Yannic Kilcher Videos (Audio Only)

#nlp #sparsity #transformers This video is an interview with Barret Zoph and William Fedus of Google Brain about Sparse Expert Models. Sparse Expert models have been hugely successful at distributing parts of models, mostly Transformers, across large array of machines and use a routing function to effectively route signals between them. This means that even though these models have a huge number of parameters, the computational load for a given signal does not increase because the model is only sparsely activated. Sparse expert models, such as Switch Transformers and GLAM can scale up to trillions of parameters and bring a number of desirable properties. We discuss everything from the fundamentals, history, strengths and weaknesses, up to the current state of the art of these models. OUTLINE: 0:00 - Intro 0:30 - What are sparse expert models? 4:25 - Start of Interview 5:55 - What do you mean by sparse experts? 8:10 - How does routing work in these models? 12:10 - What is the history of sparse experts? 14:45 - What does an individual expert learn? 19:25 - When are these models appropriate? 22:30 - How comparable are sparse to dense models? 26:30 - How does the pathways system connect to this? 28:45 - What improvements did GLAM make? 31:30 - The "designing sparse experts" paper 37:45 - Can experts be frozen during training? 41:20 - Can the routing function be improved? 47:15 - Can experts be distributed beyond data centers? 50:20 - Are there sparse experts for other domains than NLP? 52:15 - Are sparse and dense models in competition? 53:35 - Where do we go from here? 56:30 - How can people get started with this? Papers: Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (https://arxiv.org/abs/2101.03961) GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (https://arxiv.org/abs/2112.06905) Designing Effective Sparse Expert Models (https://arxiv.org/abs/2202.08906) Links: Merch: store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#neuralsearch #interview #google This is an interview with the authors Yi Tay and Don Metzler. Paper Review Video: https://youtu.be/qlB0TPBQ7YY Search engines work by building an index and then looking up things in it. Usually, that index is a separate data structure. In keyword search, we build and store reverse indices. In neural search, we build nearest-neighbor indices. This paper does something different: It directly trains a Transformer to return the ID of the most relevant document. No similarity search over embeddings or anything like this is performed, and no external data structure is needed, as the entire index is essentially captured by the model's weights. The paper experiments with various ways of representing documents and training the system, which works surprisingly well! OUTLINE: 0:00 - Intro 0:50 - Start of Interview 1:30 - How did this idea start? 4:30 - How does memorization play into this? 5:50 - Why did you not compare to cross-encoders? 7:50 - Instead of the ID, could one reproduce the document itself? 10:50 - Passages vs documents 12:00 - Where can this model be applied? 14:25 - Can we make this work on large collections? 19:20 - What's up with the NQ100K dataset? 23:55 - What is going on inside these models? 28:30 - What's the smallest scale to obtain meaningful results? 30:15 - Investigating the document identifiers 34:45 - What's the end goal? 38:40 - What are the hardest problems currently? 40:40 - Final comments & how to get started Paper: https://arxiv.org/abs/2202.06991 Abstract: In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model. To this end, we introduce the Differentiable Search Index (DSI), a new paradigm that learns a text-to-text model that maps string queries directly to relevant docids; in other words, a DSI model answers queries directly using only its parameters, dramatically simplifying the whole retrieval process. We study variations in how documents and their identifiers are represented, variations in training procedures, and the interplay between models and corpus sizes. Experiments demonstrate that given appropriate design choices, DSI significantly outperforms strong baselines such as dual encoder models. Moreover, DSI demonstrates strong generalization capabilities, outperforming a BM25 baseline in a zero-shot setup. Authors: Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, Donald Metzler Links: Merch: store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#dsi #search #google Search engines work by building an index and then looking up things in it. Usually, that index is a separate data structure. In keyword search, we build and store reverse indices. In neural search, we build nearest-neighbor indices. This paper does something different: It directly trains a Transformer to return the ID of the most relevant document. No similarity search over embeddings or anything like this is performed, and no external data structure is needed, as the entire index is essentially captured by the model's weights. The paper experiments with various ways of representing documents and training the system, which works surprisingly well! Sponsor: Diffgram https://diffgram.com?ref=yannic