Leshem (Legend) Choshen 🤖🤗 on Twitter / X

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via...

Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptation is generally...

Targeted Multilingual Adaptation for Low-resource Language Families

The "massively-multilingual" training of multilingual models is known to limit their utility in any one language, and they perform particularly poorly on low-resource languages. However, there is evid

Language model harnesses are compositional generalizers

Harnesses can lead to compositional generalization: we observe a property in training RLMs, in which similarly structured tasks are viewed as isomorphic and all individual LM calls in the harness become in-distribution.

Models overview

Claude is a family of state-of-the-art large language models developed by Anthropic. This guide introduces the available models and compares their performance.

Racket

(define translations #hash([Chinese . "你好 世界"] [English . "Hello world"] [French . "Bonjour le monde"] [German . "Hallo Welt"] [Greek . "Γειά σου, κόσμε"] [Portuguese . "Olá mundo"] [Spanish . "Hola mundo"] [Thai . "สวัสดีชาวโลก"] [Turkish . "Merhaba Dünya"]))

False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models

Subword tokenizers trained on multilingual corpora naturally produce overlapping tokens across languages. Does token overlap facilitate cross-lingual transfer or instead introduce interference between languages? Prior work offers mixed evidence, partly due to varied setups and confounders, such as token frequency or subword segmentation granularity. To address this question, we devise a controlled experiment where we train bilingual autoregressive models on multiple language pairs under systematically varied vocabulary overlap settings. Crucially, we explore a new dimension to understanding how overlap affects transfer: the semantic similarity of tokens shared across languages. We first analyze our models' hidden representations and find that overlap of any kind creates embedding spaces that capture cross-lingual semantic relationships, while this effect is much weaker in models with disjoint vocabularies. On XNLI and XQuAD, we find that models with overlap outperform models with disjoint vocabularies, and that transfer performance generally improves as overlap increases. Overall, our findings highlight the advantages of token overlap in multilingual models and show that substantial shared vocabulary remains a beneficial design choice for multilingual tokenizers.

How linguistics learned to stop worrying and love the language models

Language models (LMs) can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.

Wiki-40B: Multilingual Language Model Dataset

We propose a new multilingual language model benchmark that is composed of 40+ languages spanning several scripts and linguistic families. With around 40 billion characters, we hope this new resource will accelerate the research of multilingual modeling. We train monolingual causal language models using a state-of-the-art model (Transformer-XL) establishing baselines for many languages. We also introduce the task of multilingual causal language modeling where we train our model on the combined text of 40+ languages from Wikipedia with different vocabulary sizes and evaluate on the languages individually. We released the cleaned-up text of 40+ Wikipedia language editions, the corresponding trained monolingual language models, and several multilingual language models with different fixed vocabulary sizes.

Cross-Lingual Exploration for Parametric Knowledge | Idan Szpektor

Accepted to EMNLP Findings! While Large Language Models encode vast amounts of multicultural and factual information, this parametric knowledge is often unevenly accessible across languages. Standard inference frequently fails to surface localized facts, creating persistent gaps in cross-lingual knowledge transfer and consistency. We address this challenge in our paper "Cross-Lingual Exploration for Parametric Knowledge" (https://lnkd.in/drf36bV7), a collaboration between Elisha Diskind, Itamar Trainin, and Omri Abend from The Hebrew University of Jerusalem, Leshem Choshen from the Weizmann Institute of Science, alongside Uri Shaham and myself from our Google Research IL group. We formalize cross-lingual exploration as a structured search process across four core dimensions: language selection, exploration routing, answer aggregation, and inference budget. Evaluating across 17 typologically diverse languages on the ECLeKTic and CLIKE benchmarks, we show that allowing models to autonomously navigate alternative linguistic paths yields up to a 21% gain in knowledge transfer and a 16% boost in factual recall over native baselines, surpassing both standard English-pivot routing and native-language reasoning. Crucially, cross-lingual exploration defines a significantly more efficient compute Pareto frontier than scaling within the native query language, while driving intrinsic cross-lingual consistency gains beyond what accuracy improvements alone explain. Technically, this moves the needle for multilingual inference and knowledge elicitation pipelines, demonstrating that strategic language switching is a powerful, training-free mechanism for unlocking latent parametric knowledge.

Rethinking the Multilingual Reasoning Gap with Layer Swap

Recent reasoning Large Language Models produce a chain-of-thought (CoT) predominantly in English, even when prompted in non-English languages. Prior work suggests that forcing the CoT to remain in the input language (\emph{native reasoning}) substantially degrades performance relative to allowing the model to reason in English before answering in the input language (\emph{English-pivoted reasoning}). However, most studies of this native reasoning gap rely on inference-time interventions or limited native-language training data. We revisit this comparison at a larger scale and under comparable supervision. We construct long multilingual reasoning datasets across six languages (English, French, German, Spanish, Chinese and Swahili); fine-tune specialists in both native and English-pivoted regimes on top of \texttt{Qwen/Qwen3-8B-Base}, and evaluate across mathematics, science, general knowledge, and code. In this setting, the average native reasoning gap shrinks to 1.9--3.5\% across the five non-English languages, considerably smaller than previously reported. Weight-space analysis of the native specialists reveals aligned fine-tuning updates in the middle layers and divergence in the outer layers. This points to a largely language-agnostic reasoning core surrounded by language-specific layers. Exploiting this structure, we introduce a Layer Swap: transferring the English specialist's stronger reasoning mid-layers into each native specialist, closing most of the native reasoning gap across the five non-English languages while preserving CoT in the target language. We release all models and datasets.

Extracting Training Data from Large Language Models

Nicholas Carlini, Google; Florian Tramèr, Stanford University; Eric Wallace, UC Berkeley; Matthew Jagielski, Northeastern University; Ariel Herbert-Voss, OpenAI and Harvard University; Katherine Lee and Adam Roberts, Google; Tom Brown, OpenAI; Dawn Song, UC Berkeley; Úlfar Erlingsson, Apple; Alina Oprea, Northeastern University; Colin Raffel, Google

elvis on Twitter / X

Small Language Models are the Future of Agentic AILots to gain from building agentic systems with small language models.Capabilities are increasing rapidly!AI devs should be exploring SLMs.Here are my notes: pic.twitter.com/7dhmz9V2jB— elvis (@omarsar0) July 1, 2025

Zed now predicts your next edit with Zeta, our new open model

From the Zed Blog: A tool that predicts your next move. Powered by Zeta, our new open-source, open-data language model.