Improving Neural Machine Translation Performance through Direct Preference Optimization and Minimum Bayes Risk Decoding

Core Concepts

Direct Preference Optimization (DPO) can fine-tune Multilingual Large Language Models (MLLMs) to achieve the gains of Minimum Bayes Risk (MBR) decoding without additional computation during inference.

Abstract

The authors propose a novel self-supervised fine-tuning method based on Direct Preference Optimization (DPO) to improve the translation performance of Multilingual Large Language Models (MLLMs).

The key insights are:

1. MBR decoding can significantly boost the translation performance of MLLMs, but it is computationally expensive.
2. The authors show how DPO can be used to fine-tune MLLMs to learn the same ranking preferences as MBR decoding, without any additional computation during inference.
3. The DPO fine-tuning process uses a small monolingual dataset to create a preference dataset, where translation hypotheses are ranked by their MBR scores. The DPO algorithm is then used to fine-tune the MLLM to prefer the higher-ranked translations over the lower-ranked ones.
4. The DPO MBR fine-tuned models, when decoded with beam search, achieve similar performance as MBR decoding of the original model, outperforming the base model on multiple NMT test sets.
5. The authors investigate different preference pair selection strategies and the impact of the size of the hypothesis set used for DPO fine-tuning. They find that the method is robust to the selection strategy and can achieve strong performance with a smaller hypothesis set compared to MBR decoding.

Stats

MBR decoding can significantly boost the translation performance of MLLMs, outperforming greedy decoding and beam search.
DPO MBR fine-tuned models, when decoded with beam search, achieve similar performance as MBR decoding of the original model.
DPO MBR fine-tuning improves the translation performance of BLOOMZ and BLOOMZ-mt across a range of test sets, with gains of up to 4 BLEURT and 2 COMET scores compared to the base models.

Quotes

"Our goal is to fine-tune a base MLLM so that it has the same single-pass decoding performance as MBR decoding."
"MLLMs optimized for MBR preference achieve significantly better translation performance when decoded with beam search, achieving translation quality on par with MBR decoding of the original model."

Key Insights Distilled From

Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding

by Guangyu Yang... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2311.08380.pdf

Deeper Inquiries

How can the DPO MBR fine-tuning approach be extended to other language tasks beyond machine translation, such as text summarization or dialogue generation

The DPO MBR fine-tuning approach can be extended to other language tasks beyond machine translation by adapting the preference optimization technique to suit the specific requirements of tasks like text summarization or dialogue generation. For text summarization, the preference dataset could consist of pairs of summaries ranked based on their quality or relevance to the original text. The DPO algorithm could then be used to fine-tune a summarization model to prefer higher-ranked summaries over lower-ranked ones. Similarly, for dialogue generation, the preference dataset could include pairs of dialogues where one response is preferred over another based on criteria like coherence or informativeness. By training the model to generate responses that align with these preferences, the DPO MBR fine-tuning approach can enhance the performance of models in tasks beyond machine translation.

What are the potential risks or biases that could be introduced by the DPO MBR fine-tuning approach, and how can they be mitigated

One potential risk of the DPO MBR fine-tuning approach is the amplification of biases present in the baseline models, as the model learns to prefer certain outputs over others based on the provided preferences. To mitigate this risk, it is essential to carefully curate the preference dataset to ensure a diverse and unbiased representation of preferences. Additionally, monitoring the fine-tuned models for any undesirable behavior or biases post-fine-tuning can help in identifying and addressing any issues that may arise. Introducing specific penalties into the MBR utility function to discourage undesirable behavior could also serve as a mitigation strategy to counteract biases introduced during the fine-tuning process.

Could the DPO MBR fine-tuning approach be combined with other techniques, such as multi-task learning or prompt engineering, to further improve the performance of MLLMs

The DPO MBR fine-tuning approach can be combined with other techniques like multi-task learning or prompt engineering to further enhance the performance of Multilingual Large Language Models (MLLMs). By incorporating multi-task learning, the model can simultaneously optimize for multiple objectives, such as translation quality, summarization coherence, or dialogue relevance, leading to more robust and versatile models. Prompt engineering techniques can be used to provide specific instructions or constraints to the model during fine-tuning, guiding it to generate outputs that align with the desired preferences. By integrating these complementary approaches with DPO MBR fine-tuning, MLLMs can achieve improved performance across a range of language tasks.