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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 .

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.

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

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

Feb 16, 2026

No
Not Reported
Llm As Judge Innoeval Not Reported Adjudication
Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics

Dec 18, 2025

Yes Llm As Judge Jailbreakbench Not Reported Not Reported
Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards

Feb 20, 2026

Yes
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy , Win rate Not Reported
EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

Sep 24, 2025

Yes Llm As Judge , Simulation Env Not Reported Success rate , Cost 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
World-Model-Augmented Web Agents with Action Correction

Feb 17, 2026

No
Not Reported
Llm As Judge , Simulation Env VisualWebArena , Mind2Web Not Reported Not Reported
LogiPart: Local Large Language Models for Data Exploration at Scale with Logical Partitioning

Sep 26, 2025

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy Calibration
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

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

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