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Existing tools often focus on internal inspection or generation outcomes without providing decoding-time signals, fine-grained uncertainty measures, or interactive ways to explore alternatives. TokenScope presents an interactive interpretability environment for decoder-based LLMs that exposes token-level metrics, attention patterns, and structural information. 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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.  \nCCS Concepts  \n• Computing methodologies → Natural language generation; Information extraction; Lexical semantics; Causal reasoning and diagnostics; • Software and its engineering;  \nKeywords  \nLarge Language Models, Code Generation, Interpretability, Explainability, Attention Analysis, Program Analysis  \nACM Reference Format:  \nAmirreza Esmaeili and Fatemeh Fard. 2018. TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models. In Proceedings of Companion Proceedings of the 34th ACM Symposium on the Foundations of Software Engineering (FSE ’26). ACM, New York, NY, USA, 12 pages. [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \n1 Introduction  \nLarge Language Models (LLMs) based on transformer decodershave become central to modern software engineering workflows, supporting tasks such as code generation, refactoring, test synthesis, and program understanding [3, 6, 8]. Despite their effectiveness, these models remain opaque: practitioners and researchers are often unable to explain why a model produced a particular token,  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nFSE ’26, Montreal, Canada  \n© 2018 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-1-4503-XXXX-X/2018/06  \n[https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \nhow uncertain it was at different points in the generation, or what alternatives were plausibly considered but discarded. This lack of explainability is particularly problematic in code generation, where small token-level deviations can cascade into syntactic errors, semantic bugs, or incorrect API usage.  \nExisting interpretability and explainability tools provide partial insight into LLM behaviour but leave important gaps. Prior work largely emphasizes dataset-level analysis, post-hoc evaluation, internal representation inspection, or static attention visualization, often requiring offline analysis and substantial expertise [1, 4, 5, 7, 9] . These approaches rarely expose decoding-","cbCaisbKp49visgN","https://ap.wps.com/l/cbCaisbKp49visgN","pdf",4069136,12,"English","# Abstract\n# CCS Concepts\n# Keywords\n# ACM Reference Format\n# 1 Introduction\n## Motivation and challenges in code generation explainability\n## Gaps in existing interpretability and explainability tools\n## TokenScope: interactive decoding-time instrumentation","[{\"question\":\"What problem does TokenScope address in LLM code generation?\",\"answer\":\"TokenScope targets the lack of explainability for why an LLM outputs specific tokens during code generation, especially the absence of decoding-time uncertainty and alternative-candidate signals.\"},{\"question\":\"How does TokenScope provide token-level interpretability?\",\"answer\":\"TokenScope instruments decoding to stream fine-grained token-level signals such as confidence, margin confidence, entropy, token surprisal, attention weights, and alternative candidates at each generation step.\"},{\"question\":\"What interactive capabilities does TokenScope offer to explore generation behavior?\",\"answer\":\"TokenScope allows token replacement and counterfactual branching by modifying the generated prefix and continuing generation, enabling direct inspection and comparison of token-level decisions.\"}]","TokenScope - Token-Level Explainability and Interpretability - for Code-Oriented Tasks in Large Language Models | PDF",1789976523]