Exploring the Value of Domain-Specific Models: A Case Study in Financial Services
As the co-founder and CTO of Writer, I'm excited to share our journey in building domain-specific language models, and how our latest research reveals that even with high-accuracy general models, we still need to build and refine domain-specific models for reliable utilization.
- 1. Wasim is a co-founder and CTO of Writer, which started in 2020.
- 2. They began by building decoder-encoder models and have since created a family of about 36 models, including general and domain-specific ones.
- 3. Recently, they noticed language models (LMs) achieving high accuracy in various domains, prompting the question: should they continue building domain-specific models if general models can achieve a
- 4. To answer this, Writer created a real-world scenario evaluation called "fail" to test new models and see if they deliver the promised accuracy.
- 5. The evaluation includes two main categories: query failure and context failure.
- 6. Query failure has three subcategories: misspelling queries, incomplete queries, and out-of-domain (OOD) queries.
- 7. Context failure also has three subcategories: messing context, OCR errors, and irrelevant context.
- 8. Writer created a diverse dataset for the financial services domain to evaluate these models.
- 9. They introduced a simple evaluation key matrix that looks at two factors: whether the model gives the correct answer and if it follows grounded context.
- 10. They selected various chat models and thinking models for the evaluation.
- 11. The results showed good behavior in answering queries, but many models still failed when given wrong data or context.
- 12. Most models can generate answers with high hallucination rates, especially in financial benchmarks.
- 13. There is a significant gap between model robustness and hallucination, even for the best models.
- 14. According to Wasim, having only the best model isn't enough; building full-stack systems with grounding and guardrails is necessary for reliability.
- 15. Despite general models achieving high accuracy, domain-specific models are still needed due to the significant gap in context following and grounding.
- 16. The financial services domain specifically requires robust domain-specific models.
- 17. Writer's evaluation set, white paper, and leaderboard are open-source and available on GitHub and Hugging Face.
- 18. Smaller models can sometimes outperform larger, more complex models in context following.
- 19. The Chain of Thought concept may need further investigation based on the data from domain-specific tasks.
- 20. Even with high accuracy, there is still work to be done to improve model performance and reliability.
- 21. According to Wasim, a combination of technology advancements and full-stack systems will be required to achieve optimal performance.
- 22. For now, building and maintaining domain-specific models is still necessary for reliable use in the market.
- 23. Wasim encourages exploring the resources available on GitHub and Hugging Face for further research and collaboration.
- 24. Despite progress in language model accuracy, there are still challenges to overcome before achieving satisfactory reliability and context following in various domains.
Source: AI Engineer via YouTube
❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!