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HFEPX Daily Archive: 2025-12-02

Updated from current HFEPX corpus (Feb 27, 2026). 6 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Common metric signal: perplexity. Newest paper in this set is from Dec 2, 2025.

Papers: 6 Last published: Dec 2, 2025 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded

Updated from current HFEPX corpus (Feb 27, 2026). This page covers 6 papers centered on HFEPX Daily Archive: 2025-12-02. Common evaluation modes include Automatic Metrics, Simulation Env, with benchmark emphasis on multiple datasets. Use the anchored takeaways below to compare protocol choices and identify papers with stronger evidence depth.

Why This Matters For Eval Research

Protocol Takeaways

Metric Interpretation

  • perplexity is a common reported metric and should be paired with protocol context before ranking methods.
  • 1 papers (16.7%) mention perplexity.
  • Most common evaluation modes: Automatic Metrics.

Researcher Checklist

  • Papers with explicit human feedback: Coverage is a replication risk (0% vs 45% target).
  • Papers reporting quality controls: Coverage is a replication risk (0% vs 30% target).
  • Papers naming benchmarks/datasets: Coverage is a replication risk (0% vs 35% target).
  • Papers naming evaluation metrics: Coverage is a replication risk (16.7% vs 35% target).
  • Papers with known rater population: Coverage is a replication risk (0% vs 35% target).
  • Papers with known annotation unit: Coverage is a replication risk (0% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (0% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (0% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (0% vs 35% target).

Papers naming evaluation metrics

Coverage is a replication risk (16.7% vs 35% target).

Papers with known rater population

Coverage is a replication risk (0% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (0% vs 35% target).

Suggested Reading Order

  1. 1. Randomized Masked Finetuning: An Efficient Way to Mitigate Memorization of PIIs in LLMs

    Start with this anchor paper for scope and protocol framing. Covers Automatic Metrics.

  2. 2. Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks

    Covers Automatic Metrics.

  3. 3. From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?

    Covers Simulation Env.

  4. 4. promptolution: A Unified, Modular Framework for Prompt Optimization

    Covers Automatic Metrics.

  5. 5. BOOM: Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion

    Covers Automatic Metrics.

  6. 6. PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models

    Covers Simulation Env.

Known Limitations

  • Narrative synthesis is grounded in metadata and abstracts only; full-paper method details may be missing.
  • Extraction fields are conservative and can under-report implicit protocol details.
  • Daily and rolling archives can be sparse and should be cross-checked with neighboring windows.

Research Utility Links

automatic_metrics vs simulation_env

both=0, left_only=4, right_only=2

0 papers use both Automatic Metrics and Simulation Env.

Papers Published On This Date

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