Anthony Chen @_anthonychen
engineering software engineers @googledeepmind ♊️ phd from @ucirvine anthonywchen.github.io little worm in big apple Joined May 2017-
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Instructions/reasoning are now everywhere in retrieval - we want embeddings to do it all! 🚀 But... is it even possible? 🤔 Turns out, it's not possible for single-vector models 😱 theoretically and empirically! To make it obvious we OSS a simple eval SoTA models flop on! 🧵
What if you could not only watch a generated video, but explore it too? 🌐 Genie 3 is our groundbreaking world model that creates interactive, playable environments from a single text prompt. From photorealistic landscapes to fantasy realms, the possibilities are endless. 🧵
Bon appétit 🍝
BREAKING: Gemini 2.5 Pro is now #1 on the Arena leaderboard - the largest score jump ever (+40 pts vs Grok-3/GPT-4.5)! 🏆 Tested under codename "nebula"🌌, Gemini 2.5 Pro ranked #1🥇 across ALL categories and UNIQUELY #1 in Math, Creative Writing, Instruction Following, Longer…
BREAKING: Gemini 2.5 Pro is now #1 on the Arena leaderboard - the largest score jump ever (+40 pts vs Grok-3/GPT-4.5)! 🏆 Tested under codename "nebula"🌌, Gemini 2.5 Pro ranked #1🥇 across ALL categories and UNIQUELY #1 in Math, Creative Writing, Instruction Following, Longer… https://t.co/LAQguBUNZK
Think you know Gemini? 🤔 Think again. Meet Gemini 2.5: our most intelligent model 💡 The first release is Pro Experimental, which is state-of-the-art across many benchmarks - meaning it can handle complex problems and give more accurate responses. Try it now →…
Historic.
GDM's work converting Gemini into a SOTA dual encoder is now out! SOTA results across all benchmarks including exceptionally strong coding performance. Check out the tech report for more details and some interesting ablations!
GDM's work converting Gemini into a SOTA dual encoder is now out! SOTA results across all benchmarks including exceptionally strong coding performance. Check out the tech report for more details and some interesting ablations!
This is the most gut-wrenching blog I've read, because it's so real and so close to heart. The author is no longer with us. I'm in tears. AI is not supposed to be 200B weights of stress and pain. It used to be a place of coffee-infused eureka moments, of exciting late-night arxiv…
Thanks for sharing our paper!
Thrilled to unveil LOFT, our latest research showing how long-context language models like Gemini can subsume retrieval, RAG and more, right out of the box. This is the dream-retriever I've always wanted! No special training, amazing reasoning, few-shot promptable...🤯
Thrilled to unveil LOFT, our latest research showing how long-context language models like Gemini can subsume retrieval, RAG and more, right out of the box. This is the dream-retriever I've always wanted! No special training, amazing reasoning, few-shot promptable...🤯
Very happy to contribute to the Multimodal benchmarking on the LOFT project! Very excited to see with fewshot prompting only, Gemini 1.5 pro can already outperform CLIP on Coco, Flickr, by a large margin on 1M context tokens!
Very happy to contribute to the Multimodal benchmarking on the LOFT project! Very excited to see with fewshot prompting only, Gemini 1.5 pro can already outperform CLIP on Coco, Flickr, by a large margin on 1M context tokens!
Thanks for sharing our work!
Thanks for sharing our work!
"just put the corpus into the context"! Long context models can already match or beat various bespoke pipelines and infra in accuracy on non-trivial tasks! Hadn't expected this so soon, and honestly was hoping to milk RAG impact for a little longer 🤪
"just put the corpus into the context"! Long context models can already match or beat various bespoke pipelines and infra in accuracy on non-trivial tasks! Hadn't expected this so soon, and honestly was hoping to milk RAG impact for a little longer 🤪
Do long-context LMs obsolete retrieval, RAG, SQL and more? Excited to share our answer! arxiv.org/abs/2406.13121 from the team at @GoogleDeepMind that wrote one of the 1st papers on RAG (REALM) and repeat SOTA on retrieval (Promptagator, Gecko). w/ Gemini 1.5 Pro, the answer is 🧵
Do long-context LMs obsolete retrieval, RAG, SQL and more? Excited to share our answer! arxiv.org/abs/2406.13121 from the team at @GoogleDeepMind that wrote one of the 1st papers on RAG (REALM) and repeat SOTA on retrieval (Promptagator, Gecko). w/ Gemini 1.5 Pro, the answer is 🧵
Can long-context language models (LCLMs) subsume retrieval, RAG, SQL, and more? Introducing LOFT: a benchmark stress-testing LCLMs on million-token tasks like retrieval, RAG, and SQL. Surprisingly, LCLMs rival specialized models trained for these tasks! arxiv.org/abs/2406.13121
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