Douwe Kiela @douwekiela
@ContextualAI CEO, @Stanford Adjunct Prof. Bay Area Joined July 2013-
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RAG 2.0, Finally Getting RAG Right! by @TheTechOasis1 pub.towardsai.net/rag-2-0-finall…
Maximizing expected human utility (i.e., KTO) is the natural way to do alignment. Cool to see how well this works even in diffusion models.
Maximizing expected human utility (i.e., KTO) is the natural way to do alignment. Cool to see how well this works even in diffusion models.
Join us on April 10 as we take part in #GoogleCloudNext. Our CEO @douwekiela will dive into retrieval-augmented generation (RAG), which he pioneered at Facebook, and share how Contextual AI's RAG 2.0 approach is key to #generativeAI deployment in the #enterprise. Register to join…
WHO DID THIS? rag1.ai redirects to Pinecone!!
With RAG 2.0, the generator and retriever are always working together. Whether you're building a house or enterprise-grade AI, teamwork makes the dream work
Amazing! Great update by @yanndubs and team. @ContextualAI's open source 7b model still beats Mistral Medium and Claude 2, I'll take it :)
Amazing! Great update by @yanndubs and team. @ContextualAI's open source 7b model still beats Mistral Medium and Claude 2, I'll take it :) https://t.co/6rfvJFK67C
AI News: @xai, @Apple, @nvidia, @cohere, @ContextualAI Links: Apple's MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training - arxiv.org/pdf/2403.09611… Cohere int8 & binary Embeddings - Scale Your Vector Database to Large Datasets: txt.cohere.com/int8-binary-em……
We're at #GTC24! Join us on March 20th to hear @apsdehal share how we developed Kahneman Tversky Optimization (KTO) to speed up the #LLM and human feedback loop for #generativeai
Looks like I picked the correct place to do an internship 😅. Great results by @winniethexu
Looks like I picked the correct place to do an internship 😅. Great results by @winniethexu
Excited to share a new model with @ContextualAI that tops the AlpacaEval 2.0 leaderboard! How did we manage to rank higher than models like GPT4, Claude 3 and Mistral Medium? Enter iterative alignment… 🧵
We all share responsibility for building AI that improves lives & unlocks a better future for humanity. @svangel makes this pledge, and we are proud to initiate this Open Letter: Build AI for a Better Future. Please join @OpenAI, @Meta, @Google, @ycombinator, @huggingface,…
The Orca-Math paper does a comparison of DPO and KTO for mathematical reasoning, finding that KTO is slightly better when all data is used and 25+ pts better when you have fewer positive examples than negative examples.
The Orca-Math paper does a comparison of DPO and KTO for mathematical reasoning, finding that KTO is slightly better when all data is used and 25+ pts better when you have fewer positive examples than negative examples. https://t.co/7zyzIaRuMY
Selecting the right data is critical for LLM performance across all stages of training. This recent paper surveys data selection, and shows that there is much more exciting research to be done.
Selecting the right data is critical for LLM performance across all stages of training. This recent paper surveys data selection, and shows that there is much more exciting research to be done.
You might have missed it among the releases from Big Tech in the past week, but GritLM definitely caught our eye! This model unifies both text generation and embedding into one architecture, surpassing all openly available models on MTEB. Here are our takeaways from the paper:…
Introducing GRIT: one single language model that is instruction-tuned to be good at both representation and generation, at the same time. GritLM is open state of the art on *both* generative and embedding benchmarks + big speedups in RAG. Read more here: contextual.ai/training-with-…
(((ل()(ل() 'yoav))).. @yoavgo
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224 Followers 280 Following On job market actively seeking research positions. Mostly working on #IR, #RAG and #NLProc. Earned MS degree at Graduate School of KAIST AI.
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(((ل()(ل() 'yoav))).. @yoavgo
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Daniel Han @danielhanchen
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Patrick Lewis @PSH_Lewis
4K Followers 655 Following London-based AI/NLP Research Scientist. I co-lead the RAG & tool use team at Cohere w/ @s_hofstaetter. Previous Fundamental AI Research at Meta AI, FAIR, UCL AI
Adina Williams @adinamwilliams
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Ricky T. Q. Chen @RickyTQChen
4K Followers 809 Following Research Scientist at FAIR NY, Meta. I build simplified abstractions of the world through the lens of dynamics and flows.
