In this groundbreaking study, we delve into the intricate workings of the human brain and language models in processing natural language during conversations. By examining similarities and differences between deep learning models and the human brain, we uncover a fascinating alignment in how neural activity and model embeddings interact. Surprisingly, our findings demonstrate that the internal representations of a speech-to-text language model closely match with neural activity in the brain during speech comprehension and production. This discovery opens up a world of possibilities for developing biologically-inspired artificial neural networks that can revolutionize information processing in the future. Through collaboration with esteemed institutions, we aim to further explore these findings and create cutting-edge neural networks that mimic the brain’s language processing mechanisms.
https://research.google/blog/deciphering-language-processing-in-the-human-brain-through-llm-representations/