OUTLINE: 0:00 - Intro 0:45 - Sponsor: Diffgram 1:35 - Paper overview 3:15 - The search problem, classic and neural 8:15 - Seq2seq for directly predicting document IDs 11:05 - Differentiable search index architecture 18:05 - Indexing 25:15 - Retrieval and document representation 33:25 - Training DSI 39:15 - Experimental results 49:25 - Comments & Conclusions Paper: https://arxiv.org/abs/2202.06991 Abstract: In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model. To this end, we introduce the Differentiable Search Index (DSI), a new paradigm that learns a text-to-text model that maps string queries directly to relevant docids; in other words, a DSI model answers queries directly using only its parameters, dramatically simplifying the whole retrieval process. We study variations in how documents and their identifiers are represented, variations in training procedures, and the interplay between models and corpus sizes. Experiments demonstrate that given appropriate design choices, DSI significantly outperforms strong baselines such as dual encoder models. Moreover, DSI demonstrates strong generalization capabilities, outperforming a BM25 baseline in a zero-shot setup. Authors: Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, Donald Metzler Links: Merch: store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#mlnews #palm #dalle2 Google releases PaLM and OpenAI releases DALL-E 2 (and more news). Sponsor: Weights & BIases Start here: https://wandb.me/yannic Thumbnail credit: DALL-E 2 via Sam Altman OUTLINE 0:00 - Street interview w/ random stranger 2:25 - Intro 2:50 - PaLM - Google's 540B Pathways Language Model 7:50 - Sponsor: Weights & Biases 9:10 - OpenAI releases DALL-E 2 12:05 - Open Source Datasets and Models 13:20 - Salesforce releases CodeGen My Live Reaction to DALL-E 2: https://youtu.be/gGPv_SYVDC8 My Video on GLIDE: https://youtu.be/gwI6g1pBD84 My Video on the Pathways System: https://youtu.be/vGFaiLeoLWw References: PaLM - Google's 540B Pathways Language Model https://ai.googleblog.com/2022/04/pat... https://storage.googleapis.com/pathwa... OpenAI releases DALL-E 2 https://openai.com/dall-e-2/ https://cdn.openai.com/papers/dall-e-... https://www.instagram.com/openaidalle/ https://twitter.com/sama/status/15117... https://twitter.com/sama/media https://twitter.com/BorisMPower/statu... https://twitter.com/ariskonstant/stat... Open Source Datasets and Models https://twitter.com/multimodalart/sta... https://laion.ai/laion-5b-a-new-era-o... https://github.com/mlfoundations/open... Salesforce releases CodeGen https://github.com/salesforce/CodeGen Links: Merch: store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#aiart #deeplearning #clip Since the release of CLIP, the world of AI art has seen an unprecedented level of acceleration in what's possible to do. Whereas image generation had previously been mostly in the domain of scientists, now a community of professional artists, researchers, and amateurs are sending around colab notebooks and sharing their creations via social media. How did this happen? What is going on? And where do we go from here? Jack Morris and I attempt to answer some of these questions, following his blog post "The Weird and Wonderful World of AI Art" (linked below). OUTLINE: 0:00 - Intro 2:30 - How does one get into AI art? 5:00 - Deep Dream & Style Transfer: the early days of art in deep learning 10:50 - The advent of GANs, ArtBreeder and TikTok 19:50 - Lacking control: Pre-CLIP art 22:40 - CLIP & DALL-E 30:20 - The shift to shared colabs 34:20 - Guided diffusion models 37:20 - Prompt engineering for art models 43:30 - GLIDE 47:00 - Video production & Disco Diffusion 48:40 - Economics, money, and NFTs 54:15 - What does the future hold for AI art? Blog post: https://jxmo.notion.site/The-Weird-an... Jack's Blog: https://jxmo.io/ Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#reinforcementlearning #ai #explained This is an interview with Jesse Mu, first author of the paper. Original Paper Review: https://youtu.be/NeGJAUSQEJI Exploration is one of the oldest challenges for Reinforcement Learning algorithms, with no clear solution to date. Especially in environments with sparse rewards, agents face significant challenges in deciding which parts of the environment to explore further. Providing intrinsic motivation in form of a pseudo-reward is sometimes used to overcome this challenge, but often relies on hand-crafted heuristics, and can lead to deceptive dead-ends. This paper proposes to use language descriptions of encountered states as a method of assessing novelty. In two procedurally generated environments, they demonstrate the usefulness of language, which is in itself highly concise and abstractive, which lends itself well for this task. OUTLINE: 0:00 - Intro 0:55 - Paper Overview 4:30 - Aren't you just adding extra data? 9:35 - Why are you splitting up the AMIGo teacher? 13:10 - How do you train the grounding network? 16:05 - What about causally structured environments? 17:30 - Highlights of the experimental results 20:40 - Why is there so much variance? 22:55 - How much does it matter that we are testing in a video game? 27:00 - How does novelty interface with the goal specification? 