Summary of Mechanistic Interpretability Of Large Language Models with Applications to the Financial Services Industry, by Ashkan Golgoon et al.
Mechanistic interpretability of large language models with applications to the financial services industry
by Ashkan Golgoon, Khashayar Filom, Arjun Ravi Kannan
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Numerical Analysis (math.NA)
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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 explores the application of mechanistic interpretability to large language models, specifically Generative Pre-trained Transformers (GPTs), for use in financial services. Large language models have impressive capabilities but are complex and lack transparency, posing challenges for adaptation by financial institutions. The authors pioneer the use of mechanistic interpretability to understand GPT-2 Small’s attention patterns when identifying potential violations of Fair Lending laws. They employ direct logit attribution to study layer and attention head contributions to logit differences in residual streams. Additionally, they design clean and corrupted prompts using activation patching as a causal intervention method to localize task completion components. The authors identify significant roles played by certain attention heads (positive: 10.2, 10.7, and 11.3; negative: 9.6 and 10.6) in completing the task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how big language models can be used in finance. These models are really good at doing things like understanding text, but it’s hard to figure out exactly how they’re making decisions. This is a problem because financial institutions need to know that these models aren’t being biased or unfair. The authors are trying to solve this problem by creating ways to “reverse engineer” the language models and understand what makes them tick. They use one of these models, called GPT-2 Small, to see how it identifies potential problems with financial laws. They find out which parts of the model are most important for making decisions. |
Keywords
» Artificial intelligence » Attention » Gpt
