Mike Lewis
Mike Lewis is an AI researcher whose work spans negotiation dialogue, language-model pretraining, retrieval, and alignment. His PhD research at Edinburgh explored symbolic and distributed semantics, and he won an EMNLP Best Paper award in 2016. His papers include RoBERTa, BART, retrieval-augmented generation, LIMA, The Llama 3 Herd of Models, and Context Language Models. As of October 2026, he is a Research Scientist at Meta FAIR.
- Now at
- FAIR (Meta)
Timeline
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2016
Edinburgh semantics and an EMNLP award
His PhD research at Edinburgh explores symbolic and distributed semantics. In 2016, he wins an EMNLP Best Paper award.
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2017
Negotiation dialogue
He coauthors Deal or No Deal? in 2017, an end-to-end model that learns negotiation without annotated dialogue states and handles both language and reasoning.
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2019
RoBERTa revisits BERT
In 2019, he coauthors RoBERTa, a replication study of BERT pretraining that measures how hyperparameters and training-data size affect performance.
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2020
BART and retrieval-augmented generation
In 2020, he coauthors BART, a denoising autoencoder that corrupts text and learns to reconstruct it for sequence-to-sequence generation and comprehension. He also coauthors retrieval-augmented generation (RAG), which combines a pretrained sequence-to-sequence model with a retriever over a Wikipedia vector index to improve open-domain question answering.
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2023
LIMA alignment training
At NeurIPS in 2023, he coauthors LIMA, a 65-billion-parameter LLaMa model fine-tuned with 1,000 curated prompts and responses, without reinforcement learning or human preference modeling.
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2024
The Llama 3 models
In 2024, he coauthors The Llama 3 Herd of Models, which presents a family of foundation models for multilingual use, coding, reasoning, and tool use.
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September 2026
Context Language Models
In September 2026, he coauthors Context Language Models, which proposes language models that manage their own context by treating it as a file the model can update.
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As of October 2026
Research Scientist at Meta FAIR
As of October 2026, he is a Research Scientist at Meta FAIR.
Sources and credits
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- The Researchers | Next Play
- Deal or No Deal? End-to-End Learning for Negotiation Dialogues
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- LIMA: Less Is More for Alignment
- The Llama 3 Herd of Models
- Context Language Models