))
Does the DIFF Transformer make a Diff?
Archived series ("Inactive feed" status)
When?
This feed was archived on May 02, 2025 14:13 (
Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 449252081 series 3605861
Content provided by Brian Carter. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Brian Carter or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Introducing a novel transformer architecture, Differential Transformer, designed to improve the performance of large language models. The key innovation lies in its differential attention mechanism, which calculates attention scores as the difference between two separate softmax attention maps. This subtraction effectively cancels out irrelevant context (attention noise), enabling the model to focus on crucial information. The authors demonstrate that Differential Transformer outperforms traditional transformers in various tasks, including long-context modeling, key information retrieval, and hallucination mitigation. Furthermore, Differential Transformer exhibits greater robustness to order permutations in in-context learning and reduces activation outliers, paving the way for more efficient quantization. These advantages position Differential Transformer as a promising foundation architecture for future large language model development.
Read the research here: https://arxiv.org/pdf/2410.05258
71 episodes
Share
