arXiv Computation and Language

Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts

The paper investigates why large reasoning language models struggle to transfer parametric knowledge across different scripts. Through observational data and regression analysis on ECLeKTic and MultiLoKo datasets, the authors find that script mismatch—not language family—is the main predictor of transfer failure when controlling for model capability and question difficulty. By providing key entities in the source language and training models to reason about transliteration ambiguities, they demonstrate a reduction in the cross‑script transfer gap, suggesting that post‑training improvements can enhance cross‑lingual knowledge transfer.

arXiv Computation and Language

Aug 27

Rethinking the Multilingual Reasoning Gap with Layer Swap

The study investigates the performance gap between native-language reasoning and English-pivoted reasoning in large language models. By creating extensive multilingual reasoning datasets and fine‑tuning specialists on Qwen/Qwen3-8B-Base, the authors find that the native reasoning gap is much smaller (1.9–3.5%) than previously reported. They analyze weight‑space changes, discover a language‑agnostic reasoning core in the middle layers, and propose a Layer Swap technique that transfers these mid‑layer updates from an English specialist to native specialists, effectively closing most of the gap while maintaining native chain‑of‑thought output.

By Maxence Lasbordes, Am\'elie Chatelain, Djam\'e Seddah

llms fine-tuning

arXiv Computation and Language

Sep 30

Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch

arXiv:2604.15490v2 Announce Type: replace Abstract: Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical...

By Eleanor M. Lin, David Jurgens

llms fine-tuning

arXiv Computation and Language

Sep 10

Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning

arXiv:2609.10445v1 Announce Type: new Abstract: Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: m...

By Mehrnaz Mofakhami, Ananya Sahu, Alejandro R. Salamanca, Daniel D'souza, Alexandre Berard, Thomas Euyang, Marzieh Fadaee, Julia Kreutzer

llms fine-tuning benchmarks

arXiv AI

Aug 14

Gradual Code-Switching as Inference-Time Cross-Lingual Representational Alignment for LLMs

arXiv:2510. 05678v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have achieved notable progress in multilingual settings, their performance remains uneven across languages as LLMs often rely on English-centric latent representations.

By Haneul Yoo, Jiho Jin, Kyunghyun Cho, Alice Oh

llms safety

arXiv Machine Learning

Aug 4

Native Multilingual Chain-of-Thought Reasoning in Low-Resource Southeast Asian Languages

arXiv:2608. 00533v1 Announce Type: cross Abstract: Large Language Models have achieved substantial progress in reasoning capabilities.

By Sean Gip Lim, William Chandra Tjhi, Hai Leong Chieu

llms rag agents fine-tuning efficiency benchmarks safety

arXiv AI

Jul 8

Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs

arXiv:2607. 06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English.

By Andrea Alfarano, Andrea Bacciu, Saab Mansour, Amin Mantrach, Marcello Federico

llms rag

arXiv AI

Sep 7

Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

The paper surveys efficient reasoning in large language models, contrasting fast intuitive (System 1) and slow deep (System 2) reasoning. It analyzes why System 2 is computationally costly yet more accurate, and why System 1 is efficient but less effective. The survey covers causes of inefficiency, patterns of reasoning behavior, and potential solutions to balance performance and computational budgets, offering actionable insights and an open‑source repository for ongoing research.

By Rui Wang, Hongru Wang, Boyang Xue, Jianhui Pang, Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong, Heng Ji, Kam-Fai Wong

llms

arXiv Computation and Language

Sep 17

PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics

arXiv:2505.23126v5 Announce Type: replace Abstract: Although many benchmarks evaluate the reasoning abilities of Large Language Models (LLMs) within domains such as mathematics, coding, or data wrang...

By Atharva Naik, Prakam, Yash Mathur, Darsh Agrawal, Manav Kapadnis, Yuwei An, Clayton Marr, Carolyn Rose, David Mortensen

llms benchmarks

Hugging Face Trending Papers

Jun 25

Multilingual Reasoning Cascades Need More Context

Translation cascades for reasoning translate the query from another language to English, reason in English, and translate the answer back to the original language. This is a competitive approach to multilingual reasoning, but structurally lossy, since each stage discards information later stages may need, including cues for cultural grounding, register, and disambiguation.

nlp benchmarks

arXiv AI

Jun 18

Rethinking Cross-lingual Gaps from a Statistical Viewpoint

arXiv:2510. 15551v2 Announce Type: replace-cross Abstract: Any piece of knowledge is usually expressed in one or a handful of natural languages on the web or in any large corpus.

By Vihari Piratla, Purvam Jain, Darshan Singh, Trevor Cohn, Preethi Jyothi, Partha Talukdar

llms

arXiv AI

Sep 1

Do Language Models Reason Across Languages?

The paper investigates whether language models can reason across languages by introducing a two‑hop question answering task that requires inference over two multilingual documents. Results show that models are more sensitive to language variation in answer‑span documents than in bridging documents, and that up to 33% of multilingual cases involve correct final answers despite failing to infer bridging information in the first step. The study also reveals an 18% composition failure rate and proposes a three‑stage SUBQ prompting method that improves accuracy from 10.1% to 66.5%.

By Yan Meng, Wafaa Mohammed, Christof Monz

llms nlp

arXiv AI

Aug 18

LLMs Get Smarter from Targeted Synthetic Multilingual Data

arXiv:2608. 15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt.

By Ishika Agarwal, Arkajyoti Charaborty, Tanner Sorensen, Neha Gupta, Andreas Stolcke

llms fine-tuning

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