← Back to all news
arXiv Computation and Language September 15, 2026 By Pavel Chizhov, Anton Changalidis, Vishnu Prasad Vijaya Kumar, Yannick Detrois, Mattia Nee, Pierre-Carl Langlais, Ivan P. Yamshchikov

Who Benchmarks the Benchmarks? Towards Comprehensive Evaluation of Commonsense Reasoning Benchmarks

Read the original on arXiv Computation and Language →

The Flow has not summarised this story yet — read it at arXiv Computation and Language.

  • llms
  • benchmarks

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv AI
Aug 13

Commonsense on Demand: Generating and Selectively Integrating Commonsense Knowledge for Natural Language Inference

arXiv:2507. 15100v3 Announce Type: replace-cross Abstract: Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis.

By Chathuri Jayaweera, Brianna Yanqui, Bonnie J. Dorr
llmsbenchmarkssafety
More like this →
arXiv AI
Jul 14

Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces

arXiv:2604. 11996v2 Announce Type: replace-cross Abstract: Should we trust Large Language Models (LLMs) with high accuracy?

By Manas Pathak, Xingyao Chen, Shuozhe Li, Amy Zhang, Liu Leqi
llmsbenchmarks
More like this →
arXiv Computation and Language
Sep 23

ReasonLab: A Controlled and Auditable Evaluation of Prompting Techniques for Multiple-Choice QA

arXiv:2607.14109v2 Announce Type: replace Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central...

By Inder Preet, Shuxin Lin, Dhaval Patel
llmsnlpbenchmarks
More like this →
arXiv AI
Jul 17

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.

By Inder Preet, Shuxin Lin, Dhaval Patel
llmsnlpbenchmarks
More like this →
arXiv AI
Aug 3

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.

By Philipp D. Siedler, Jordan Sassoon
llmsagentsbenchmarkssafety
More like this →
arXiv AI
Oct 1

ArgGYM: A Procedural, Engine-Verified Benchmark for Structured Defeasible Reasoning

arXiv:2609.38409v1 Announce Type: new Abstract: Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable r...

By \.Ibrahim Ethem Deveci, Funda Tan \c{C}al{\i}k, Bar{\i}\c{s} Deniz Sa\u{g}lam, Duygu Ataman
llmsreinforcement-learningbenchmarks
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea