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HFEPX Hub

General + Pairwise Preference (Last 30 Days)

Updated from current HFEPX corpus (Apr 19, 2026). 37 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 19, 2026). 37 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: AlpacaEval. 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 Apr 8, 2026.

Papers: 37 Last published: Apr 8, 2026 Global RSS Tag RSS
GeneralPairwise PreferenceLast 30d

Researcher Quick Triage

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

All Sampled Papers (37) Replication-Ready Only (3)

High-Signal Coverage

100.0%

37 / 37 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 48.6% of papers in this hub.
  • AlpacaEval is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is inter-annotator agreement reporting (5.4% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

  • AlpacaEval appears in 2.7% of hub papers (1/37); use this cohort for benchmark-matched comparisons.
  • Codabench appears in 2.7% of hub papers (1/37); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 18.9% of hub papers (7/37); compare with a secondary metric before ranking methods.
  • relevance is reported in 13.5% of hub papers (5/37); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (8.1% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (8.1% 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 (13.5% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (51.4% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 8.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (13.5% coverage).
  • Benchmark coverage is thin (8.1% of papers mention benchmarks/datasets).

Suggested Next Analyses

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

Recommended Queries

Judge vs Human Agreement Benchmark Slice: AlpacaEval Metric Slice: accuracy IAA-Reported Evaluations 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
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

 Yes
Pairwise Preference , Rubric Rating
Human Eval , Automatic Metrics Rewardbench Accuracy , Helpfulness Not Reported
ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims

Mar 27, 2026

 Yes
Pairwise Preference
Automatic Metrics Codabench Recall , Recall@k Not Reported
DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignment

Mar 23, 2026

 Yes
Pairwise Preference
Automatic Metrics MT Bench , AlpacaEval Accuracy Not Reported

Protocol Diff (Top Papers)

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

Signal Personalized RewardBench: Evaluating Reward Models… ClimateCheck 2026: Scientific Fact-Checking and Dis… DSPA: Dynamic SAE Steering for Data-Efficient Prefe…
Human Feedback Pairwise Preference, Rubric RatingPairwise PreferencePairwise Preference
Evaluation Modes Human Eval, Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks RewardbenchCodabenchMT Bench, AlpacaEval
Metrics Accuracy, HelpfulnessRecall, Recall@kAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit PairwiseUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Abstract: Today's large language models (LLMs) are trained to align with user preferences through methods such.

  2. 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.

  3. Self-Debias: Self-correcting for Debiasing Large Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: pairwise preferences. Abstract: Unlike standard preference optimization which applies broad penalties, Self-Debias employs a fine-grained trajectory-level objective subject.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: Rewardbench / accuracy. Abstract: While benchmarks for general response quality are.

  5. Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + pairwise preferences. Abstract: Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge).

  6. From Consensus to Split Decisions: ABC-Stratified Sentiment in Holocaust Oral Histories

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: kappa. Abstract: We report pairwise percent agreement, Cohen.

  7. Measuring Faithfulness Depends on How You Measure: Classifier Sensitivity in LLM Chain-of-Thought Evaluation

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: kappa. Abstract: Three classifiers (a regex-only detector, a.

  8. Signals: Trajectory Sampling and Triage for Agentic Interactions

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: cost. Abstract: In a controlled annotation study on.

Known Limitations

Known Limitations

  • Only 8.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (13.5% 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 (37)
  • Critique Edit (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (18)
  • Llm As Judge (2)
  • Human Eval (1)

Top Benchmarks

  • AlpacaEval (1)
  • Codabench (1)
  • MT Bench (1)
  • Rewardbench (1)

Top Metrics

  • Accuracy (7)
  • Relevance (5)
  • Coherence (4)
  • Cost (3)

Rater Population Mix

  • Domain Experts (5)

Quality Controls

  • Inter Annotator Agreement Reported (2)
  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 8.1% · metrics 54.1% · quality controls 8.1%.

Top Papers

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