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HFEPX Hub

CS.HC + Automatic Metrics Papers

Updated from current HFEPX corpus (Feb 27, 2026). 28 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: Retrieval. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 26, 2026.

Papers: 28 Last published: Feb 26, 2026 Global RSS Tag RSS
Cs.HCAutomatic Metrics

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 28 papers for CS.HC + Automatic Metrics Papers. Dominant protocol signals include automatic metrics, with frequent benchmark focus on Retrieval and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 3.6% of hub papers (1/28); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 17.9% of hub papers (5/28); compare with a secondary metric before ranking methods.
  • cost is reported in 10.7% of hub papers (3/28); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Tighten coverage on Papers with explicit human feedback. Coverage is usable but incomplete (32.1% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (0% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (3.6% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (35.7% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (21.4% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (10.7% vs 35% target).

Papers with explicit human feedback

Coverage is usable but incomplete (32.1% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (0% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (3.6% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (35.7% vs 35% target).

Papers with known rater population

Coverage is usable but incomplete (21.4% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (10.7% vs 35% target).

Suggested Reading Order

  1. 1. LLM Novice Uplift on Dual-Use, In Silico Biology Tasks

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. Exploring Human-Machine Coexistence in Symmetrical Reality

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. Evaluating the Usage of African-American Vernacular English in Large Language Models

    Adds automatic metrics for broader coverage within this hub.

  5. 5. SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

    Adds automatic metrics with expert verification for broader coverage within this hub.

  6. 6. "Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems

    Adds automatic metrics with expert verification for broader coverage within this hub.

  7. 7. An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems

    Adds automatic metrics with expert verification for broader coverage within this hub.

  8. 8. PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&A

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (21.4% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

Benchmark Brief

Retrieval

Coverage: 1 papers (3.6%)

1 papers (3.6%) mention Retrieval.

Examples: Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition

Metric Brief

accuracy

Coverage: 5 papers (17.9%)

5 papers (17.9%) mention accuracy.

Examples: Modeling Distinct Human Interaction in Web Agents , What Do LLMs Associate with Your Name? A Human-Centered Black-Box Audit of Personal Data , How much does context affect the accuracy of AI health advice?

Metric Brief

cost

Coverage: 3 papers (10.7%)

3 papers (10.7%) mention cost.

Examples: When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models , SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery , A Scalable Framework for Evaluating Health Language Models

Metric Brief

agreement

Coverage: 2 papers (7.1%)

2 papers (7.1%) mention agreement.

Examples: Through the Judge's Eyes: Inferred Thinking Traces Improve Reliability of LLM Raters , A Scalable Framework for Evaluating Health Language Models

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: LLM Novice Uplift on Dual-Use, In Silico Biology Tasks , When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models , Exploring Human-Machine Coexistence in Symmetrical Reality

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: LLM Novice Uplift on Dual-Use, In Silico Biology Tasks , When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models , Exploring Human-Machine Coexistence in Symmetrical Reality

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: LLM Novice Uplift on Dual-Use, In Silico Biology Tasks , When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models , Exploring Human-Machine Coexistence in Symmetrical Reality

Top Papers

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