FREYR, a modular pipeline that separates intent detection, parameter generation, and summarization, achieves higher task completion than Ollama's native tool calling on the LLMaker benchmark.
ChatVis: Automating Scientific Visualization with a Large Language Model
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We develop an iterative assistant we call ChatVis that can synthetically generate Python scripts for data analysis and visualization using a large language model (LLM). The assistant allows a user to specify the operations in natural language, attempting to generate a Python script for the desired operations, prompting the LLM to revise the script as needed until it executes correctly. The iterations include an error detection and correction mechanism that extracts error messages from the execution of the script and subsequently prompts LLM to correct the error. Our method demonstrates correct execution on five canonical visualization scenarios, comparing results with ground truth. We also compared our results with scripts generated by several other LLMs without any assistance. In every instance, ChatVis successfully generated the correct script, whereas the unassisted LLMs failed to do so. The code is available on GitHub: https://github.com/tanwimallick/ChatVis/.
citation-role summary
background 1
citation-polarity summary
fields
cs.SE 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
FREYR: A Framework for Recognizing and Executing Your Requests
FREYR, a modular pipeline that separates intent detection, parameter generation, and summarization, achieves higher task completion than Ollama's native tool calling on the LLMaker benchmark.