Low-Rank Adaptation for Multilingual Summarization: An Empirical Study
This paper presents an empirical investigation into Low-Rank Adaptation (LoRA), a Parameter-Efficient Fine-Tuning (PEFT) method, applied within the context of multilingual summarization. The surge in the performance of NLP tasks due to LLMs like PaLM 2, LLaMA 2, and the GPT series has been significant. However, the growing size of these models has introduced constraints concerning memory, making traditional fine-tuning approaches less feasible. This paper emphasizes the efficacy of LoRA, not only comparing it to full fine-tuning across different model sizes but also probing its potential in varied data availability scenarios and its performance in cross-lingual transfer.
Key Contributions and Findings
- Effectiveness of LoRA:
- LoRA displayed competitive performance when vast amounts of training data were available, and it surpassed full fine-tuning in low-data and cross-lingual transfer scenarios. This highlights its adaptability and efficiency across different data regimes.
- Few-shot Cross-lingual Transfer:
- In cross-lingual settings, continued LoRA fine-tuning outperformed not just full fine-tuning, but also innovative approaches like LoraHub, which dynamically composes LoRA using few-shot samples.
- Comparative Performance:
- With limited training data, LoRA demonstrated greater stability and superior performance, primarily avoiding overfitting, a prevalent issue with full fine-tuning under such settings.
- Scalability and Robustness:
- For scenarios with larger models (e.g. PaLM 2-S), LoRA matched the performance of full fine-tuning in high-data environments, signifying that LoRA's performance improves with increased model capacity.
Empirical Analysis
Experiments were conducted on the XLSum and XWikis datasets, which provide diverse settings for multilingual summarization across multiple languages. Key performance indicators such as ROUGE-L, NLI for faithfulness, and seahorse for conciseness were employed to gauge summary quality.
- High-data Regime: While full fine-tuning led to higher ROUGE-L scores, LoRA surpassed it in NLI and seahorse metrics, indicating more concise and faithful summaries.
- Low-data Regime: LoRA maintained a consistent advantage in generating relevant summaries, proven by higher scores across all metrics compared to full fine-tuning.
- Cross-lingual Transfer: In zero-shot settings, full fine-tuning on English data led to suboptimal performance when adapting to other languages, while LoRA maintained its advantage across different languages.
Theoretical Implications
LoRA's performance suggests that low-rank adaptations add a layer of flexibility and efficiency, allowing for better cross-task generalizability without the heavy computational cost typically associated with large-scale model updates. This supports the notion that PEFT methods could be pivotal in making large models adaptable for a wider range of languages and tasks while maintaining computational feasibility.
Future Directions
This paper encourages extending LoRA's applications beyond summarization to more challenging multilingual tasks. The integration of other PEFT methods, such as adapters or prefix tuning with LoRA, could also be explored for potentially enhanced results. Moreover, dynamic module compositions that have shown promise in settings like few-shot transfer pave the way for innovative approaches to harness parameter-efficient solutions in diverse LLM applications.
In conclusion, this paper exemplifies the potential of parameter-efficient models in bridging performance and feasibility gaps within the landscape of LLM fine-tuning. As NLP models continue to evolve, approaches like LoRA are crucial for advancing multilingual capabilities and maximizing resource efficiency.