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HFEPX Hub

Human Eval Papers (Last 45 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 15 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 15 papers are grouped in this hub page. Common evaluation modes: Human Eval, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Adjudication. Frequently cited benchmark: AIME. 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 20, 2026.

Papers: 15 Last published: Feb 20, 2026 Global RSS Tag RSS
Human EvalLast 45d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

High-Signal Coverage

100.0%

15 / 15 sampled papers are not low-signal flagged.

Replication-Ready Set

0

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 0 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 1 papers support judge-vs-human agreement analysis.
  • 2 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 45.5% of papers report explicit human-feedback signals, led by pairwise preferences.
  • human evaluation appears in 73.3% of papers in this hub.
  • AIME is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is adjudication (6.7% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • AIME appears in 9.1% of hub papers (1/15); use this cohort for benchmark-matched comparisons.
  • Correctbench appears in 9.1% of hub papers (1/15); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 27.3% of hub papers (3/15); compare with a secondary metric before ranking methods.
  • f1 is reported in 18.2% of hub papers (2/15); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (45.5% vs 45% target).

  • Moderate: Papers reporting quality controls

    Coverage is usable but incomplete (18.2% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (18.2% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (45.5% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (18.2% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (27.3% vs 35% target).

Strengths

  • Strong human-feedback signal (45.5% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 18.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% coverage).
  • Benchmark coverage is thin (18.2% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (AIME vs Correctbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and f1.
Recommended Queries (Expanded)

Recommended Queries

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
Validating Political Position Predictions of Arguments

Feb 20, 2026

Yes Human Eval Not Reported Agreement Gold Questions , Inter Annotator Agreement Reported
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

Feb 21, 2026

Yes Human Eval GSM8K , AIME Not Reported Not Reported
RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind

Jan 22, 2026

Yes Human Eval Rebuttalbench Not Reported Not Reported
Discovering Implicit Large Language Model Alignment Objectives

Feb 17, 2026

Yes Human Eval Not Reported Not Reported Not Reported
Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback

Feb 24, 2026

Yes Human Eval Not Reported Not Reported Not Reported
AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization

Feb 23, 2026

No
Not Reported
Human Eval , Llm As Judge Not Reported Not Reported Not Reported
FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health

Feb 17, 2026

No
Not Reported
Human Eval , Simulation Env Not Reported Not Reported Adjudication
CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models

Feb 24, 2026

No
Not Reported
Human Eval , Automatic Metrics Not Reported Accuracy Not Reported
Vichara: Appellate Judgment Prediction and Explanation for the Indian Judicial System

Feb 20, 2026

No
Not Reported
Human Eval , Automatic Metrics Not Reported F1 Not Reported
Distill and Align Decomposition for Enhanced Claim Verification

Feb 25, 2026

No
Not Reported
Human Eval , Automatic Metrics Not Reported Accuracy , F1 Not Reported
Claim Automation using Large Language Model

Feb 18, 2026

No
Not Reported
Human Eval , Automatic Metrics Not Reported Accuracy Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal Validating Political Position Predictions of Argume… Think$^{2}$: Grounded Metacognitive Reasoning in La… RebuttalAgent: Strategic Persuasion in Academic Reb…
Human Feedback Pairwise PreferencePairwise PreferencePairwise Preference, Critique Edit
Evaluation Modes Human EvalHuman EvalHuman Eval
Benchmarks Not reportedGSM8K, AIMERebuttalbench
Metrics AgreementNot reportedNot reported
Quality Controls Gold Questions, Inter Annotator Agreement ReportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit PairwiseUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Distill and Align Decomposition for Enhanced Claim Verification

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Focus: accuracy. Abstract: Human evaluation confirms the high quality of the generated subclaims.

  2. Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + pairwise preferences. Abstract: Preference-based RL offers an appealing alternative by learning from human preferences over.

  3. CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Focus: accuracy. Abstract: CARE also produces high-quality, contextually grounded rationales, validated by both automatic and human.

  4. Validating Political Position Predictions of Arguments

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: agreement. Abstract: Real-world knowledge representation often requires capturing subjective, continuous attributes.

  5. Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: GSM8K. Abstract: Blinded human evaluations over 580 query pairs show an.

  6. AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: human evaluation. Abstract: We evaluate AgenticSum on two public datasets, using reference-based metrics, LLM-as-a-judge assessment, and.

  7. RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind

    Adds human evaluation with pairwise preferences for broader protocol coverage within this hub. Signals: human evaluation + pairwise preferences. Focus: Rebuttalbench. Abstract: For reliable and efficient automated evaluation,.

  8. Discovering Implicit Large Language Model Alignment Objectives

    Adds human evaluation with rubric ratings for broader protocol coverage within this hub. Signals: human evaluation + rubric ratings. Abstract: Existing interpretation methods typically rely on pre-defined rubrics,.

Known Limitations

Known Limitations

  • Only 18.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Pairwise Preference (4)
  • Critique Edit (1)
  • Rlaif Or Synthetic Feedback (1)
  • Rubric Rating (1)

Evaluation Modes

  • Human Eval (11)
  • Automatic Metrics (4)
  • Llm As Judge (1)
  • Simulation Env (1)

Top Benchmarks

  • AIME (1)
  • Correctbench (1)
  • Cruxeval (1)
  • GSM8K (1)

Top Metrics

  • Accuracy (3)
  • F1 (2)
  • Agreement (1)
  • F1 macro (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

  • Adjudication (1)
  • Gold Questions (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 33.3% · benchmarks 13.3% · metrics 46.7% · quality controls 13.3%.

Top Papers

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