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HFEPX Hub

CS.AI + Llm As Judge Papers

Updated from current HFEPX corpus (Mar 8, 2026). 19 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 19 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: Adjudication. Frequently cited benchmark: AdvBench. 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 2, 2026.

Papers: 19 Last published: Mar 2, 2026 Global RSS Tag RSS
Cs.AILlm As Judge

Researcher Quick Triage

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

All Sampled Papers (19) Replication-Ready Only (4)

High-Signal Coverage

100.0%

19 / 19 sampled papers are not low-signal flagged.

Replication-Ready Set

4

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

3

Papers containing both `human_eval` and `llm_as_judge`.

  • 4 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 3 papers support judge-vs-human agreement analysis.
  • 2 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.

Currently showing only replication-ready papers in ranking and matrix sections (4 papers).

Why This Matters For Eval Research

  • 46.7% of papers report explicit human-feedback signals, led by rubric ratings.
  • LLM-as-judge appears in 78.9% of papers in this hub.
  • AdvBench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 3 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is adjudication (5.3% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • AdvBench appears in 6.7% of hub papers (1/19); use this cohort for benchmark-matched comparisons.
  • CAPArena appears in 6.7% of hub papers (1/19); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 33.3% of hub papers (5/19); compare with a secondary metric before ranking methods.
  • cost is reported in 13.3% of hub papers (2/19); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (46.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (13.3% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (46.7% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (66.7% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (46.7% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (20% vs 35% target).

Strengths

  • Strong human-feedback signal (46.7% of papers).
  • Most papers provide measurable evaluation context (46.7% benchmarks, 66.7% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 13.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (20% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (AdvBench vs CAPArena) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: AdvBench Metric Slice: accuracy Recent High-Signal Papers

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

Oct 21, 2025

 Yes
Rubric Rating
Human Eval , Llm As Judge CAPArena Spearman Not Reported
PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

Mar 2, 2026

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

Feb 27, 2026

 Yes
Red Team
Llm As Judge AdvBench , Jbf Eval Success rate , Jailbreak success rate Not Reported
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

Aug 3, 2025

 No
Not Reported
Llm As Judge Livemcpbench Task success Not Reported

Protocol Diff (Top Papers)

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

Signal PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge F… PanCanBench: A Comprehensive Benchmark for Evaluati… Jailbreak Foundry: From Papers to Runnable Attacks…
Human Feedback Rubric RatingRubric Rating, Expert VerificationRed Team
Evaluation Modes Human Eval, Llm As JudgeLlm As Judge, Automatic MetricsLlm As Judge
Benchmarks CAPArenaPancanbench, HealthbenchAdvBench, Jbf Eval
Metrics SpearmanAccuracySuccess rate, Jailbreak success rate
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsUnknown
Annotation Unit Multi Dim RubricMulti 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. PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + rubric ratings. Focus: Pancanbench / accuracy. Abstract: Moreover, high rubric-based scores do not ensure factual correctness,.

  2. When Metrics Disagree: Automatic Similarity vs. LLM-as-a-Judge for Clinical Dialogue Evaluation

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: accuracy. Abstract: This paper details the baseline model selection, fine-tuning process, evaluation methods, and the implications.

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + red-team protocols. Focus: AdvBench / success rate. Abstract: This system enables a standardized AdvBench evaluation of.

  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. HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: agreement. Abstract: For each dialogue history, we pair human and model.

  6. EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

    Adds LLM-as-judge with expert verification for broader protocol coverage within this hub. Signals: LLM-as-judge + expert verification. Focus: success rate. Abstract: We evaluate EpidemIQs across several different epidemic.

  7. InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: Innoeval. Abstract: However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened evaluation dimensions,.

  8. Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics

    Adds LLM-as-judge with red-team protocols for broader protocol coverage within this hub. Signals: LLM-as-judge + red-team protocols. Focus: Jailbreakbench. Abstract: We replace fragile pattern-based refusal detection with an.

Known Limitations

Known Limitations

  • Only 13.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (20% 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

  • Rubric Rating (3)
  • Expert Verification (2)
  • Pairwise Preference (2)
  • Red Team (2)

Evaluation Modes

  • Llm As Judge (15)
  • Automatic Metrics (5)
  • Human Eval (3)
  • Simulation Env (2)

Top Benchmarks

  • AdvBench (1)
  • CAPArena (1)
  • Healthbench (1)
  • Innoeval (1)

Top Metrics

  • Accuracy (5)
  • Cost (2)
  • Success rate (2)
  • Task success (2)

Rater Population Mix

  • Domain Experts (7)

Quality Controls

  • Adjudication (1)
  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 42.1% · benchmarks 36.8% · metrics 57.9% · quality controls 10.5%.

Top Papers

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

    Yimin Zhao, Sheela R. Damle, Simone E. Dekker, Scott Geng, Karly Williams Silva · Mar 2, 2026 · Citations: 0

    Rubric RatingExpert Verification Llm As JudgeAutomatic Metrics

    Large language models (LLMs) have achieved expert-level performance on standardized examinations, yet multiple-choice accuracy poorly reflects real-world clinical utility and safety.

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

    Amith Ananthram, Elias Stengel-Eskin, Lorena A. Bradford, Julia Demarest, Adam Purvis · Oct 21, 2025 · Citations: 0

    Rubric Rating Human EvalLlm As Judge

    In this work, we introduce PoSh, a metric for detailed image description that uses scene graphs as structured rubrics to guide LLMs-as-a-Judge, producing aggregate scores grounded in fine-grained errors (e.g.

  • Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

    Zhicheng Fang, Jingjie Zheng, Chenxu Fu, Wei Xu · Feb 27, 2026 · Citations: 0

    Red Team Llm As Judge Multi Agent

    Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols.

  • LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

    Guozhao Mo, Wenliang Zhong, Jiawei Chen, Qianhao Yuan, Xuanang Chen · Aug 3, 2025 · Citations: 0

    Llm As Judge Tool Use

    Unfortunately, there is still a large gap between real-world MCP usage and current evaluation: they typically assume single-server settings and directly inject tools into the model's context, bypassing the challenges of large-scale…

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