30:20 - The fundamental problems of exploration 32:15 - Are these algorithms subject to catastrophic forgetting? 34:45 - What current models could bring language to other environments? 40:30 - What does it take in terms of hardware? 43:00 - What problems did you encounter during the project? 46:40 - Where do we go from here? Paper: https://arxiv.org/abs/2202.08938 Abstract: Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One common solution is to use intrinsic rewards to encourage agents to explore their environment. However, recent intrinsic exploration methods often use state-based novelty measures which reward low-level exploration and may not scale to domains requiring more abstract skills. Instead, we explore natural language as a general medium for highlighting relevant abstractions in an environment. Unlike previous work, we evaluate whether language can improve over existing exploration methods by directly extending (and comparing to) competitive intrinsic exploration baselines: AMIGo (Campero et al., 2021) and NovelD (Zhang et al., 2021). These language-based variants outperform their non-linguistic forms by 45-85% across 13 challenging tasks from the MiniGrid and MiniHack environment suites. Authors: Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah Goodman, Tim Rocktäschel, Edward Grefenstette Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m

#reinforcementlearning #ai #explained Exploration is one of the oldest challenges for Reinforcement Learning algorithms, with no clear solution to date. Especially in environments with sparse rewards, agents face significant challenges in deciding which parts of the environment to explore further. Providing intrinsic motivation in form of a pseudo-reward is sometimes used to overcome this challenge, but often relies on hand-crafted heuristics, and can lead to deceptive dead-ends. This paper proposes to use language descriptions of encountered states as a method of assessing novelty. In two procedurally generated environments, they demonstrate the usefulness of language, which is in itself highly concise and abstractive, which lends itself well for this task. OUTLINE: 0:00 - Intro 1:10 - Paper Overview: Language for exploration 5:40 - The MiniGrid & MiniHack environments 7:00 - Annotating states with language 9:05 - Baseline algorithm: AMIGo 12:20 - Adding language to AMIGo 22:55 - Baseline algorithm: NovelD and Random Network Distillation 29:45 - Adding language to NovelD 31:50 - Aren't we just using extra data? 34:55 - Investigating the experimental results 40:45 - Final comments Paper: https://arxiv.org/abs/2202.08938 Abstract: Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One common solution is to use intrinsic rewards to encourage agents to explore their environment. However, recent intrinsic exploration methods often use state-based novelty measures which reward low-level exploration and may not scale to domains requiring more abstract skills. Instead, we explore natural language as a general medium for highlighting relevant abstractions in an environment. Unlike previous work, we evaluate whether language can improve over existing exploration methods by directly extending (and comparing to) competitive intrinsic exploration baselines: AMIGo (Campero et al., 2021) and NovelD (Zhang et al., 2021). These language-based variants outperform their non-linguistic forms by 45-85% across 13 challenging tasks from the MiniGrid and MiniHack environment suites. Authors: Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah Goodman, Tim Rocktäschel, Edward Grefenstette Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#mlnews #gpt3 #pathways Your updates on the latest and greatest from the depths of Machine Learning! Sponsor: Weights & Biases https://wandb.me/yannic OUTLINE: 0:00 - Intro 0:15 - Weights & Biases Report about Reports 2:45 - GPT-3 learns to edit 6:30 - Make-A-Scene: Text-to-Image with Human Priors 8:00 - Pathways: Google's new High-Performance ML scheduler 10:45 - DouBlind: Open Peer-Review 12:45 - CLIP meets GamePhysics 14:40 - Residual Quantization pushes Image Generation SOTA 16:15 - Helpful Things References: Weights & Biases Report about Reports https://wandb.ai/wandb/wandb_example/... GPT-3 learns to edit https://openai.com/blog/gpt-3-edit-in... https://beta.openai.com/playground?mo... Make-A-Scene: Text-to-Image with Human Priors https://arxiv.org/pdf/2203.13131.pdf https://www.youtube.com/watch?v=QLTyq... Pathways: Google's new High-Performance ML scheduler https://arxiv.org/pdf/2203.12533.pdf DouBlind: Open Peer-Review https://doublind.com/#web-intro https://doublind.com/search?query=kil... CLIP meets GamePhysics https://arxiv.org/pdf/2203.11096.pdf https://www.reddit.com/r/GamePhysics/... https://asgaardlab.github.io/CLIPxGam... Residual Quantization pushes Image Generation SOTA https://arxiv.org/pdf/2203.01941.pdf https://github.com/kakaobrain/rq-vae-... Helpful Things https://github.com/TDAmeritrade/stumpy https://github.com/linkedin/fasttreeshap https://github.com/vopani/jaxton https://twitter.com/mark_riedl/status... https://github.com/eilab-gt/NovGrid