Mike Lewis @ml_perception
6K Followers 227 Following Llama3 pre-training lead. Partially to blame for things like the Cicero Diplomacy bot, BART, RoBERTa, kNN-LM, top-k sampling & Deal Or No Deal.
rishi @RishiBommasani
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3K Followers 2K Following Research Scinetist @ Google Research, Professor in Computer Science; HCI, Crowdsourcing, Human Computation, RecSystems, SemWeb,, AI, ML, ActiveLearning
🆕 on @airstreetpress: we delve into the murky world of frontier model economics, asking whether bigger, badder, and faster really is the future for most applications. Some highlights below 🧵
RAG 2.0, Finally Getting RAG Right! by @TheTechOasis1 pub.towardsai.net/rag-2-0-finall…
Interesting graphic from @ContextualAI on their RAG 2.0 product: According to their findings, RAG 2.0 scales far better than existing solutions as context length grows
Let's talk about @ContextualAI building the next generation of RAG for enterprise customers → goo.gle/3U18U2C #TheNewWayToCloud
Join us on April 10 as we take part in #GoogleCloudNext. Our CEO @douwekiela will dive into retrieval-augmented generation (RAG), which he pioneered at Facebook, and share how Contextual AI's RAG 2.0 approach is key to #generativeAI deployment in the #enterprise. Register to join…
a now-mandatory periodic reminder on the brittleness of prompting: proceedings.neurips.cc/paper_files/pa… with @EthanJPerez and @douwekiela
I've left OpenAI. I'm mostly taking some time to rest. But I also have a few projects in the oven 🧑🍳 Here's one that I'm really excited about: we have a 🚨new paper🚨 out on aligning AI with human values, with the folk at @meaningaligned!! 😊✨🎉 Why I think it's cool: 🧵
“What are human values, and how do we align to them?” Very excited to release our new paper on values alignment, co-authored with @ryan_t_lowe and funded by @OpenAI. 📝: meaningalignment.org/values-and-ali…
This was one of the best talks from @saastr AI day with @douwekiela on RAG2 and building AI for enterprise - the model is only ~10-20% of the system - specialized models have better trade offs, since the atomic unit in enterprise is the specific workflows
Our CEO @douwekiela spoke at @saastr 's AI Day yesterday - want to know what it takes to build AI products for the enterprise? Watch the recording here: youtube.com/live/NOAcuI7qa…
RewardBench updates: * Reduced weight of prior sets to 50% so people can't easily gamify by training on Anthropic HH / Summarize etc * Models: Archangel KTO suite from @ContextualAI and @ethayarajh, showing scaling of Llama 1 and Pythia suite on constant data. The first DPO like…
With RAG 2.0, the generator and retriever are always working together. Whether you're building a house or enterprise-grade AI, teamwork makes the dream work
Tool use should also be optimized end-to-end like RAG.
Today, we’re excited to announce RAG 2.0, our end-to-end system for developing production-grade AI. Using RAG 2.0, we’ve created Contextual Language Models (CLMs), which achieve state-of-the-art performance on a variety of industry benchmarks. CLMs outperform strong RAG…
from @douwekiela (a pioneer of Retrieval-Augmented Generation) on the value of end-to-end co-optimization of systems (language models + retrievers):
Today, we’re excited to announce RAG 2.0, our end-to-end system for developing production-grade AI. Using RAG 2.0, we’ve created Contextual Language Models (CLMs), which achieve state-of-the-art performance on a variety of industry benchmarks. CLMs outperform strong RAG…
@ContextualAI Sounds like you're pushing the boundaries of NLP with RAG 2.0! 🚀 With CLMs setting new records, it'll be fascinating to see how they transform the AI landscape. Can't wait to dive into your benchmarks and see those results in action!
Check out @ContextualAI's great work on RAG 2.0 that trains a RAG system end-to-end. I'm glad to see more and more work using freshness (w/ FreshQA) as one of the evaluation criteria.
Our first set of RAG 2.0 models, Contextual Language Models (CLMs), significantly improve performance over current systems across axes critical for enterprise work: open-domain question answering, faithfulness, and freshness.
Closed models, often more concise, got a boost on LC AE. But open models are still looking strong: -@Alibaba_Qwen 72B-chat @JustinLin610 @huybery -@SnorkelAI’s Mistral PairRM @HoangTranDV -@ContextualAI’s Mistral KTO @winniethexu -@allen_ai’s Yi-34B PairRM @billyuchenlin ...
Big thanks to -@gblazex @percyliang @tatsu_hashimoto -@appenz @srush_nlp @WenhuChen @douwekiela @abacaj @DimitrisPapail @Teknium1 @natolambert @HoangTranDV and others for raising concerns about length. I agree(d) with each of those! -the community who made PRs for > 80 models!
AlpacaEval is now length-controlled (LC)! ✅ highest correlation with Chat Arena (0.98) ✅ no reannotation ✅ simple interpretation: win rate if model length = baseline length ✅ robust to length gamification 0.98 that’s essentially evaluation on Arena but in 3min and <$10.
contextual . onlybangers . ai
Today, we’re excited to announce RAG 2.0, our end-to-end system for developing production-grade AI. Using RAG 2.0, we’ve created Contextual Language Models (CLMs), which achieve state-of-the-art performance on a variety of industry benchmarks. CLMs outperform strong RAG…
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