arXiv AI August 3, 2026 By Philipp D. Siedler, Jordan Sassoon

Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation

arXiv:2607. 28801v1 Announce Type: cross Abstract: Benchmark datasets are central to evaluating Large Language Models (LLMs), yet they are typically conceived as monolithic tasks, obscuring substantial variation in the demands of individual samples.

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arXiv AI

Sep 16

A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance

arXiv:2609.16592v1 Announce Type: new Abstract: This paper presents an end-to-end approach for generating context-specific large language model (LLM) benchmark datasets by combining expert input with...

By Kimberly Le Truong, Nari Johnson, Anna Kawakami, Hoda Heidari

llms benchmarks data-engineering

arXiv AI

Jun 11

Models That Know How Evaluations Are Designed Score Safer

arXiv:2605. 28591v2 Announce Type: replace-cross Abstract: The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings.

By Katharina Deckenbach, Haritz Puerto, Jonas Geiping, Sahar Abdelnabi

fine-tuning benchmarks safety

arXiv AI

Jun 11

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

arXiv:2601. 17717v3 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities.

By Kaituo Zhang, Mingzhi Hu, Hoang Anh Duy Le, Fariha Kabir Torsha, Zhimeng Jiang, Minh Khai Bui, Chia-Yuan Chang, Yu-Neng Chuang, Zhen Xiong, Ying Lin, Guanchu Wang, Na Zou

llms

arXiv AI

Jul 28

Beyond Shapley: An Influence-Based Data Auditing Pipeline for LLM Alignment and Evaluation

arXiv:2607. 22766v1 Announce Type: cross Abstract: The alignment of Large Language Models (LLMs) is increasingly bottlenecked by data quality.

By Yunting Song, Matthew Watson, Peter Grabowski, Jun Qin

llms reinforcement-learning benchmarks safety data-engineering

arXiv AI

Jul 21

BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation

arXiv:2604. 09497v2 Announce Type: replace-cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.

By Hippolyte Gisserot-Boukhlef, Nicolas Boizard, Emmanuel Malherbe, C\'eline Hudelot, Pierre Colombo

llms

arXiv AI

4d ago

PADM\'E: Preference Alignment Data Synthesis for Meta-Evaluation of LM Agent Evaluators

PADM'E is a method for synthesizing preference‑aligned data to meta‑evaluate language‑model (LM) evaluators of agentic behaviors. It reframes meta‑evaluation as a preference judgment problem, generating criterion‑based data with small LMs and no human involvement. In a prototype, PADM'E produced 1,000 samples across four domains and three criteria, and human validation showed agreement with human judgment rising from 73% to 85% compared to a naive baseline.

By Cheng Chang, Yining Mao, Peng Qi

llms agents safety

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Related stories

A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance

Models That Know How Evaluations Are Designed Score Safer

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

Beyond Shapley: An Influence-Based Data Auditing Pipeline for LLM Alignment and Evaluation

BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation

PADM\'E: Preference Alignment Data Synthesis for Meta-Evaluation of LM Agent Evaluators