https://developer.nvidia.com/isaac-gym https://github.com/NVIDIA-Omniverse/I... Links: Merch: store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#nlp #gpt3 #prompt This is an interview with the authors of this work, Aman Madaan and Niket Tandon. Large language models such as GPT-3 have enabled many breakthroughs and new applications recently, but they come with an important downside: Training them is very expensive, and even fine-tuning is often difficult. This paper presents an adaptive method to improve performance of such models after deployment, without ever changing the model itself. This is done by maintaining a memory of interactions and then dynamically adapting new prompts by augmenting them with memory content. This has many applications, from non-intrusive fine-tuning to personalization. OUTLINE: 0:00 - Intro 0:45 - Paper Overview 2:00 - What was your original motivation? 4:20 - There is an updated version of the paper! 9:00 - Have you studied this on real-world users? 12:10 - How does model size play into providing feedback? 14:10 - Can this be used for personalization? 16:30 - Discussing experimental results 17:45 - Can this be paired with recommender systems? 20:00 - What are obvious next steps to make the system more powerful? 23:15 - Clarifying the baseline methods 26:30 - Exploring cross-lingual customization 31:00 - Where did the idea for the clarification prompt come from? 33:05 - What did not work out during this project? 34:45 - What did you learn about interacting with large models? 37:30 - Final thoughts Paper: https://arxiv.org/abs/2201.06009 Code & Data: https://github.com/madaan/memprompt Abstract: Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT-3 would mistakenly interpret "What word is similar to good?" to mean a homonym, while the user intended a synonym. Our goal is to effectively correct such errors via user interactions with the system but without retraining, which will be prohibitively costly. We pair GPT-3 with a growing memory of recorded cases where the model misunderstood the user's intents, along with user feedback for clarification. Such a memory allows our system to produce enhanced prompts for any new query based on the user feedback for error correction on similar cases in the past. On four tasks (two lexical tasks, two advanced ethical reasoning tasks), we show how a (simulated) user can interactively teach a deployed GPT-3, substantially increasing its accuracy over the queries with different kinds of misunderstandings by the GPT-3. Our approach is a step towards the low-cost utility enhancement for very large pre-trained LMs. All the code and data is available at this https URL. Authors: Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang Links: Merch: store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#nlp #gpt3 #prompt Large language models such as GPT-3 have enabled many breakthroughs and new applications recently, but they come with an important downside: Training them is very expensive, and even fine-tuning is often difficult. This paper presents an adaptive method to improve performance of such models after deployment, without ever changing the model itself. This is done by maintaining a memory of interactions and then dynamically adapting new prompts by augmenting them with memory content. This has many applications, from non-intrusive fine-tuning to personalization. Sponsor: Introduction to Graph Neural Networks Course https://www.graphneuralnets.com/p/int... OUTLINE: 0:00 - Intro 0:40 - Sponsor: Introduction to GNNs Course (link in description) 1:30 - Paper Overview: Improve GPT-3 after deployment via user feedback 5:30 - Proposed memory-based architecture 13:00 - A detailed look at the components 15:00 - Example tasks 24:30 - My concerns with the example setup 26:20 - Baselines used for comparison 29:50 - Experimental Results 34:20 - Conclusion & Comments Paper: https://arxiv.org/abs/2201.06009 Code & Data: https://github.com/madaan/memprompt Abstract: Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT-3 would mistakenly interpret "What word is similar to good?" to mean a homonym, while the user intended a synonym. Our goal is to effectively correct such errors via user interactions with the system but without retraining, which will be prohibitively costly. We pair GPT-3 with a growing memory of recorded cases where the model misunderstood the user's intents, along with user feedback for clarification. Such a memory allows our system to produce enhanced prompts for any new query based on the user feedback for error correction on similar cases in the past. On four tasks (two lexical tasks, two advanced ethical reasoning tasks), we show how a (simulated) user can interactively teach a deployed GPT-3, substantially increasing its accuracy over the queries with different kinds of misunderstandings by the GPT-3. Our approach is a step towards the low-cost utility enhancement for very large pre-trained LMs. All the code and data is available at this https URL. Authors: Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang Links: Merch: store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n