If bigco knows an image is likely bad, they would not have allowed it to be generated in the first place. So the attacks are most likely coming from (finetuned) oss models anyway.

🧵(4/4) Learn more:
Paper: arxiv.org/abs/2407.15462
Code: github.com/bailuding/rails
Interested in pushing the boundaries of foundational technologies together? Find @bailuding.bsky.social or I at WWW 2025 in Sydney or reach out!

#TheWebConf25 #WWW2025 #RAG #RecSys #LLMs #NeuralRetrieval

🧵(3/4) MoL achieves dense retrieval-level speed on GPUs, while delivering 20-30% better Hit Rate@50-400 on 100M+ item DBs (arxiv.org/abs/2306.04039, arxiv.org/abs/2407.13218). These gains make a compelling case for migrating web-scale vector databases to Retrieval with Learned Similarities (RAILS).

🧵(2/4) Our paper introduces Mixture-of-Logits (MoL) - a universal approximator of similarity functions unifying sparse/dense retrieval, multi-embeddings, and generative approaches. Beyond theory, MoL achieves SotA from sequential retrieval on Transformers/HSTU to finetuning LMs for RAG/QA.

Issue is, we've already had a historic opportunity to make social media fully user-controlled and unbiased, yet our lawmakers chose to optimize for tech oligarchs' net worth instead...

The flood of papers in turn pushes flashy marketing and rushed publishing over substance. A self-reinforcing cycle that we should break.

Source email is under Exhibit 13 for anyone else wondering www.courtlistener.com/docket/69013...

Do you happen to have links to the actual emails?