Summary of Enhancing Financial Domain Adaptation Of Language Models Via Model Augmentation, by Kota Tanabe et al.
Enhancing Financial Domain Adaptation of Language Models via Model Augmentation
by Kota Tanabe, Masanori Hirano, Kazuki Matoya, Kentaro Imajo, Hiroki Sakaji, Itsuki Noda
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a method called Composition to Augment Language Models (CALM) that effectively adapts large language models (LLMs) to the financial domain. CALM extends the capabilities of existing models by introducing cross-attention between two LLMs with different functions. The study demonstrates the effectiveness of CALM in enhancing the financial performance of an LLM with strong response capabilities, leveraging a financial-specialized LLM trained on a different dataset. The models are evaluated through quantitative Japanese financial benchmarks and qualitative response comparisons, showing that CALM enables superior responses with higher scores than original models and baselines. Additionally, comparative experiments reveal that connecting middle layers is most effective in facilitating adaptation to the financial domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make language models better for financial tasks. It creates a way called CALM that connects two different language models together. This helps the model learn about finance and answer questions more accurately. The study uses special financial data and benchmarks to test the model, showing it works better than other models. |
Keywords
» Artificial intelligence » Cross attention
