Automatic Metrics + Expert Verification (Last 90 Days)

Why This Matters For Eval Research

• 100% of papers report explicit human-feedback signals, led by expert verification.
• automatic metrics appears in 100% of papers in this hub.
• BIRD is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

• Most common quality-control signal is adjudication (13.3% of papers).
• Rater context is mostly domain experts, and annotation is commonly ranking annotation; use this to scope replication staffing.
• Stratify by benchmark (BIRD vs Cricbench) before comparing methods.

Benchmark Interpretation

• BIRD appears in 6.7% of hub papers (1/15); use this cohort for benchmark-matched comparisons.
• Cricbench appears in 6.7% of hub papers (1/15); use this cohort for benchmark-matched comparisons.

Metric Interpretation

• accuracy is reported in 33.3% of hub papers (5/15); compare with a secondary metric before ranking methods.
• precision is reported in 20% of hub papers (3/15); compare with a secondary metric before ranking methods.

Researcher Checklist

• Strong: Papers with explicit human feedback

  Coverage is strong (100% vs 45% target).

• Moderate: Papers reporting quality controls

  Coverage is usable but incomplete (26.7% vs 30% target).

• Gap: Papers naming benchmarks/datasets

  Coverage is a replication risk (13.3% vs 35% target).

• Strong: Papers naming evaluation metrics

  Coverage is strong (93.3% vs 35% target).

• Strong: Papers with known rater population

  Coverage is strong (100% vs 35% target).

• Moderate: Papers with known annotation unit

  Coverage is usable but incomplete (26.7% vs 35% target).

Strengths

• Strong human-feedback signal (100% of papers).

Known Gaps

• Benchmark coverage is thin (13.3% of papers mention benchmarks/datasets).

Suggested Next Analyses

• Stratify by benchmark (BIRD vs Cricbench) before comparing methods.
• Track metric sensitivity by reporting both accuracy and precision.
• Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

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 and quality-control evidence. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Phenotyping is fundamental to rare disease diagnosis, but manual curation.

2. Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots

   High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + expert verification. Focus: agreement. Abstract: Human-in-the-loop validation is essential in safety-critical clinical AI,.

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

   High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: Multimodal large language models (MLLMs) have shown great.

4. SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

   High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: Minimally invasive surgery has dramatically improved patient operative.

5. HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

   Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + expert verification. Focus: HLE / accuracy. Abstract: Overall, HLE-Verified improves HLE-style evaluations by reducing.

6. CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

   Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + expert verification. Focus: DROP / accuracy. Abstract: We evaluate six state-of-the-art models, including GPT-4o,.

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

   Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: cost. Abstract: The code, datasets, and evaluation protocols.

8. APEX-Agents

   Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: pass@1. Abstract: We open source the APEX-Agents benchmark.

Known Limitations

• Benchmark coverage is thin (13.3% of papers mention benchmarks/datasets).
• Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
• Cross-page comparisons should be benchmark- and metric-matched to avoid protocol confounding.

Research Utility Snapshot