MELA: Multilingual Evaluation of Linguistic Acceptability

• URL: http://arxiv.org/abs/2311.09033v3
• Date: Thu, 6 Jun 2024 12:31:51 GMT
• Title: MELA: Multilingual Evaluation of Linguistic Acceptability
• Authors: Ziyin Zhang, Yikang Liu, Weifang Huang, Junyu Mao, Rui Wang, Hai Hu,
• Abstract summary: We present the largest benchmark to date on linguistic acceptability: Multilingual Evaluation of Linguistic Acceptability -- MELA, with 46K samples covering 10 languages. In pursuit of multilingual interpretability, we conduct probing experiments with fine-tuned XLM-R. Cross-lingual transfer experiments show that transfer in acceptability judgment is non-trivial.
• Score: 7.524375463656369
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, we present the largest benchmark to date on linguistic acceptability: Multilingual Evaluation of Linguistic Acceptability -- MELA, with 46K samples covering 10 languages from a diverse set of language families. We establish LLM baselines on this benchmark, and investigate cross-lingual transfer in acceptability judgements with XLM-R. In pursuit of multilingual interpretability, we conduct probing experiments with fine-tuned XLM-R to explore the process of syntax capability acquisition. Our results show that GPT-4o exhibits a strong multilingual ability, outperforming fine-tuned XLM-R, while open-source multilingual models lag behind by a noticeable gap. Cross-lingual transfer experiments show that transfer in acceptability judgment is non-trivial: 500 Icelandic fine-tuning examples lead to 23 MCC performance in a completely unrelated language -- Chinese. Results of our probing experiments indicate that training on MELA improves the performance of XLM-R on syntax-related tasks. Our data is available at https://github.com/sjtu-compling/MELA.

Related papers

• M-Prometheus: A Suite of Open Multilingual LLM Judges [64.22940792713713]
  We introduce M-Prometheus, a suite of open-weight LLM judges that can provide both direct assessment and pairwise comparison feedback on multilingual outputs. M-Prometheus models outperform state-of-the-art open LLM judges on multilingual reward benchmarks spanning more than 20 languages, as well as on literary machine translation (MT) evaluation covering 4 language pairs.
  arXiv  Detail & Related papers  (2025-04-07T11:37:26Z)
• MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation [60.52580061637301]
  MMLU-ProX is a comprehensive benchmark covering 13 typologically diverse languages with approximately 11,829 questions per language. We evaluate 25 state-of-the-art large language models (LLMs) using 5-shot chain-of-thought (CoT) and zero-shot prompting strategies, analyzing their performance across linguistic and cultural boundaries. Our experiments reveal consistent performance degradation from high-resource languages to lower-resource ones, with the best models achieving over 70% accuracy on English but dropping to around 40% for languages like Swahili.
  arXiv  Detail & Related papers  (2025-03-13T15:59:20Z)
• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models [53.9835961434552]
  We introduce the Chinese Instruction-Following Benchmark (CIF-Bench) to evaluate the generalizability of large language models (LLMs) to the Chinese language. CIF-Bench comprises 150 tasks and 15,000 input-output pairs, developed by native speakers to test complex reasoning and Chinese cultural nuances. To mitigate data contamination, we release only half of the dataset publicly, with the remainder kept private, and introduce diversified instructions to minimize score variance.
  arXiv  Detail & Related papers  (2024-02-20T16:02:12Z)
• Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages [51.301942056881146]
  We investigate how large language models (LLMs) function as rerankers in cross-lingual information retrieval systems for African languages. Our implementation covers English and four African languages (Hausa, Somali, Swahili, and Yoruba) We examine cross-lingual reranking with queries in English and passages in the African languages.
  arXiv  Detail & Related papers  (2023-12-26T18:38:54Z)
• TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes [9.254047358707014]
  We introduce the Multilingual Instruction-Tuning dataset (MITS), comprised of Alpaca-52K, Dolly-15K, and Vicuna Benchmark translations into 132 languages. Secondly, we propose a new method called emphTaCo: Translation-Assisted Cross-Linguality, which utilizes translations in a chain-of-thought process to instruction-tune LLMs on new languages through a curriculum-learning process. Our results indicate that the TaCo method impresses GPT-4 with an 82% score for a low-resource language in the Vicuna Benchmark dataset, doubling the performance in contrast to instruction tuning
  arXiv  Detail & Related papers  (2023-11-17T06:55:32Z)
• Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations [59.056367787688146]
  This paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. We construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages. By utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages.
  arXiv  Detail & Related papers  (2023-10-31T08:09:20Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM [8.858671209228536]
  We focus on BLOOM's multilingual ability by evaluating its machine translation performance across several datasets. We study several aspects including prompt design, model sizes, cross-lingual transfer and the use of discursive context.
  arXiv  Detail & Related papers  (2023-03-03T13:23:42Z)
• Probing Multilingual Language Models for Discourse [0.0]
  We find that the XLM-RoBERTa family of models consistently show the best performance. Our results also indicate that model distillation may hurt the ability of cross-lingual transfer of sentence representations.
  arXiv  Detail & Related papers  (2021-06-09T06:34:21Z)
• AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages [75.08199398141744]
  We present AmericasNLI, an extension of XNLI (Conneau et al.), to 10 indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. We find that XLM-R's zero-shot performance is poor for all 10 languages, with an average performance of 38.62%.
  arXiv  Detail & Related papers  (2021-04-18T05:32:28Z)

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.