@inproceedings{f8b1e0b341ed469ebe95488edfd6444f,
title = "LLaMA-Annotate—Visualizing Token-Level Confidences for LLMs",
abstract = "LLaMA-Annotate is a tool that allows visually inspecting the confidences that a large language model assigns to individual tokens, and the alternative tokens considered for that position. We provide both a simple, non-interactive command-line interface, as well as a more elaborate web application. Besides generally helping to form an intuition about the “thinking” of the LLM, our tool can be used for context-aware spellchecking, or to see how a different prompt or a differently trained LLM can impact the interpretation of a piece of text. The tool can be tried online at https://huggingface.co/spaces/s-t-j/llama-annotate.",
keywords = "Large language model, Token-level confidence, Visualization",
author = "Erik Schultheis and John, {S. T.}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD ; Conference date: 09-09-2024 Through 13-09-2024",
year = "2024",
month = sep,
day = "1",
doi = "10.1007/978-3-031-70371-3_33",
language = "English",
isbn = "978-3-031-70370-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "424--428",
editor = "Albert Bifet and Povilas Daniu{\v s}is and Jesse Davis and Tomas Krilavi{\v c}ius and Meelis Kull and Eirini Ntoutsi and Kai Puolam{\"a}ki and Indrė {\v Z}liobaitė",
booktitle = "Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track - European Conference, ECML PKDD 2024, Proceedings",
address = "Germany",
}