arXiv AI

The Importance of Being Statistically Earnest: A Critical Re-evaluation of GSM-Symbolic

The paper re‑examines the GSM‑Symbolic benchmark, arguing that its claim of widespread reasoning failures in 25 LLMs is based on weak statistics. Using bootstrapped Generalised Linear Mixed Models on 20 open‑weight models, only eight show significant performance changes, and a systematic shift toward larger integers in the dataset explains many of these effects. The authors also uncover model‑specific failure modes such as variable binding fragility, arithmetic limits, and dual‑task interference, cautioning against blanket conclusions about LLM reasoning.

arXiv AI

Oct 5

Multilingual GSM-Symbolic: What determines capability transfer across languages?

The paper introduces Multilingual GSM‑Symbolic, a large multilingual math dataset with 30,000 question‑answer pairs across 15 languages, designed to evaluate cross‑lingual capability transfer using symbolic templates that generate millions of variations. It quantifies key determinants of transfer—model size, language resource level, reasoning ability, and typological distance—showing that larger models and better reasoning reduce performance gaps between low‑ and high‑resource languages, while typological distance has limited impact. The authors present a predictive framework that explains 92 % of language‑to‑language variation and can estimate unseen language performance within 6 pp, improving to 4.2 pp with only ten target‑language templates.

By Kenneth Enevoldsen, Riley Herchert, Sofie Mosegaard, Dan Saattrup Smart, Simon Enni, Isaac Chung, Sofie Bruun, Ayush Sunil Munot, Max M\"uller-Eberstein, Adnan El-Assadi, Elisa Bassignana, Gianluca Barmina, Hafsteinn Einarsson, Iben Nyholm Debess, Linda Freienthal, Lukas Galke Poech, Mike Zhang, Nicolas Legrand, Vladimir Salnikov, Yevhen Kostiuk, Zafar Hussain, Sagandeep Kaur, Agnes Toftg{\aa}rd, Marie Mattson, Kristoffer Nielbo

arXiv AI

Sep 30

MGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation

arXiv:2601.21225v4 Announce Type: replace-cross Abstract: Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation ha...

By Tianyi Xu, Kosei Uemura, Alfred Malengo Kondoro, Tadesse Destaw Belay, Catherine Nana Nyaah Essuman, Ifeoma Okoh, Ganiyat Afolabi, Ayodele Awokoya, David Ifeoluwa Adelani

llms benchmarks

Hugging Face Trending Papers

Jul 14

GSM-Plus-BN: A Perturbation-Based Benchmark for Bangla Mathematical Reasoning in Large Language Models

The evaluation of mathematical reasoning in large language models (LLMs) has predominantly focused on high-resource languages like English. This has created a significant barrier to the equitable development and deployment of AI in linguistically diverse regions such as Bangladesh, where over 230 million people speak Bengali.

llms benchmarks

arXiv AI

3d ago

Prompt PerturbAItions: Mechanisms of LLM Response Inconsistencies

arXiv:2605.30913v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed in conversational settings where user phrasing ranges from polite to casual to toxic,...

By Soorya Ram Shimgekar, Agam Goyal, Amruta Parulekar, Joshua Chen, Yian Wang, Navin Kumar, Hari Sundaram, Eshwar Chandrasekharan, Koustuv Saha

llms benchmarks safety

arXiv AI

Jul 22

Structured Synthetic Reasoning Data for Arithmetic Fine-Tuning of Small Language Models

arXiv:2607. 18266v1 Announce Type: new Abstract: Small language models are attractive for local deployment, but they often struggle with multi-step arithmetic reasoning.

By Jake O'Grady, Effirul Ramlan

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 2

Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs

arXiv:2509. 17314v4 Announce Type: replace-cross Abstract: Software increasingly relies on the emergent capabilities of Large Language Models (LLMs), from natural language understanding to program analysis and generation.

By Juyeon Yoon, Somin Kim, Robert Feldt, Shin Yoo

llms benchmarks

arXiv AI

Aug 25

More Accurate or More Efficient? Evaluating Locally Deployed Compact Open-Weight Language Models for Mathematical Reasoning

arXiv:2608.22048v1 Announce Type: new Abstract: Large language models are increasingly deployed on local hardware for privacy, cost, and accessibility reasons. Yet many evaluations emphasize accuracy...

By Orion Powers, Daniella Seum, Khaled Slhoub

llms safety

arXiv AI

Aug 25

Measuring Stability and Failure Behavior in Language Models Under Structured Perturbations

arXiv:2608.22138v1 Announce Type: new Abstract: Language models are usually judged by a single accuracy score, which does not reveal how their performance degrades as inputs are perturbed. We present...

By Samira Golsefid

llms

arXiv AI

Jul 14

Valid $\ne$ Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought

arXiv:2607. 11266v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of Large Language Models (LLMs), yet it often incurs substantial computational costs due to over-reasoning: the generation of redundant, verbose, or irrelevant steps.

By Daeyeop Lee, Hwanjo Yu

llms efficiency benchmarks

arXiv AI

Jun 3

Testing LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks

arXiv:2606. 03606v1 Announce Type: cross Abstract: Large language models achieve strong performance on arithmetic reasoning benchmarks, and one common response to arithmetic brittleness is to delegate computation to code.

By Malia Barker, Bishal Lakha, Edoardo Serra, Francesco Gullo

llms benchmarks

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

llms nlp benchmarks

Hugging Face Trending Papers

Jun 22

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models

Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for ensuring predictive credibility. While traditional uncertainty taxonomy paradigms, such as the dichotomy of aleatoric and epistemic uncertainties, provide conceptual foundations, they often fail to capture the multi-component and multi-stage nature of LLM generation and struggle to evaluate the effectiveness of various Uncertainty Quantification (UQ) methods.

llms benchmarks

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