appier-research/structure-gen
Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models
This project helps evaluate how well large language models (LLMs) perform when asked to provide answers in specific formats like JSON or XML, compared to giving free-form responses. It takes various LLM outputs and analyzes their accuracy in reasoning and understanding domain-specific knowledge under these constraints. Researchers and developers working with LLMs would use this to understand the practical impact of structured generation.
No commits in the last 6 months.
Use this if you need to understand how forcing an LLM to output information in a specific format (like JSON or XML) impacts its accuracy and reasoning ability on tasks.
Not ideal if you're looking for a tool to develop a new structured generation method or optimize existing ones, rather than to evaluate their performance impact.
Stars
26
Forks
4
Language
Python
License
—
Category
Last pushed
May 31, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/llm-tools/appier-research/structure-gen"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
genlm/genlm-control
Controlled text generation with programmable constraints
Intelligent-CAT-Lab/AlphaTrans
Artifact repository for the paper "AlphaTrans: A Neuro-Symbolic Compositional Approach for...
madaan/self-refine
LLMs can generate feedback on their work, use it to improve the output, and repeat this process...
PCI-ORG/PCI-Personnel
Policy Change Index for Personnel (PCI-Personnel)
gokmengokhan/deo-llm-reframing
Replication materials: Testing Distance-Engagement Oscillation as a prompting framework for...