Context-Ablation Analysis Reveals Dependence of Subquestions on Answer Options

Critical reasoning is a crucial aspect of natural language processing (NLP) that enables machines to comprehend and interpret text with the same level of understanding as humans. This ability involves not only understanding what a passage explicitly says but also grasping its underlying assumptions, argument structure, and supported conclusions. Developing systems capable of critical reasoning is one of the ultimate goals of NLP, and recent advancements in large language models (LLMs) have brought humans and machines closer to achieving this goal.
The article explores the concept of critical reasoning, its significance in NLP, and the various approaches proposed by researchers to develop systems that can reason critically. The authors explain that some studies focus on explaining the rationale behind commonsense and logical reasoning using graphs, while others decompose the reasoning process into smaller tasks, such as identifying supporting textual spans or underlying facts.
The article also highlights the challenges of writing rationales for logical reasoning questions, which involve multiple steps with varying granularity. To overcome these challenges, the authors propose using answer options in multiple-choice questions, which allow models to identify the correct option and eliminate the incorrect ones. They also collect human-written free-form rationales to improve the quality of model-generated rationales.
The authors conclude that developing systems capable of critical reasoning is an ongoing endeavor, but recent advancements in LLMs have brought us closer to achieving this goal. By leveraging these models and incorporating human-written rationales, we can create more accurate and comprehensive NLP systems that can reason critically like humans.
In summary, the article provides a comprehensive overview of critical reasoning in NLP, discussing its significance, approaches proposed by researchers, challenges faced, and future directions. The authors strive to demystify complex concepts by using everyday language and engaging metaphors or analogies to capture the essence of the article without oversimplifying it.