Why Not Transform Chat Large Language Models to Non-English?

• URL: http://arxiv.org/abs/2405.13923v2
• Date: Fri, 31 May 2024 07:22:45 GMT
• Title: Why Not Transform Chat Large Language Models to Non-English?
• Authors: Xiang Geng, Ming Zhu, Jiahuan Li, Zhejian Lai, Wei Zou, Shuaijie She, Jiaxin Guo, Xiaofeng Zhao, Yinglu Li, Yuang Li, Chang Su, Yanqing Zhao, Xinglin Lyu, Min Zhang, Jiajun Chen, Hao Yang, Shujian Huang,
• Abstract summary: The scarcity of non-English data limits the development of non-English large language models (LLMs) TransLLM divides the transfer problem into some common sub-tasks with the translation chain-of-thought. Our method, using only single-turn data, outperforms strong baselines and ChatGPT on multi-turn benchmark MT-bench.
• Score: 57.16587777261422
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The scarcity of non-English data limits the development of non-English large language models (LLMs). Transforming English-centric LLMs to non-English has been identified as an effective and resource-efficient method. Previous works start from base LLMs and perform knowledge distillation (KD) with data generated by stronger LLMs, e.g. GPT-4. Compared to base LLMs, chat LLMs are further optimized for advanced abilities, e.g. multi-turn conversation and human preference alignment, and thus more powerful in both helpfulness and safety. However, transforming a chat LLM involves two critical issues: (1) How can we effectively transfer advanced abilities without their supervised data? (2) How can we prevent the original knowledge from catastrophic forgetting during transformation? We target these issues by introducing a simple framework called TransLLM. For the first issue, TransLLM divides the transfer problem into some common sub-tasks with the translation chain-of-thought, which uses the translation as the bridge between English and non-English step-by-step. We further enhance the performance of sub-tasks with publicly available data. For the second issue, we propose a method comprising two synergistic components: low-rank adaptation for training to maintain the original LLM parameters, and recovery KD, which utilizes data generated by the chat LLM itself to recover the original knowledge from the frozen parameters. In the experiments, we transform the LLaMA-2-chat-7B to the Thai language. Our method, using only single-turn data, outperforms strong baselines and ChatGPT on multi-turn benchmark MT-bench. Furthermore, our method, without safety data, rejects more harmful queries of safety benchmark AdvBench than both ChatGPT and GPT-4.

Related papers

• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at various English tasks. Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model [14.92282077647913]
  Continual fine-tuning (CFT) is the process of sequentially fine-tuning an LLM to enable the model to adapt to downstream tasks. We study a two-phase CFT process in which an English-only end-to-end fine-tuned LLM is sequentially fine-tuned on a multilingual dataset. We observe that the similarity'' of Phase 2 tasks with Phase 1 determines the LLM's adaptability.
  arXiv  Detail & Related papers  (2024-10-21T13:39:03Z)
• TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation [16.46292795782835]
  Code translation is crucial for software migration, system ablation, and cross-platform development. Traditional rule-based methods rely on manually-written rules, which can be time-consuming and often result in less readable code. More recently, the advance of Large Language Models (LLMs) further boosts learning-based code translation. We propose a novel multi-agent system TRANSAGENT, which enhances LLM-based code translation by fixing the syntax errors and semantic errors.
  arXiv  Detail & Related papers  (2024-09-30T02:53:03Z)
• Extend Model Merging from Fine-Tuned to Pre-Trained Large Language Models via Weight Disentanglement [72.97553348776425]
  We make a pioneering effort to broaden the applicability of merging techniques from FT to PT LLMs. We introduce an approach based on WeIght DisENtanglement (WIDEN) to effectively extend the merging scope. We merge Qwen1.5-Chat (an FT LLM with instruction-following skills) with Sailor (a PT LLM with multilingual abilities) across 7B and 14B model scales.
  arXiv  Detail & Related papers  (2024-08-06T10:46:46Z)
• ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline [42.61538071832468]
  Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving. We tailor the Self-Critique pipeline, which addresses the challenge in the feedback learning stage of LLM alignment.
  arXiv  Detail & Related papers  (2024-04-03T17:51:18Z)
• Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning [57.323716555996114]
  Off-target translation remains an unsolved problem, especially for low-resource languages. Recent works have either designed advanced prompting strategies to highlight the functionality of translation instructions or exploited the in-context learning ability of LLMs. In this work, we design a two-stage fine-tuning algorithm to improve the instruction-following ability (especially the translation direction) of LLMs.
  arXiv  Detail & Related papers  (2024-03-21T13:47:40Z)
• TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement [26.26493253161022]
  Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT) We introduce a systematic LLM-based self-refinement translation framework, named textbfTEaR.
  arXiv  Detail & Related papers  (2024-02-26T07:58:12Z)
• Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis [103.89753784762445]
  Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT) This paper systematically investigates the advantages and challenges of LLMs for MMT. We thoroughly evaluate eight popular LLMs, including ChatGPT and GPT-4.
  arXiv  Detail & Related papers  (2023-04-10T15:51:30Z)
• Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval [66.69799641522133]
  State-of-the-art neural (re)rankers are notoriously data hungry. Current approaches typically transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders. We show that two parameter-efficient approaches to cross-lingual transfer, namely Sparse Fine-Tuning Masks (SFTMs) and Adapters, allow for a more lightweight and more effective zero-shot transfer.
  arXiv  Detail & Related papers  (2022-04-05T15:44:27Z)

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.