Could you give me a hint? Generating inference graphs for defeasible reasoning

Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, Eduard H Hovy

Keywords: graph generation, kb generation, commonsense reasoning, defeasible reasoning

TL;DR: Graphs generated by language models hep humans at defeasible reasoning

Abstract: Defeasible reasoning is a mode of reasoning where conclusions can be overturned by taking into account new evidence. A commonly used method in cognitive science and logic literature is to handcraft argumentation supporting inference graphs. While humans find inference graphs very useful for reasoning, constructing them at scale is difficult. In this paper, we automatically generate such inference graphs through transfer learning from a related NLP task that shares the kind of reasoning that inference graphs support. Through automated metrics and human evaluation, we find that our method generates meaningful graphs for the defeasible inference task. Human accuracy on this task improves by 20% by consulting the generated graphs. Our findings open up exciting new research avenues for cases where machine reasoning can help human reasoning.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/could-you-give-me-a-hint-generating-inference/code)