Local Structure Matters Most in Most Languages

• URL: http://arxiv.org/abs/2211.05025v1
• Date: Wed, 9 Nov 2022 16:58:44 GMT
• Title: Local Structure Matters Most in Most Languages
• Authors: Louis Clou\^atre and Prasanna Parthasarathi and Amal Zouaq and Sarath Chandar
• Abstract summary: We replicate a study on the importance of local structure, and the relative unimportance of global structure, in a multilingual setting. We find that the phenomenon observed on the English language broadly translates to over 120 languages.
• Score: 15.870989191524094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many recent perturbation studies have found unintuitive results on what does and does not matter when performing Natural Language Understanding (NLU) tasks in English. Coding properties, such as the order of words, can often be removed through shuffling without impacting downstream performances. Such insight may be used to direct future research into English NLP models. As many improvements in multilingual settings consist of wholesale adaptation of English approaches, it is important to verify whether those studies replicate or not in multilingual settings. In this work, we replicate a study on the importance of local structure, and the relative unimportance of global structure, in a multilingual setting. We find that the phenomenon observed on the English language broadly translates to over 120 languages, with a few caveats.

Related papers

• The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
  Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
  arXiv  Detail & Related papers  (2025-06-11T16:00:54Z)
• When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners [111.50503126693444]
  We show that language-specific ablation consistently boosts multilingual reasoning performance.<n>Compared to post-training, our training-free ablation achieves comparable or superior results with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-05-21T08:35:05Z)
• On the Acquisition of Shared Grammatical Representations in Bilingual Language Models [6.266732217239363]
  We ask what happens to a monolingual language model when it begins to be trained on a second language. To find evidence of shared multilingual representations, we turn to structural priming, a method used to study grammatical representations in humans. We argue that this asymmetry may shape hypotheses about human structural priming effects.
  arXiv  Detail & Related papers  (2025-03-05T23:27:58Z)
• The Roles of English in Evaluating Multilingual Language Models [6.396057276543912]
  We argue that these roles have different goals: task performance versus language understanding. We recommend to move away from this imprecise method and instead focus on furthering language understanding.
  arXiv  Detail & Related papers  (2024-12-11T14:02:55Z)
• Investigating Language-Specific Calibration For Pruning Multilingual Large Language Models [11.421452042888523]
  We compare different calibration languages for pruning multilingual models across diverse languages, tasks, models, and SotA pruning techniques. Our results offer practical suggestions, for example, calibrating in the target language can efficiently retain the language modeling capability but does not necessarily benefit downstream tasks.
  arXiv  Detail & Related papers  (2024-08-26T16:29:13Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We find that Llama Instruct and Mistral models exhibit high degrees of language confusion. We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models [79.46179534911019]
  Large language models (LLMs) have demonstrated multilingual capabilities; yet, they are mostly English-centric due to imbalanced training corpora. This work extends the evaluation from NLP tasks to real user queries. For culture-related tasks that need deep language understanding, prompting in the native language tends to be more promising.
  arXiv  Detail & Related papers  (2024-03-15T12:47:39Z)
• Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models [12.700783525558721]
  English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks. This paper introduces the decomposed prompting approach to probe the linguistic structure understanding of these LLMs in sequence labeling tasks.
  arXiv  Detail & Related papers  (2024-02-28T15:15:39Z)
• Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed? [40.13166574854085]
  We investigate the minimal amount of multilinguality required to elicit cross-lingual generalisation in English-centric large language models. We find that multilingual instruction tuning with as few as two to three languages is both necessary and sufficient to elicit effective cross-lingual generalisation.
  arXiv  Detail & Related papers  (2023-12-20T00:49:52Z)
• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models [23.62852626011989]
  We show that grammatical structures in higher-resource languages bleed into lower-resource languages. We show this bias via a novel method for comparing the fluency of multilingual models to the fluency of monolingual Spanish and Greek models.
  arXiv  Detail & Related papers  (2022-10-11T17:06:38Z)
• Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models [73.11488464916668]
  This study investigates the dynamics of the multilingual pretraining process. We probe checkpoints taken from throughout XLM-R pretraining, using a suite of linguistic tasks. Our analysis shows that the model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones.
  arXiv  Detail & Related papers  (2022-05-24T03:35:00Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• Analysing The Impact Of Linguistic Features On Cross-Lingual Transfer [3.299672391663527]
  We analyze a state-of-the-art multilingual model and try to determine what impacts good transfer between languages. We show that looking at particular syntactic features is 2-4 times more helpful in predicting the performance than an aggregated syntactic similarity.
  arXiv  Detail & Related papers  (2021-05-12T21:22:58Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.