Tootfinder

ReCoVeR the Target Language: Language Steering without Sacrificing Task Performance
Hannah Sterz, Fabian David Schmidt, Goran Glava\v{s}, Ivan Vuli\'c
https://arxiv.org/abs/2509.14814

ReCoVeR the Target Language: Language Steering without Sacrificing Task Performance
As they become increasingly multilingual, Large Language Models (LLMs) exhibit more language confusion, i.e., they tend to generate answers in a language different from the language of the prompt or the answer language explicitly requested by the user. In this work, we propose ReCoVeR (REducing language COnfusion in VEctor Representations), a novel lightweight approach for reducing language confusion based on language-specific steering vectors. We first isolate language vectors with the help of…

LA-based Lingokids, an interactive learning app for kids aged 2-8, raised $120M led by Bullhound Capital and General Catalyst to help expand its IP partnerships (Dean Takahashi/GamesBeat)
https://gamesbeat.com/lingokids-raises-120m-for-interactive-learning-…

Lingokids raises $120M for interactive learning app for kids
Lingokids, the top interactive app for kids ages two to eight with more than 185 million families worldwide, has raised $120 million.

Code Less to Code More: Streamlining Language Server Protocol and Type System Development for Language Families
Federico Bruzzone, Walter Cazzola, Luca Favalli
https://arxiv.org/abs/2509.15150

Code Less to Code More: Streamlining Language Server Protocol and Type System Development for Language Families
Developing editing support for $L$ languages in $E$ editors is complex and time-consuming. Some languages do not provide dedicated editors, while others offer a single native editor. The $\textit{language server protocol}$ (LSP) reduces the language-editor combinations $L \times E$ to $L + E$, where a single language server communicates with editors via LSP plugins. However, overlapping implementations of linguistic components remain an issue. Existing language workbenches struggle with modular…

Programming language tradeoffs.
https://www.garfieldtech.com/blog/language-tradeoffs

Meta rolls out Hyperscape Capture in early access, letting developers and creators use Quest 3 devices to scan a room and turn it into a VR space in minutes (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/09/17/meta-launches-hyper…

Meta launches Hyperscape, technology to turn real-world spaces into VR | TechCrunch
Meta launches Hyperscape into beta, allowing users to turn real world spaces into digital spaces for VR.

UNVEILING: What Makes Linguistics Olympiad Puzzles Tricky for LLMs?
Mukund Choudhary, KV Aditya Srivatsa, Gaurja Aeron, Antara Raaghavi Bhattacharya, Dang Khoa Dang Dinh, Ikhlasul Akmal Hanif, Daria Kotova, Ekaterina Kochmar, Monojit Choudhury
https://arxiv.org/abs/2508.11260

UNVEILING: What Makes Linguistics Olympiad Puzzles Tricky for LLMs?
Large language models (LLMs) have demonstrated potential in reasoning tasks, but their performance on linguistics puzzles remains consistently poor. These puzzles, often derived from Linguistics Olympiad (LO) contests, provide a minimal contamination environment to assess LLMs' linguistic reasoning abilities across low-resource languages. This work analyses LLMs' performance on 629 problems across 41 low-resource languages by labelling each with linguistically informed features to unveil weakne…

Large Language Model probabilities cannot distinguish between possible and impossible language
Evelina Leivada, Raquel Montero, Paolo Morosi, Natalia Moskvina, Tamara Serrano, Marcel Aguilar, Fritz Guenther
https://arxiv.org/abs/2509.15114

Large Language Model probabilities cannot distinguish between possible and impossible language
A controversial test for Large Language Models concerns the ability to discern possible from impossible language. While some evidence attests to the models' sensitivity to what crosses the limits of grammatically impossible language, this evidence has been contested on the grounds of the soundness of the testing material. We use model-internal representations to tap directly into the way Large Language Models represent the 'grammatical-ungrammatical' distinction. In a novel benchmark, we elicit…

Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG
Dayeon Ki, Marine Carpuat, Paul McNamee, Daniel Khashabi, Eugene Yang, Dawn Lawrie, Kevin Duh
https://arxiv.org/abs/2509.13930

Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG
Multilingual Retrieval-Augmented Generation (mRAG) systems enable language models to answer knowledge-intensive queries with citation-supported responses across languages. While such systems have been proposed, an open questions is whether the mixture of different document languages impacts generation and citation in unintended ways. To investigate, we introduce a controlled methodology using model internals to measure language preference while holding other factors such as document relevance c…

Can Large Language Models Robustly Perform Natural Language Inference for Japanese Comparatives?
Yosuke Mikami, Daiki Matsuoka, Hitomi Yanaka
https://arxiv.org/abs/2509.13695 ht…

Can Large Language Models Robustly Perform Natural Language Inference for Japanese Comparatives?
Large Language Models (LLMs) perform remarkably well in Natural Language Inference (NLI). However, NLI involving numerical and logical expressions remains challenging. Comparatives are a key linguistic phenomenon related to such inference, but the robustness of LLMs in handling them, especially in languages that are not dominant in the models' training data, such as Japanese, has not been sufficiently explored. To address this gap, we construct a Japanese NLI dataset that focuses on comparative…

LinguaSafe: A Comprehensive Multilingual Safety Benchmark for Large Language Models
Zhiyuan Ning, Tianle Gu, Jiaxin Song, Shixin Hong, Lingyu Li, Huacan Liu, Jie Li, Yixu Wang, Meng Lingyu, Yan Teng, Yingchun Wang
https://arxiv.org/abs/2508.12733

LinguaSafe: A Comprehensive Multilingual Safety Benchmark for Large Language Models
The widespread adoption and increasing prominence of large language models (LLMs) in global technologies necessitate a rigorous focus on ensuring their safety across a diverse range of linguistic and cultural contexts. The lack of a comprehensive evaluation and diverse data in existing multilingual safety evaluations for LLMs limits their effectiveness, hindering the development of robust multilingual safety alignment. To address this critical gap, we introduce LinguaSafe, a comprehensive multi…