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HFEPX Hub

Automatic Metrics + Medicine Papers

Updated from current HFEPX corpus (Feb 27, 2026). 69 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. 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: 69 Last published: Feb 26, 2026 Global RSS Tag RSS
Automatic MetricsMedicine

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 69 papers for Automatic Metrics + Medicine Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, Banglasummeval 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 13% of hub papers (9/69); use this cohort for benchmark-matched comparisons.
  • Banglasummeval appears in 1.4% of hub papers (1/69); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 39.1% of hub papers (27/69); compare with a secondary metric before ranking methods.
  • cost is reported in 13% of hub papers (9/69); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (15.9% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (5.8% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (15.9% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (62.3% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (29% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (10.1% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (15.9% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (5.8% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (15.9% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (62.3% vs 35% target).

Papers with known rater population

Coverage is usable but incomplete (29% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (10.1% vs 35% target).

Suggested Reading Order

  1. 1. An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

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

  2. 2. Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?

    High citation traction makes this a useful baseline for method and protocol context.

  3. 3. Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department

    High citation traction makes this a useful baseline for method and protocol context.

  4. 4. TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought

    High citation traction makes this a useful baseline for method and protocol context.

  5. 5. TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion

    Adds automatic metrics for broader coverage within this hub.

  6. 6. Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models

    Adds automatic metrics for broader coverage within this hub.

  8. 8. MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

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

Known Limitations

  • Only 5.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (10.1% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

automatic_metrics vs simulation_env

both=1, left_only=68, right_only=0

1 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

Banglasummeval

Coverage: 1 papers (1.4%)

1 papers (1.4%) mention Banglasummeval.

Examples: BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization

Benchmark Brief

Livemcpbench

Coverage: 1 papers (1.4%)

1 papers (1.4%) mention Livemcpbench.

Examples: LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

Top Papers

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