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HFEPX Hub

CS.CL + General Papers

Updated from current HFEPX corpus (Apr 9, 2026). 414 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 9, 2026). 414 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Inter Annotator Agreement Reported. Frequently cited benchmark: ALFWorld. 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: 414 Last published: Mar 22, 2026 Global RSS Tag RSS
Cs.CLGeneral

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 414 total papers in this hub).

High-Signal Coverage

100.0%

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

Replication-Ready Set

21

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

3

Papers containing both `human_eval` and `llm_as_judge`.

  • 21 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 3 papers support judge-vs-human agreement analysis.
  • 13 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 (21 papers).

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Why This Matters For Eval Research

  • 68.4% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 46.9% of papers in this hub.
  • ALFWorld 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 inter-annotator agreement reporting (2.4% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.

Benchmark Interpretation

  • ALFWorld appears in 1.2% of hub papers (5/414); use this cohort for benchmark-matched comparisons.
  • HotpotQA appears in 1.2% of hub papers (5/414); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 26.1% of hub papers (108/414); compare with a secondary metric before ranking methods.
  • cost is reported in 11.1% of hub papers (46/414); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (68.4% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (20.5% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (54.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (12.8% vs 35% target).

  • Moderate: Papers with known annotation unit

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

Strengths

  • Strong human-feedback signal (68.4% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 31.9% of papers.

Known Gaps

  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.8% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (ALFWorld vs HotpotQA) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
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
SCOPE: Selective Conformal Optimized Pairwise LLM Judging

Feb 13, 2026

Yes Automatic Metrics MT Bench , LMSYS Chatbot Arena Error rate Calibration
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

Yes Human Eval , Automatic Metrics Rewardbench Accuracy , Helpfulness Not Reported
TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories

Apr 8, 2026

Yes Automatic Metrics Tracesafe Bench Accuracy Not Reported
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

Yes Automatic Metrics Sodium Bench Accuracy Not Reported
Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching

Apr 7, 2026

Yes Automatic Metrics Scirepeval Recall Not Reported
When AI Meets Early Childhood Education: Large Language Models as Assessment Teammates in Chinese Preschools

Mar 25, 2026

Yes Automatic Metrics Interaction2eval Agreement , Cost Not Reported
Rethinking Atomic Decomposition for LLM Judges: A Prompt-Controlled Study of Reference-Grounded QA Evaluation

Mar 30, 2026

Yes Automatic Metrics TruthfulQA Accuracy Not Reported
SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions

Mar 14, 2026

Yes Automatic Metrics Semeval F1 , F1 macro Not Reported
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

Feb 14, 2026

Yes Automatic Metrics MT Bench , AlpacaEval Elo Not Reported
PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning

Jan 17, 2026

Yes Automatic Metrics Calconflictbench Error rate 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… SCOPE: Selective Conformal Optimized Pairwise LLM J…
Human Feedback DemonstrationsRubric RatingPairwise Preference
Evaluation Modes Human Eval, Llm As JudgeHuman Eval, Llm As JudgeAutomatic Metrics
Benchmarks WebArena, ToolBenchCAPArenaMT Bench, LMSYS Chatbot Arena
Metrics Precision, Pass@1SpearmanError rate
Quality Controls Not reportedNot reportedCalibration
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit TrajectoryMulti Dim RubricPairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. More Human, More Efficient: Aligning Annotations with Quantized SLMs

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: agreement. Abstract: As Large Language Model (LLM) capabilities advance, the demand for.

  2. From Intuition to Calibrated Judgment: A Rubric-Based Expert-Panel Study of Human Detection of LLM-Generated Korean Text

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: We present LREAD, a Korean-specific instantiation of a rubric-based expert-calibration.

  3. Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    High citation traction makes this a strong baseline for protocol comparison. Signals: human evaluation + pairwise preferences. Focus: Rewardbench / accuracy. Abstract: While benchmarks for general response quality.

  4. c824ab48-4915-4c68-a7d5-82128a3197fc

    High citation traction makes this a strong baseline for protocol comparison.

  5. 37e66347-dcaf-4178-8b3b-169baef9860d

    High citation traction makes this a strong baseline for protocol comparison.

  6. 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.

  7. LMUnit: Fine-grained Evaluation with Natural Language Unit Tests

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: Biggenbench / agreement. Abstract: As language models become integral to critical.

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

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: human evaluation + rubric ratings. Focus: CAPArena / spearman. Abstract: In this work, we introduce PoSh,.

Known Limitations

Known Limitations

  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.8% 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 (152)
  • Red Team (39)
  • Rubric Rating (35)
  • Critique Edit (33)

Evaluation Modes

  • Automatic Metrics (194)
  • Llm As Judge (47)
  • Simulation Env (44)
  • Human Eval (30)

Top Benchmarks

  • ALFWorld (5)
  • HotpotQA (5)
  • LMSYS Chatbot Arena (5)
  • MMLU (4)

Top Metrics

  • Accuracy (108)
  • Cost (46)
  • F1 (18)
  • Latency (18)

Rater Population Mix

  • Domain Experts (50)
  • Mixed (3)

Quality Controls

  • Inter Annotator Agreement Reported (10)
  • Calibration (8)
  • Adjudication (7)
  • Gold Questions (1)
Coverage diagnostics (sample-based): human-feedback 91.7% · benchmarks 53.3% · metrics 73.3% · quality controls 21.7%.

Top Papers

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