Daily Archive
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.
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.
Evaluation emphasis: Automatic Metrics and Simulation Env appear frequently in this slice.
Evidence: Randomized Masked Finetuning: An Efficient Way to Mitigate Memorization of PIIs in LLMs , Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks , From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?
Metric concentration: perplexity is repeatedly reported in this group.
Evidence: Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks , From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars? , promptolution: A Unified, Modular Framework for Prompt Optimization
This hub is useful for narrowing protocol decisions before running expensive evaluations.
Evidence: From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars? , promptolution: A Unified, Modular Framework for Prompt Optimization , BOOM: Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion
Use benchmark and metric filters to narrow to directly comparable evaluation setups.
Evidence: promptolution: A Unified, Modular Framework for Prompt Optimization , BOOM: Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion , PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models
Papers with explicit human feedback is visible in approximately 0% of papers in this set.
Evidence: BOOM: Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion , PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models , Randomized Masked Finetuning: An Efficient Way to Mitigate Memorization of PIIs in LLMs
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).
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. Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks
Covers Automatic Metrics.
3. From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?
Covers Simulation Env.
4. promptolution: A Unified, Modular Framework for Prompt Optimization
Covers Automatic Metrics.
5. BOOM: Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion
Covers Automatic Metrics.
6. PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models
Covers Simulation Env.
automatic_metrics vs simulation_env
both=0, left_only=4, right_only=2
0 papers use both Automatic Metrics and Simulation Env.
Kunj Joshi, David A. Smith · Dec 2, 2025
We present MaxTER, a Pareto-optimal evaluation framework for assessing privacy-utility tradeoffs, and show the performance of RMFT vs Deduplication by Area Under The Response Curve (AURC) metric.
Songwen Zhao, Danqing Wang, Kexun Zhang, Jiaxuan Luo, Zhuo Li · Dec 2, 2025
Vibe coding is a new programming paradigm in which human engineers instruct large language model (LLM) agents to complete complex coding tasks with little supervision.
Dawei Li, Abdullah Alnaibari, Arslan Bisharat, Manny Sandoval, Deborah Hall · Dec 2, 2025
To assess mediation quality, we construct a large Reddit-based dataset and propose a multi-stage evaluation pipeline combining principle-based scoring, user simulation, and human comparison.
Tom Zehle, Timo Heiß, Moritz Schlager, Matthias Aßenmacher, Matthias Feurer · Dec 2, 2025
It integrates multiple contemporary discrete prompt optimizers, supports systematic and reproducible benchmarking, and returns framework-agnostic prompt strings, enabling seamless integration into existing LLM pipelines while remaining agno
Sai Koneru, Fabian Retkowski, Christian Huber, Lukas Hilgert, Seymanur Akti · Dec 2, 2025
The globalization of education and rapid growth of online learning have made localizing educational content a critical challenge.
Robert Belanec, Ivan Srba, Maria Bielikova · Dec 2, 2025
While its modular design supports extensibility, it natively provides a representative set of 19 PEFT methods, 27 classification and text generation datasets addressing 12 tasks, and both standard and PEFT-specific evaluation metrics.
