Press & Talks

Selected Press

• 2024.11: MIT Technology Reviews – Innovators Under 35 – A trailblazer in retrieval-augmented generation research is taking on the challenge of preventing hallucinations and elevating LLM reliability by putting external knowledge to use.

• 2024.7: Forbes – Innovation – How RAG-Powered AI Applications Have A Positive Impact On Businesses
• 2023.11: Towards Data Science – How Self-RAG Could Revolutionize Industrial LLMs
• 2022.10: UW CSE News – Lost in translation no more: IBM Fellowship winner Akari Asai asks — and answers — big questions in NLP to expand information access to all
• 2020.2: VentureBeat – Salesforce’s AI navigates Wikipedia to find answers to complex questions
• 2018.2: MIT Technology Review – 100,000 happy moments – What makes people happy? A huge database is making it possible to discern the answer at last.

Talks with Public Recordings

• 2023.7: ACL 2023 Tutorial: Retrieval-based Language Models and Applications (zoom recording)

• 2023.10: Self-Reflective Language Models with Retrieval at University of Massachusetts Amherst Machine Learning and Friends Lunch

• 2023.2: Adaptive and trustworthy NLP with retrieval for information access for everyone at The AI Talks

External talks

• 205.1 (expected): University of Pittsburgh CS Colloquium
• 2024.11: CMU WInE group OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
• 2024.5: Microsoft Research India, Reliable, Adaptable, and Attributable Language Models with Retrieval
• 2024.5: NIH, Reliable, Adaptable, and Attributable Language Models with Retrieval
• 2024.3: Meta Smart Glass AI Reading Group, Reliable, Adaptive and Attributable LMs with Retrieval
• 2024.3: Meta Smart Glass AI Reading Group, Reliable, Adaptive and Attributable LMs with Retrieval
• 2024.2: University College London, Web Intelligence Group, Reliable, Adaptive and Attributable LMs with Retrieval
• 2023.12: Microsoft Research Health Futures team, Self-reflective Language Models with Retrieval
• 2023.10: University of Edinburgh, Institute for Language, Cognition and Computation, Self-reflective Language Models with Retrieval
• 2023.10: University of Massachusetts Amherst Machine Learning and Friends Lunch, Self-reflective Language Models with Retrieval
• 2023.8: Preferred Networks, Retrieval-augmented LMs and Applications
• 2023.8: Okinawa Institute of Science and Technology OIST, Retrieval-augmented LMs and Applications
• 2023.8: University of Tokyo, Graduate School of Information Science and Technology, Retrieval-augmented LMs and Applications
• 2023.8: The University of Queensland Data Science Seminar Series: Investigating and Building Efficient and Reliable LMs with Retrieval Virtual
• 2023.7: The First International Workshop on Retrieval-enhanced Machine Learning @ SIGIR: Investigating and Building Efficient and Reliable LMs with Retrieval Virtual
• 2023.4: MILA NLP Reading Group, Adaptive and trustworthy NLP with Retrieval Montreal
• 2023.3: The AI TAL, Adaptive and trustworthy NLP with Retrieval
• 2022.12: AI Quiz King, Towards Better Multilingual Information Access
• 2022.8: Amazon (Alexa), Towards Better Multilingual Information Access
• 2021.10: SEA: Search Engines Amsterdam, Information Retrieval Lab at the University of Amsterdam, One Question Answering Model for Many Languages with Cross-lingual Passage Retrieval
• 2021.7: Apple (Web Answers), One Question Answering Model for Many Languages with Cross-lingual Passage Retrieval
• 2021.1: Apple (Web Answers), XOR QA: Cross-lingual Open-Retrieval Question Answering
• 2021.1: Google Language, XOR QA: Cross-lingual Open-Retrieval Question Answering
• 2020.6: Google Research, Learning to Retrieve Reasoning Paths over Wikipedia Graphs