Unlocking Structured Commonsense Reasoning with Code-LLMs

COCOGEN is the first to use Code-LLMs for structured commonsense reasoning, showing how converting commonsense tasks to Python code improves generation. This method opens up new opportunities for structured reasoning in NLP.

Table of Links

Abstract and 1 Introduction

2 COCOGEN: Representing Commonsense structures with code and 2.1 Converting (T,G) into Python code

2.2 Few-shot prompting for generating G

3 Evaluation and 3.1 Experimental setup

3.2 Script generation: PROSCRIPT

3.3 Entity state tracking: PROPARA

3.4 Argument graph generation: EXPLAGRAPHS

4 Analysis

5 Related work

6 Conclusion, Acknowledgments, Limitations, and References

A Few-shot models size estimates

B Dynamic prompt Creation

C Human Evaluation

D Dataset statistics

E Sample outputs

F Prompts

G Designing Python class for a structured task

H Impact of Model size

I Variation in prompts

6 Conclusion

We present the first work to ...