Pratyay Banerjee
Papers
1
Total Citations
4
H-Index
1
About
Pratyay Banerjee is a researcher whose work sits at the intersection of natural language processing, reasoning, and artificial intelligence. His primary research focus is on understanding and enhancing the reasoning capabilities of large language models, particularly transformer architectures. In his influential paper, "Can Transformers Reason About Effects of Actions?" (2020), Banerjee investigated whether transformers can perform logical reasoning beyond simple pattern matching, specifically examining their ability to reason about cause-and-effect relationships expressed in natural language. This work demonstrated that transformers can, in limited settings, apply rules expressed as conditional statements to derive conclusions, suggesting a path toward more robust machine reasoning. While his most-cited paper has garnered 4 citations, its conceptual importance lies in probing the boundaries of what neural networks can achieve in terms of structured reasoning. Banerjee’s contributions are particularly valuable for students and researchers interested in neuro-symbolic AI, knowledge representation, and the development of language models that can genuinely understand and manipulate logical rules.
Research Focus
Key Achievements
Top Papers
- 1Can Transformers Reason About Effects of Actions?4 citations · 2020