Inside the Black Box: Interpreting LLMs with DLBacktrace (DLB)
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Date: Thursday, 5th June, 2025
Time: 11:00 AM EDT | 4:00 PM BST | 8:30 PM IST
Leading banks, financial institutions, and healthcare providers are increasingly experimenting with using large language models (LLMs) for day-to-day tasks and high-risk use cases. Because of the sensitivity and riskiness of these tasks, model interpretability is critically required for these experiments to deliver acceptable outcomes for all key stakeholders, like AI practitioners, Underwriters, Business owners, Auditors, and Regulators. The opacity of these models poses significant operational and regulatory risks for both practitioners and AI risk management departments inside these enterprises. It is no longer optional—it is essential.
Introducing DLBacktrace: Precision Explainability for Mission-Critical AI
DLBacktrace (Arxiv & Github), developed by AryaXAI, offers a new approach to interpreting deep learning models of any modality and size by meticulously tracing relevance from model decisions to their inputs. DLB can produce massive information on the international functioning of the LLMs at each parameter, or layer, or on the inputs for each generated token/prediction. This deterministic, model-agnostic method significantly improves transparency and reliability, essential for mission-critical operations.
Why Risk, Compliance, and Data Leaders Choose DLBacktrace
- Provides deterministic, reproducible explanations suitable for stringent audit scenarios.
- Excels in handling complex, high-dimensional data inherent in LLMs.
- Ensures robust compliance with global regulatory frameworks like the EU AI Act and RBI's MRM, reducing organizational risk.
- Unlike SAEs, these don’t need any training and directly use the model and the inference inputs.
Key Takeaways for Executives:
- Insights into compliance with critical regulations such as GDPR, EU AI Act, and RBI MRM.
- A deep dive into DLBacktrace’s unique modes (default and contrastive) and their strategic uses.
- A comparative analysis demonstrating DLBacktrace’s distinct advantages over traditional methods like SHAP, LIME, and GradCAM.
- A walkthrough of the implementation of DLB for LLma 3.1 and MoEs like Olmoe.
Join us to explore how DLBacktrace can empower your leadership with transparent, accountable, and compliant AI strategies that significantly mitigate risk and foster stakeholder trust.
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