Self-supervised Analogical Learning using Language Models
Authors: Ben Zhou, Sarthak Jain, Yi Zhang, Qiang Ning, Shuai Wang, Yassine Benajiba, Dan Roth


Reviewer paper summaries: This work introduces a novel self-supervised framework that leverages analogical learning to enhance the performance of language models on complex reasoning tasks without relying on human annotations. The proposed method uses two main processes: conceptualization and simplification, to transform complex questions into more nuanced forms, thus improving the model's abduction capability. This work paves the way for more efficient and scalable training of language models, particularly in applications requiring nuanced understanding and reasoning over complex, multifaceted questions. The findings encourage further research into self-supervised learning methods that can autonomously refine their reasoning capabilities over time.
Summary of strengths: The author proposes a novel method that converting complex questions into a more nuanced form to improve model's abduction capability without the need for human annotations. The conversion is automatic and completed in two steps, conceptualization and simplification. The effectiveness is also proved by evaluating the setup on a wide range of complex reasoning tasks.
Summary of weaknesses: As the author mentioned, there is limited data sizes, and the effectiveness of large-scale self-supervision is unexplored.
Comments, suggestions and typos: While the paper demonstrates improvements on specific datasets like StrategyQA, GSM8K, and HotpotQA, expanding the evaluation to include more diverse datasets could strengthen the claims about generalizability and robustness.
Overall assessment scores: 4