Compass: Large Multilingual Language Model for South-east Asia
This addresses the challenge of limited linguistic resources for Southeast Asian languages, supporting development needs at Shopee, but it is incremental as it builds on existing multilingual modeling techniques.
The paper tackles the problem of large language models underperforming in low-resource Southeast Asian languages like Indonesian by introducing CompassLLM, a model that surpasses benchmarks such as Vicuna-7b-v1.5 and SeaLLM in evaluation tasks.
Large language models have exhibited significant proficiency in languages endowed with extensive linguistic resources, such as English and Chinese. Nevertheless, their effectiveness notably diminishes when applied to languages characterized by limited linguistic resources, particularly within the Southeast Asian linguistic landscape, such as Indonesian. The scarcity of linguistic resources for these languages presents challenges associated with inadequate training, restricted vocabulary coverage, and challenging evaluation processes. In response to these exigencies, we have introduced CompassLLM, a large multilingual model specifically tailored for Southeast Asian languages, with the primary aim of supporting the developmental requirements of Shopee. Our methodology encompasses several key strategies. To progressively enhance multilingual proficiencies, we implemented a multi-stage pre-training strategy integrated with curriculum learning, gradually intensifying the focus on low-resource languages. Concurrently, to better accommodate low-resource human instructions, we curated and generated a repository of high-quality multilingual human instructions, culminating the CompassLLM-SFT model through supervised instruction fine-tuning. Finally, to reinforce the model's alignment with human preference behaviors, we have embraced the principle of Direct Preference Optimization (DPO) to obtain CompassLLM-DPO model. Preliminary evaluation of the CompassLLM model yields promising results, with our model surpassing benchmark models like Vicuna-7b-v1.5, Sealion, Falcon and SeaLLM, across diverse evaluation tasks, as verified through both automated and human-driven assessments. Notably, our model exhibits its superior performance in South-east Asia languages, such as Indonesian language.