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arXiv Computation and Language

Improving Cross-Lingual Transfer for Sequential Sentence Classification in Research Papers via Structural Similarity

The paper investigates cross‑lingual transfer for sequential sentence classification (SSC) in research papers, focusing on 13 non‑English languages. Experiments show that linguistic proximity does not reliably predict transfer success, whereas structural similarity in rhetorical organization—particularly label distribution similarity—correlates positively with performance. The authors introduce three generative‑model methods that exploit structural cues, achieving parity with strong encoder baselines on‑domain and outperforming them when transferring to unseen languages.

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arXiv Computation and Language
Aug 27

Apples to Apples? Towards Comparable Crosslingual Language Model Evaluation

The paper investigates how to fairly compare language models across languages, noting that current evaluation methods vary widely and lack empirical validation. By training controlled monolingual models on parallel data and testing multilingual LLMs, the authors find that many normalized metrics suffer from biases due to tokenization, encoding, and orthographic differences. Instead, they recommend using sentence‑level negative log‑likelihood over semantically equivalent sequences for more reliable cross‑lingual comparisons.

By Xiulin Yang, Ethan Gotlieb Wilcox, Catherine Arnett
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arXiv Machine Learning
Sep 11

Structural priors for data-efficient language learning

The paper explores structural transfer, where models are first trained on non-language data such as music, probabilistic grammars, and cellular automata to induce priors for natural language tasks. This pretraining acts as a weight initialization for multilingual language modeling and leads to lower next-token prediction loss and smaller weight shifts during subsequent language training. However, the improved loss does not consistently translate into better downstream linguistic performance, and the efficiency of non-language data is lower than that of additional language data.

By Yana Veitsman, Jonas Mayer Martins, Jonathan Lautenschlager, Lisa Beinborn
benchmarks
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arXiv Computation and Language
Sep 1

Loci Similes: A Benchmark for Extracting Intertextualities in Latin Literature

The paper introduces Loci Similes, a benchmark for detecting intertextual links in Latin literature. It provides a curated dataset of about 176,000 text segments and 1,490 expert-verified parallels, including 945 labeled references from an existing source. Baselines for retrieval and classification are established using both lexical methods and pretrained encoder language models.

By Julian Schelb, Michael Wittweiler, Marie Revellio, Barbara Feichtinger, Andreas Spitz
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arXiv Computation and Language
Aug 28

Double Trouble: Bilingual Pretraining Leaves Language-Conditioned Effects in Shared-Language Representations

The study compares an English-only and a bilingual decoder-only model, each 310 M parameters, trained on eight diverse languages while controlling for English exposure, compute, and document overlap. After aligning on shared English vocabulary, the authors find that token embeddings appear similar, but the deeper hidden states used for prediction diverge across models. This hidden‑state mismatch grows through middle transformer layers and persists despite controls, indicating that contextual processing differs between the models. "whyItMatters":"The findings show that embedding alignment can conceal significant internal representation differences, which is crucial for any downstream work that assumes aligned multilingual models are interchangeable."

By Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos
llmsragsafety
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