Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16243 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908499011698688 |
|---|---|
| author | Demirtaş, İrem Payzun, Burak Arslan, Seçil |
| author_facet | Demirtaş, İrem Payzun, Burak Arslan, Seçil |
| contents | Thanks to the growing popularity of large language models over the years, there is great potential for their applications in finance. Despite the exceptional performance of larger proprietary models, which are presented as black-box solutions through APIs, smaller models that can be hosted on-premise present opportunities for adaptability and privacy. Especially in cases where the management of sensitive information and application of domain knowledge is important, like finance, enhancing the capabilities of smaller models becomes crucial, notably for underrepresented languages. In this work, we introduce TULIP models, which adapt Llama 3.1 8B and Qwen 2.5 7B for domain and language adaptation, focusing on financial Turkish use cases.
The five-stage development pipeline involves data collection, continual pre-training (CPT), benchmark design, synthetic data generation and supervised fine-tuning (SFT). The results show that the capabilities of the models can be enhanced to effectively accomplish targeted tasks in this specific domain and language. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16243 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks Demirtaş, İrem Payzun, Burak Arslan, Seçil Computation and Language Thanks to the growing popularity of large language models over the years, there is great potential for their applications in finance. Despite the exceptional performance of larger proprietary models, which are presented as black-box solutions through APIs, smaller models that can be hosted on-premise present opportunities for adaptability and privacy. Especially in cases where the management of sensitive information and application of domain knowledge is important, like finance, enhancing the capabilities of smaller models becomes crucial, notably for underrepresented languages. In this work, we introduce TULIP models, which adapt Llama 3.1 8B and Qwen 2.5 7B for domain and language adaptation, focusing on financial Turkish use cases. The five-stage development pipeline involves data collection, continual pre-training (CPT), benchmark design, synthetic data generation and supervised fine-tuning (SFT). The results show that the capabilities of the models can be enhanced to effectively accomplish targeted tasks in this specific domain and language. |
| title | TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.16243 |