Enhancing Cross-lingual Performance of Small Language Models through Lottery Ticket Prompt-learning

Conceitos Básicos

The proposed Lottery Ticket Prompt-learning (LTP) framework selectively prompts a subset of the model's parameters to effectively adapt small-sized language models to cross-lingual tasks, especially for low-resource languages.

Resumo

The paper introduces the Lottery Ticket Prompt-learning (LTP) framework, which integrates the Lottery Ticket Hypothesis with prompt-based fine-tuning to effectively prompt small-sized language models for cross-lingual tasks.
The key steps are:

Lottery ticket fine-tuning: The authors select a subset of the model's parameters that have changed the most during fine-tuning on English data. This subset is used in the subsequent prompt-learning step.
Prompting and sparse LM fine-tuning: The authors prepend a sequence of continuous vectors (soft prompts) to the input and only update the prompt-related parameters and the selected subset of parameters during fine-tuning on the downstream cross-lingual tasks.

The authors demonstrate the effectiveness of the LTP framework on cross-lingual natural language inference tasks, particularly in low-resource language settings. Experimental results show that the LTP framework outperforms baseline methods while only updating 20% of the original model parameters.
The authors also analyze the impact of different parameter selection strategies and active ratios, finding that selecting parameters from the middle layers of the model can achieve comparable performance with further parameter reduction.

Estatísticas

"Emotionally Charged and Brilliantly Crafted" is an example movie review used to illustrate prompt-based classification.
The authors use 300K English sentences from Wikipedia for the parameter selection process.
The XNLI dataset is used for the cross-lingual natural language inference task, with training and development data sampled for few-shot settings.
The AmericasNLI dataset is used to evaluate performance on truly low-resource indigenous languages.

Citações

"Current soft prompt methods yield limited performance when applied to small-sized models (fewer than a billion parameters)."
"Prompt+LM tuning bears the risk of overfitting when engaging with extremely small datasets due to the vast number of parameters involved."
"Our approach facilitates adaptation to low-resource languages, both seen and unseen by the pre-trained models, by reducing tuned parameter sizes without significantly altering language-specific knowledge."

Principais Insights Extraídos De

Effectively Prompting Small-sized Language Models for Cross-lingual Tasks via Winning Tickets

by Mingqi Li,Fe... às arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01242.pdf

Perguntas Mais Profundas

How can the LTP framework be extended to other cross-lingual tasks beyond natural language inference?

The LTP framework can be extended to other cross-lingual tasks beyond natural language inference by adapting the parameter selection and fine-tuning process to suit the specific requirements of the task at hand. For tasks such as machine translation, named entity recognition, or sentiment analysis, the framework can be modified to select parameters that are most relevant to the task and the languages involved. This may involve identifying key linguistic features or structures that are crucial for the task and focusing on fine-tuning those parameters. Additionally, incorporating task-specific prompts or constraints during the fine-tuning process can further enhance the model's performance on diverse cross-lingual tasks.

What are the potential limitations of the Lottery Ticket Hypothesis-based parameter selection approach, and how can it be further improved?

One potential limitation of the Lottery Ticket Hypothesis-based parameter selection approach is the reliance on a single round of pruning and retraining, which may not always lead to the optimal selection of winning tickets. To address this limitation, the approach can be improved by incorporating iterative pruning and retraining cycles to refine the selection of winning tickets and improve the overall performance of the model. Additionally, exploring different pruning strategies, such as magnitude-based pruning or structured pruning, can help identify more effective winning tickets and enhance the efficiency of the parameter selection process.

How can the insights from the analysis of parameter selection in different layers be leveraged to design more efficient cross-lingual language models?

The insights from the analysis of parameter selection in different layers can be leveraged to design more efficient cross-lingual language models by focusing on the middle layers of the model, which are found to contain more expressive parameters that are beneficial for cross-lingual transfer. By prioritizing the selection of active parameters from these middle layers during the fine-tuning process, the model can capture language-specific and task-specific information more effectively, leading to improved performance on cross-lingual tasks. Additionally, fine-tuning strategies can be tailored to target specific layers based on their linguistic and semantic properties, optimizing the model's adaptability and generalization across languages.