TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models
arXiv:2607.01235v1 Announce Type: new Abstract: Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners. While recent tools provide insights into model internals or generation outcomes, they often lack decoding-time signals, fine-grained uncertainty measures, and interactive mechanisms for exploring alternative generation paths. We present TokenScope, an interactive interpretability and analysis tool for decoder-based LLMs that exposes token-level metrics, attention patterns, and structura
As LLMs become more integrated into complex tasks like code generation, the need for transparency and interpretability at a granular level is becoming critical for debugging, security, and trust.
Tools like TokenScope are essential for addressing the 'black box' problem of large language models, enabling better understanding, control, and ultimately, more reliable and ethical AI applications, particularly in high-stakes fields like software development.
The development of sophisticated interpretability tools will allow developers and researchers to debug, optimize, and secure LLM-generated code more effectively, potentially accelerating the adoption and trustworthiness of AI in software engineering.
- · AI researchers
- · Software developers
- · Cybersecurity firms
- · AI platform providers
- · Companies relying on opaque AI systems
- · Attackers exploiting LLM vulnerabilities
Improved understanding of LLM decision-making mechanisms leads to more robust and less error-prone AI-powered code generation.
Enhanced interpretability fosters greater confidence in deploying LLMs for critical software development tasks, accelerating the shift towards AI-assisted programming.
The democratization of advanced interpretability tools could lead to novel AI safety and auditing practices, influencing future regulatory frameworks for AI systems.
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