Expert Verification Papers (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 65.2% of papers in this hub.
• Ad-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

• Most common quality-control signal is adjudication (8.7% of papers).
• Rater context is mostly domain experts, and annotation is commonly ranking annotation; use this to scope replication staffing.
• Stratify by benchmark (Ad-Bench vs BIRD) before comparing methods.

Benchmark Interpretation

• Ad-Bench appears in 4.3% of hub papers (1/23); use this cohort for benchmark-matched comparisons.
• BIRD appears in 4.3% of hub papers (1/23); use this cohort for benchmark-matched comparisons.

Metric Interpretation

• accuracy is reported in 21.7% of hub papers (5/23); compare with a secondary metric before ranking methods.
• precision is reported in 13% of hub papers (3/23); 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 (21.7% vs 30% target).

• Gap: Papers naming benchmarks/datasets

  Coverage is a replication risk (17.4% vs 35% target).

• Strong: Papers naming evaluation metrics

  Coverage is strong (65.2% 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 (26.1% vs 35% target).

Strengths

• Strong human-feedback signal (100% of papers).
• Agentic evaluation appears in 30.4% of papers.

Known Gaps

• Benchmark coverage is thin (17.4% of papers mention benchmarks/datasets).

Suggested Next Analyses

• Stratify by benchmark (Ad-Bench vs BIRD) 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. pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation

   High citation traction makes this a strong baseline for protocol comparison. Signals: expert verification. Abstract: Parameter-efficient fine-tuning has demonstrated promising results across various visual adaptation tasks, such as.

4. TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

   High citation traction makes this a strong baseline for protocol comparison. Signals: simulation environments + expert verification. Abstract: As mental health chatbots proliferate to address the global treatment.

5. AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

   Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + expert verification. Focus: Ad-Bench / pass@1. Abstract: While Large Language Model (LLM) agents have.

6. 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.

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

   Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: DROP / accuracy. Abstract: We evaluate six state-of-the-art.

8. Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

   Adds evaluation protocol evidence with expert verification for broader protocol coverage within this hub. Signals: expert verification. Focus: LiveCodeBench. Abstract: Existing Multi-Agent Systems (MAS) typically rely on static,.

Known Limitations

• Benchmark coverage is thin (17.4% 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