TL;DR
This paper introduces a federated prompt tuning approach for multilingual large language models that overcomes data sharing and linguistic barriers, improving performance for low-resource languages while respecting privacy constraints.
Contribution
The paper proposes a novel federated prompt tuning paradigm that enhances multilingual LLMs' efficiency and fairness, especially for low-resource languages, under data sharing restrictions.
Findings
Achieves 6.9% higher accuracy than traditional methods
Improves data efficiency and model stability
Facilitates mutual language enhancements
Abstract
Pre-trained large language models (LLMs) have become a cornerstone of modern natural language processing, with their capabilities extending across a wide range of applications and languages. However, the fine-tuning of multilingual LLMs, especially for low-resource languages, faces significant challenges arising from data-sharing restrictions (the physical border) and inherent linguistic differences (the linguistic border). These barriers hinder users of various languages, particularly those in low-resource regions, from fully benefiting from the advantages of LLMs. To address these challenges, we propose the Federated Prompt Tuning Paradigm for multilingual scenarios, which utilizes parameter-efficient fine-tuning while adhering to data sharing restrictions. We design a comprehensive set of experiments and analyze them using a novel notion of language distance to highlight the…
Peer Reviews
Decision·ICLR 2024 poster
- The method is very practical since it is simple and efficient, and it is an appropriate method for training multilingual model. - Good analysis on the data efficiency and distance measurement, showing the effectiveness of the proposed method.
- In terms of novelty, the proposed idea is not new, and it is only a further investigation of the multilingual setting. - Lack of clarity. The paper does not provide enough information about how the prompts are constructed or look like and hyperparameters for all settings. I suggest adding the information to the paper or appendix.
The innovation lies in that the paper somehow mashes federated learning, multi-lingual (low resource) language models, and Parameter-Efficient Fine-Tuning in one paper. The fact that they managed to come up with a storyline for a system that bolsters the benefit of each approach is commendable.
- poor presentation: the citations are not separable enough from the main text, e.g., without any parenthesis, rendering the submission unreadable. Against the tradition and ease of reading, abbreviations are not defined in advance, e.g., NLI, PFL, PLM. - claims unverifiable: no code release. - conflating existing metrics with innovation: language distance is not a new concept. - conceptual weakness: the contrived baseline was bound to give the proposed approach an edge due to lack of federated
- Federated learning have recently gained good traction, the paper is a good application of it in the tasks of finetuning LLM. The paper chooses to use prompt tuning instead of full tuning to save costs, as well as to avoid overfitting on small data. - The method produces better performance on the 2 classification tasks compared to baselines
- The proposed is a very trivial combination of federated learning and prompt tuning, which both are established methodology in their own realm. There is no novelty, such as modification or adjustment to the method that may have give a better results. In other words, people with an objective to do federated learning for privacy purpose can easily come up with prompt tuning as a solution to reduce costs. - Though it may have implicitly inferred by the concept of FL, the paper did not mention why
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