Train More Parameters But Mind Their Placement: Insights into Language Adaptation with PEFT

• URL: http://arxiv.org/abs/2412.12674v1
• Date: Tue, 17 Dec 2024 08:44:00 GMT
• Title: Train More Parameters But Mind Their Placement: Insights into Language Adaptation with PEFT
• Authors: Jenny Kunz,
• Abstract summary: We aim to enhance the generation performance of an LLM by specialising it using unstructured text corpora. We find that increasing the number of trainable parameters leads to better and more robust language adaptation. Although improvements are consistent in 0-shot summarisation, some adapted models struggle with longer context lengths.
• Score: 0.8702432681310401
• License:
• Abstract: Smaller LLMs still face significant challenges even in medium-resourced languages, particularly when it comes to language-specific knowledge -- a problem not easily resolved with machine-translated data. In this case study on Icelandic, we aim to enhance the generation performance of an LLM by specialising it using unstructured text corpora. A key focus is on preventing interference with the models' capabilities of handling longer context during this adaptation. Through ablation studies using various parameter-efficient fine-tuning (PEFT) methods and setups, we find that increasing the number of trainable parameters leads to better and more robust language adaptation. LoRAs placed in the feed-forward layers and bottleneck adapters show promising results with sufficient parameters, while prefix tuning and (IA)3 are not suitable. Although improvements are consistent in 0-shot summarisation, some adapted models struggle with longer context lengths, an issue that can be mitigated by adapting only the final layers.

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