针对菲律宾语优化的LLM，提升本地语言处理能力。
FiLLM -- A Filipino-optimized Large Language Model based on Southeast Asia Large Language Model (SEALLM)
- 基于SeaLLM-7B 2.5，用低秩适配微调提升效率。
- 在命名实体识别等任务上表现优于部分基线模型。
- 适合需要菲律宾语自然语言处理的应用场景。
本研究提出FiLLM，一款针对菲律宾语优化的大语言模型，旨在提升菲律宾语的自然语言处理能力。基于SeaLLM-7B 2.5模型，采用低秩适配（LoRA）微调方法，在保持任务性能的同时优化内存效率。模型在多个菲律宾语数据集上训练与评估，涵盖命名实体识别（NER）、词性标注（POS）、依存句法分析和文本摘要等关键任务。通过F1分数、精确率、召回率、压缩率和关键词重叠率等指标，与CalamanCy模型进行对比。结果显示，Calamancy在多项指标上表现更优，体现出更强的菲律宾语理解与适应能力。本研究为菲律宾语NLP应用提供了高效、可扩展的定制化模型支持。
原文摘要 · Abstract (English)
This study presents FiLLM, a Filipino-optimized large language model, designed to enhance natural language processing (NLP) capabilities in the Filipino language. Built upon the SeaLLM-7B 2.5 model, FiLLM leverages Low-Rank Adaptation (LoRA) fine-tuning to optimize memory efficiency while maintaining task-specific performance. The model was trained and evaluated on diverse Filipino datasets to address key NLP tasks, including Named Entity Recognition (NER), Part-of-Speech (POS) tagging, Dependency Parsing, and Text Summarization. Performance comparisons with the CalamanCy model were conducted using F1 Score, Precision, Recall, Compression Rate, and Keyword Overlap metrics. Results indicate that Calamancy outperforms FILLM in several aspects, demonstrating its effectiveness in processing Filipino text with improved linguistic comprehension and adaptability. This research contributes to the advancement of Filipino NLP applications by providing an optimized, efficient, and scalable language model tailored for local linguistic needs.
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