TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models
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Computer Science > Computation and Language
Title:TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models
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 structural information during generation. TokenScope supports interactive token replacement, counterfactual branching, and code-aware aggregation via abstract syntax trees. By unifying decoding-time signals with structural program analysis, TokenScope enables systematic investigation of LLM behaviour during code generation.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) |
| Cite as: | arXiv:2607.01235 [cs.CL] |
| (or arXiv:2607.01235v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01235
arXiv-issued DOI via DataCite
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Submission history
From: Amirreza Esmaeili [view email][v1] Thu, 30 Apr 2026 11:23:33 UTC (3,418 KB)
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