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HFEPX Hub

Human Eval Or Llm As Judge Papers

Updated from current HFEPX corpus (Feb 27, 2026). 46 papers are grouped in this hub page. Common evaluation modes: Human Eval, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Inter Annotator Agreement Reported. Frequently cited benchmark: Retrieval. Common metric signal: agreement. 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: 46 Last published: Feb 26, 2026 Global RSS Tag RSS
Human EvalLlm As Judge

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 46 papers for Human Eval Or Llm As Judge Papers. Dominant protocol signals include human evaluation, automatic metrics, LLM-as-judge, with frequent benchmark focus on Retrieval, AIME and metric focus on agreement, accuracy. 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 4.3% of hub papers (2/46); use this cohort for benchmark-matched comparisons.
  • AIME appears in 2.2% of hub papers (1/46); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • agreement is reported in 30.4% of hub papers (14/46); compare with a secondary metric before ranking methods.
  • accuracy is reported in 17.4% of hub papers (8/46); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Tighten coverage on Papers with explicit human feedback. Coverage is usable but incomplete (28.3% vs 45% target).
  • Tighten coverage on Papers reporting quality controls. Coverage is usable but incomplete (19.6% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (17.4% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (58.7% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (21.7% vs 35% target).
  • Tighten coverage on Papers with known annotation unit. Coverage is usable but incomplete (21.7% vs 35% target).

Papers with explicit human feedback

Coverage is usable but incomplete (28.3% vs 45% target).

Papers reporting quality controls

Coverage is usable but incomplete (19.6% vs 30% target).

Papers naming benchmarks/datasets

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

Papers naming evaluation metrics

Coverage is strong (58.7% vs 35% target).

Papers with known rater population

Coverage is usable but incomplete (21.7% vs 35% target).

Papers with known annotation unit

Coverage is usable but incomplete (21.7% vs 35% target).

Suggested Reading Order

  1. 1. Human Label Variation in Implicit Discourse Relation Recognition

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

  2. 2. Distill and Align Decomposition for Enhanced Claim Verification

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

  3. 3. Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs

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

  4. 4. Overton Pluralistic Reinforcement Learning for Large Language Models

    Include an LLM-as-judge paper to assess judge design and agreement assumptions.

  5. 5. Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning

    Include an LLM-as-judge paper to assess judge design and agreement assumptions.

  6. 6. MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification

    Adds human evaluation for broader coverage within this hub.

  7. 7. A Benchmark for Deep Information Synthesis

    Adds human evaluation for broader coverage within this hub.

  8. 8. Pressure Reveals Character: Behavioural Alignment Evaluation at Depth

    Adds human evaluation for broader coverage within this hub.

Known Limitations

  • Only 19.6% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (21.7% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs llm_as_judge

both=2, left_only=36, right_only=8

2 papers use both Human Eval and Llm As Judge.

human_eval vs automatic_metrics

both=9, left_only=29, right_only=2

9 papers use both Human Eval and Automatic Metrics.

llm_as_judge vs automatic_metrics

both=2, left_only=8, right_only=9

2 papers use both Llm As Judge and Automatic Metrics.

Top Papers

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