Asai Named to MIT Technology Review's '35 Innovators Under 35'

Incoming LTI faculty member Akari Asai recognized in prestigious list for work on retrieval-augmented generation

Akari Asai, who will join the Language Technologies Institute in the fall of 2026 as an assistant professor, has been named one of MIT Technology Review's "35 Innovators Under 35" for 2025.

Asai, currently a research scientist at the Allen Institute for AI, conducts research primarily on natural language processing (NLP) and machine learning, with a focus on large language models (LLMs). She has a particular interest in what she calls the "core limitations" of LLMs – those that cannot be resolved by scaling alone – such as hallucinations. 

The MIT Technology Review list honored Asai in its Artificial Intelligence category, discussing her pioneering work in the field of retrieval-augmented generation (RAG), a method whereby an LLM references pre-stored data sources in order to mitigate the risk of outputting hallucinated information. The Self-RAG framework, introduced by Asai and her co-authors in 2023, represents a significant improvement in the efficacy of RAG techniques.

"Even as AI advances quickly, language models still face limits like hallucinations and efficiency," Asai said. "My research pursues a better path: augmented language models that pair LMs with complementary modules such as retrieval, tools, and self-checks to overcome these limits."

The 35 Under 35 list is voted on by MIT Technology Review editors as well as expert external judges, who choose from among hundreds of nominees. "These scientists and entrepreneurs stood out for their early accomplishments and the ways they’re using their skills and expertise to tackle important problems," according to the article announcing the list, where honorees are featured both on the magazine's website and its September/October print edition.

Asai, who will also hold a courtesy appointment in the Machine Learning Department, received her Ph.D. in NLP from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She said that she looks forward to continuing research in the field of RAGs as she joins the CMU community: "At Carnegie Mellon, I look forward to building the next generation of augmented LMs and agentic systems and broadening their real-world impact."