Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners

• URL: http://arxiv.org/abs/2405.13816v2
• Date: Tue, 18 Jun 2024 16:30:01 GMT
• Title: Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners
• Authors: Shimao Zhang, Changjiang Gao, Wenhao Zhu, Jiajun Chen, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang,
• Abstract summary: Large Language Models (LLMs) have shown impressive language capabilities. In this work, we investigate the spontaneous multilingual alignment improvement of LLMs. We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
• Score: 67.85635044939836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Large Language Models (LLMs) have shown impressive language capabilities. While most of the existing LLMs have very unbalanced performance across different languages, multilingual alignment based on translation parallel data is an effective method to enhance the LLMs' multilingual capabilities. In this work, we discover and comprehensively investigate the spontaneous multilingual alignment improvement of LLMs. We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages, even including those unseen during instruction-tuning. Additionally, we utilize different settings and mechanistic interpretability methods to analyze the LLM's performance in the multilingual scenario comprehensively. Our work suggests that LLMs have enormous potential for improving multilingual alignment efficiently with great language and task generalization.

Related papers

• 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)
• Towards Truthful Multilingual Large Language Models: Benchmarking and Alignment Strategies [38.3269908062146]
  We construct a benchmark for truthfulness evaluation in multilingual scenarios. We propose Fact-aware Multilingual Selective Synergy (FaMSS) to optimize the data allocation across a large number of languages.
  arXiv  Detail & Related papers  (2024-06-20T15:59:07Z)
• 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)
• UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset [69.33424532827608]
  Open-source large language models (LLMs) have gained significant strength across diverse fields. In this work, we construct an open-source multilingual supervised fine-tuning dataset. The resulting UltraLink dataset comprises approximately 1 million samples across five languages.
  arXiv  Detail & Related papers  (2024-02-07T05:05:53Z)
• How Vocabulary Sharing Facilitates Multilingualism in LLaMA? [19.136382859468693]
  Large Language Models (LLMs) often show strong performance on English tasks, while exhibiting limitations on other languages. This study endeavors to examine the multilingual capability of LLMs from the vocabulary sharing perspective.
  arXiv  Detail & Related papers  (2023-11-15T16:13:14Z)
• Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback [61.83548032416181]
  We present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
  arXiv  Detail & Related papers  (2023-07-29T18:01:46Z)
• Breaking Language Barriers with a LEAP: Learning Strategies for Polyglot LLMs [5.682384717239095]
  Large language models (LLMs) are at the forefront of transforming numerous domains globally. This paper tackles the imperative challenge of enhancing the multilingual performance of LLMs. We present novel techniques that unlock the true potential of LLMs in a polyglot landscape.
  arXiv  Detail & Related papers  (2023-05-28T14:48:38Z)
• Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions [68.01449013641532]
  Large-scale Pretrained Language Models (LLMs) have shown strong abilities in multilingual translations. We present a detailed analysis by finetuning a multilingual pretrained language model, XGLM-7B, to perform multilingual translation.
  arXiv  Detail & Related papers  (2023-05-24T12:00:24Z)
• Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs [16.770697902481107]
  Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities. We propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings. The results show that GPT exhibits highly translating-like behaviour in multilingual settings.
  arXiv  Detail & Related papers  (2023-05-24T02:05:03Z)

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.