existing construction methods either depend on labor-intensive expert reasoning or on fully automated systems lacking expert involvement, creating a tension between scalability and interpretability."

Benchmarks and datasets

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

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Demonstrations
Rater population
Domain Experts
Expertise required
Coding
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology.

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

Key takeaways

  • Semantic map models visualize systematic relations among semantic functions through graph structures and are widely used in linguistic typology.
  • However, existing construction methods either depend on labor-intensive expert reasoning or on fully automated systems lacking expert involvement, creating a tension between scalability and interpretability.
  • We introduce \textbf{XISM}, an interactive system that combines data-driven inference with expert knowledge.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) 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

Expert verification evaluation Human demonstrations evaluation

Contribution summary

  • We introduce XISM, an interactive system that combines data-driven inference with expert knowledge.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Demonstrations

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • 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

    No metric terms extracted.