How CODEX Model Size Influences COCOGEN's Output Quality
24 Apr 2025
Exploring the impact of model size on COCOGEN's performance, CODEX-002 outperforms CODEX-001. Prompt sensitivity decreases as model size increases.
COCOGEN vs DAVINCI: A Human Evaluation of Structured Commonsense Graph Generation
24 Apr 2025
Human evaluation shows COCOGEN outperforms DAVINCI in generating more relevant and correct commonsense graphs for tasks like EXPLAGRAPHS and PROSCRIPT.
Using Code-LLMs for Structured Commonsense Reasoning
24 Apr 2025
COCOGEN pioneers the use of Code-LLMs for structured commonsense generation, opening new avenues for NLP tasks that require structured prediction and reasoning.
Unlocking Structured Commonsense Reasoning with Code-LLMs
23 Apr 2025
COCOGEN pioneers the use of Code-LLMs for structured commonsense generation, opening new avenues for NLP tasks that require structured prediction and reasoning.
Using Code-LLMs for Symbolic and Structured Reasoning
23 Apr 2025
COCOGEN uses CodeLLMs for structured commonsense generation, going beyond traditional symbolic reasoning tasks by translating them into Python code.
Structured LLM Prompts Drive Better Results with COCOGEN
23 Apr 2025
COCOGEN’s performance gains stem from combining structured code prompts with CodeLLMs—outperforming text-based models even under dynamic or duplicate inputs.
COCOGEN Sets Few-Shot Benchmark in Entity and Argument Graph Tasks
22 Apr 2025
COCOGEN delivers top-tier results in entity state tracking and argument graph generation—surpassing fine-tuned models with just a few Python-coded examples.
Study Shows Few-Shot Code Generation Outperforms Fine-Tuned Models
22 Apr 2025
COCOGEN beats GPT-3 and fine-tuned models in structured commonsense tasks with just 15 Python-based examples. Efficient, powerful, and precise.
Why Converting Graphs to Python Code Improves AI Reasoning
22 Apr 2025
COCOGEN converts commonsense graphs into Python code, helping CodeLLMs outperform traditional models in structured reasoning tasks.