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Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

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arxiv 2401.08417 v4 pith:TZBV5PN4 submitted 2024-01-16 cs.CL

Haoran Xu , Amr Sharaf , Yunmo Chen , Weiting Tan , Lingfeng Shen , Benjamin Van Durme , Kenton Murray , Young Jin Kim This is my paper
classification cs.CL
keywords modelsperformancetranslationllmsalmacontrastivegpt-4machine
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Moderate-sized large language models (LLMs) -- those with 7B or 13B parameters -- exhibit promising machine translation (MT) performance. However, even the top-performing 13B LLM-based translation models, like ALMA, does not match the performance of state-of-the-art conventional encoder-decoder translation models or larger-scale LLMs such as GPT-4. In this study, we bridge this performance gap. We first assess the shortcomings of supervised fine-tuning for LLMs in the MT task, emphasizing the quality issues present in the reference data, despite being human-generated. Then, in contrast to SFT which mimics reference translations, we introduce Contrastive Preference Optimization (CPO), a novel approach that trains models to avoid generating adequate but not perfect translations. Applying CPO to ALMA models with only 22K parallel sentences and 12M parameters yields significant improvements. The resulting model, called ALMA-R, can match or exceed the performance of the WMT competition winners and GPT-4 on WMT'21, WMT'22 and WMT'23 test datasets.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 19 citations worldwide. Full citation record

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  4. $M^2PO$: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation

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    M2PO combines a QE-plus-alignment reward with a dynamic curriculum and multi-pair DPO loss, and reports WMT21-22 gains for a 7B translation model, but the abstract's WMT23/24 9B parity claims are unsupported.

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  6. RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation

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    RIVAL iteratively re-trains a reward model adversarially against the current translator and adds a BLEU-predicting head, improving in-domain WMT and subtitle translation over SFT baselines.

  7. Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models

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    A survey of context-aware machine translation with large language models, categorizing prompting, fine-tuning, and agent-based approaches.

  8. Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Dynamic chunking plus question-aware chunk selection improves long-context QA, but the headline numbers are partly inflated by choosing hyperparameters on the test benchmarks.

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