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HFEPX Hub

CS.CL + Llm As Judge Papers

Updated from current HFEPX corpus (Apr 12, 2026). 103 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 103 papers are grouped in this hub page. Common evaluation modes: Llm As Judge, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Calibration. Frequently cited benchmark: Healthbench. 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 Mar 22, 2026.

Papers: 103 Last published: Mar 22, 2026 Global RSS Tag RSS
Cs.CLLlm As Judge

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 103 total papers in this hub).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

13

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

4

Papers containing both `human_eval` and `llm_as_judge`.

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

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

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Why This Matters For Eval Research

  • 48.7% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LLM-as-judge appears in 75.7% of papers in this hub.
  • Healthbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 2 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is rater calibration (2.9% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • Healthbench appears in 2.6% of hub papers (2/103); use this cohort for benchmark-matched comparisons.
  • AdvBench appears in 1.3% of hub papers (1/103); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 28.2% of hub papers (22/103); compare with a secondary metric before ranking methods.
  • agreement is reported in 10.3% of hub papers (8/103); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (48.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (7.7% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30.8% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (56.4% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (28.2% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (29.5% vs 35% target).

Strengths

  • Strong human-feedback signal (48.7% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 7.7% of papers report quality controls; prioritize calibration/adjudication evidence.

Suggested Next Analyses

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

Recommended Queries

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

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
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

Yes Human Eval , Llm As Judge WebArena , ToolBench Precision , Pass@1 Not Reported
PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions

Oct 21, 2025

Yes Human Eval , Llm As Judge CAPArena Spearman Not Reported
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

Mar 28, 2026

Yes Llm As Judge , Automatic Metrics MMLU Accuracy , Relevance Not Reported
PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

Mar 2, 2026

Yes Llm As Judge , Automatic Metrics Pancanbench , Healthbench Accuracy Not Reported
Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

Feb 27, 2026

Yes Llm As Judge AdvBench , Jbf Eval Success rate , Jailbreak success rate Not Reported
Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith

Mar 25, 2026

No
Not Reported
Human Eval , Llm As Judge Not Reported Accuracy , Kappa Inter Annotator Agreement Reported
HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue

Jan 9, 2026

Yes Human Eval , Llm As Judge Not Reported Agreement Not Reported
Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

Apr 8, 2026

Yes Llm As Judge IFEval , Healthbench Not Reported Not Reported
Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study

Apr 2, 2026

Yes Llm As Judge , Automatic Metrics Not Reported Accuracy Not Reported
A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness

Feb 4, 2026

Yes Llm As Judge Reliablebench Not Reported Not Reported
Does LLM Alignment Really Need Diversity? An Empirical Study of Adapting RLVR Methods for Moral Reasoning

Mar 11, 2026

Yes Llm As Judge Morebench Not Reported Not Reported
IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation

Mar 5, 2026

Yes Llm As Judge If Rewardbench Not Reported Not Reported

Protocol Diff (Top Papers)

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

Signal AgentHER: Hindsight Experience Replay for LLM Agent… PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge F… PubMed Reasoner: Dynamic Reasoning-based Retrieval…
Human Feedback DemonstrationsRubric RatingExpert Verification
Evaluation Modes Human Eval, Llm As JudgeHuman Eval, Llm As JudgeLlm As Judge, Automatic Metrics
Benchmarks WebArena, ToolBenchCAPArenaMMLU
Metrics Precision, Pass@1SpearmanAccuracy, Relevance
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownDomain ExpertsDomain Experts
Annotation Unit TrajectoryMulti Dim RubricUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. HyperMem: Hypergraph Memory for Long-Term Conversations

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: However, existing approaches as Retrieval-Augmented Generation (RAG) and graph-based memory mostly.

  2. Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Focus: IFEval. Abstract: LLM-as-a-judge has become the de facto approach for evaluating LLM outputs.

  3. AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + demonstration data. Focus: WebArena / precision. Abstract: AgentHER realises this idea through a four-stage.

  4. PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + rubric ratings. Focus: CAPArena / spearman. Abstract: In this work, we introduce PoSh, a.

  5. Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + red-team protocols. Focus: AdvBench / success rate. Abstract: This system enables a standardized AdvBench.

  6. PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

    Adds LLM-as-judge with rubric ratings for broader protocol coverage within this hub. Signals: LLM-as-judge + rubric ratings. Focus: Pancanbench / accuracy. Abstract: Moreover, high rubric-based scores do not.

  7. PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

    Adds LLM-as-judge with expert verification for broader protocol coverage within this hub. Signals: LLM-as-judge + expert verification. Focus: MMLU / accuracy. Abstract: Moreover, LLM-as-judge evaluations prefer our responses.

Known Limitations

Known Limitations

  • Only 7.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • 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 (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Pairwise Preference (20)
  • Rubric Rating (12)
  • Expert Verification (7)
  • Red Team (4)

Evaluation Modes

  • Llm As Judge (78)
  • Automatic Metrics (33)
  • Human Eval (10)
  • Simulation Env (6)

Top Benchmarks

  • Healthbench (2)
  • AdvBench (1)
  • ALFWorld (1)
  • Aurora Bench (1)

Top Metrics

  • Accuracy (22)
  • Agreement (8)
  • Cost (6)
  • F1 (6)

Rater Population Mix

  • Domain Experts (22)

Quality Controls

  • Calibration (3)
  • Adjudication (2)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 65.0% · benchmarks 38.3% · metrics 51.7% · quality controls 10.0%.

Top Papers

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