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December 10, 2023 – December 16, 2023

Microsoft at NeurIPS 2023

Central Standard Time (UTC -6)

Location: New Orleans, LA

Microsoft Research blog

Illustrated figure of lifelong model editing with GRACE. On the left is a question and the model’s existing answer to it (which is incorrect). Editing method needs to update it the correct answer. In the middle the architecture is shown where the language model is frozen and embeddings are extracted to retrieve appropriate values (new embeddings) from the codebook. On the right the codebook is shown which includes a set of trainable embeddings.
Microsoft Research Blog

| Tom Hartvigsen and Hamid Palangi

Lifelong model editing fixes mistakes discovered after model deployment. This work could expand sequential editing to model properties like fairness and privacy and enable a new class of solutions for adapting LLMs over long deployment lifetimes.

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Microsoft Research Blog

| Boxin Wang, Bo Li, and Zinan Lin

This paper received the outstanding benchmarks track paper award during NeurIPS 2023 (opens in new tab). How trustworthy are generative pre-trained transformer (GPT) models? To answer this question, University of Illinois Urbana-Champaign, together with Stanford University, University of California, Berkeley,…