COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
📰 ArXiv cs.AI
Learn how COMPASS addresses performance disparities in large language models across languages using adaptive semantic sampling and parameter-efficient fine-tuning
Action Steps
- Implement COMPASS framework using PEFT to adapt LLMs to target languages
- Train lightweight models with adaptive semantic sampling to reduce negative cross-lingual interference
- Evaluate the performance of COMPASS on multilingual datasets to measure its effectiveness
- Compare the results with traditional fine-tuning methods to assess the benefits of COMPASS
- Apply COMPASS to real-world applications such as language translation and text classification
Who Needs to Know This
NLP engineers and researchers can benefit from this framework to improve the performance of their language models across multiple languages
Key Insight
💡 COMPASS addresses performance disparities in LLMs across languages by leveraging PEFT and adaptive semantic sampling
Share This
🚀 Introducing COMPASS: a novel framework for adapting LLMs to target languages using adaptive semantic sampling and PEFT 🌎
Key Takeaways
Learn how COMPASS addresses performance disparities in large language models across languages using adaptive semantic sampling and parameter-efficient fine-tuning
Full Article
Title: COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
Abstract:
arXiv:2604.20720v1 Announce Type: cross Abstract: Large language models (LLMs) often exhibit performance disparities across languages, with naive multilingual fine-tuning frequently degrading performance due to negative cross-lingual interference. To address this, we introduce COMPASS (COntinual Multilingual PEFT with Adaptive Semantic Sampling), a novel data-centric framework for adapting LLMs to target languages. COMPASS leverages parameter-efficient fine-tuning (PEFT) by training lightweight,
Abstract:
arXiv:2604.20720v1 Announce Type: cross Abstract: Large language models (LLMs) often exhibit performance disparities across languages, with naive multilingual fine-tuning frequently degrading performance due to negative cross-lingual interference. To address this, we introduce COMPASS (COntinual Multilingual PEFT with Adaptive Semantic Sampling), a novel data-centric framework for adapting LLMs to target languages. COMPASS leverages parameter-efficient fine-tuning (PEFT) by training lightweight,
DeepCamp AI