Unlocking Structured Commonsense Reasoning with Code-LLMs | HackerNoon

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 employ large language models of code for structured commonsense generation. By converting the output commonsense structures to Python code, COCOGEN provides a simple and effective method for leveraging the code-generation abilities of Code-LLMs for structured generation. These results open a promising direction for structural commonsense reasoning. We believe that the principles and the methods presented in this paper are applicable to additional NLP tasks that require “language understanding” and structured prediction.

Acknowledgments

We thank Kaixin Ma, Keisuke Sakaguchi and Niket Tandon for thoughtful discussion and helping with PROSCRIPT datasets and the anonymous reviewers for valuable feedback. This material is partly based on research sponsored in part by the Air Force Research Laboratory under agreement number FA8750-19-2-0200. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. Government. This project was also partially supported by a gift from AWS AI.

Limitations

Some experiments in this work are performed with language models that are not open-sourced, namely DAVINCI, CURIE, and CODEX. Existing documentation (Brown et al., 2020; Chen et al., 2021b) does not fully describe the details of these models, such as the pretraining corpus, model size, and model biases. Therefore, we can only provide educational guesses on these details (analysis in Appendix A). In addition, even though CODEX is free to use for research as of June 2022, we are unsure whether the research community will continue to have free access in the future. Nonetheless, we release our code and model outputs to ensure the reproducibility of our work. Furthermore, in cases where the models we experiment with reveal any issue, the publicly available code will allow future investigations.

Another limitation of our work is that we exclusively experiment with datasets in English. Exploring the efficacy of structured generation methods in cross-lingual settings is an interesting and important future work.

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