How does one run inference for a generative autoregressive language model that has been trained with a fixed context size? Streaming LLMs combine the performance of windowed attention, but avoid the drop in performance by using attention sinks - an interesting phenomenon where the token at position 0 acts as an absorber of "extra" attention.

OUTLINE:

0:00 - Introduction

1:20 - What is the problem?

10:30 - The hypothesis: Attention Sinks

15:10 - Experimental evidence

18:45 - Streaming LLMs

20:45 - Semantics or position?

22:30 - Can attention sinks be learned?

27:45 - More experiments

30:10 - Comparison to Big Bird

Paper: https://arxiv.org/abs/2309.17453

Abstract:

Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses two major challenges. Firstly, during the decoding stage, caching previous tokens' Key and Value states (KV) consumes extensive memory. Secondly, popular LLMs cannot generalize to longer texts than the training sequence length. Window attention, where only the most recent KVs are cached, is a natural approach -- but we show that it fails when the text length surpasses the cache size. We observe an interesting phenomenon, namely attention sink, that keeping the KV of initial tokens will largely recover the performance of window attention. In this paper, we first demonstrate that the emergence of attention sink is due to the strong attention scores towards initial tokens as a ``sink'' even if they are not semantically important. Based on the above analysis, we introduce StreamingLLM, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence lengths without any fine-tuning. We show that StreamingLLM can enable Llama-2, MPT, Falcon, and Pythia to perform stable and efficient language modeling with up to 4 million tokens and more. In addition, we discover that adding a placeholder token as a dedicated attention sink during pre-training can further improve streaming deployment. In streaming settings, StreamingLLM outperforms the sliding window recomputation baseline by up to 22.2x speedup. Code and datasets are provided at this https URL.

Authors: Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, Mike Lewis

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Promptbreeder is a self-improving self-referential system for automated prompt engineering. Give it a task description and a dataset, and it will automatically come up with appropriate prompts for the task. This is achieved by an evolutionary algorithm where not only the prompts, but also the mutation-prompts are improved over time in a population-based, diversity-focused approach.

OUTLINE:

0:00 - Introduction

2:10 - From manual to automated prompt engineering

10:40 - How does Promptbreeder work?

21:30 - Mutation operators

36:00 - Experimental Results

38:05 - A walk through the appendix

Paper: https://arxiv.org/abs/2309.16797

Abstract:

Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper, we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain. Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way. That is, Promptbreeder is not just improving task-prompts, but it is also improving the mutationprompts that improve these task-prompts. Promptbreeder outperforms state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve Prompting on commonly used arithmetic and commonsense reasoning benchmarks. Furthermore, Promptbreeder is able to evolve intricate task-prompts for the challenging problem of hate speech classification.

Authors: Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel

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Retention is an alternative to Attention in Transformers that can both be written in a parallel and in a recurrent fashion. This means the architecture achieves training parallelism while maintaining low-cost inference. Experiments in the paper look very promising.

OUTLINE:

0:00 - Intro

2:40 - The impossible triangle

6:55 - Parallel vs sequential

15:35 - Retention mechanism

21:00 - Chunkwise and multi-scale retention

24:10 - Comparison to other architectures

26:30 - Experimental evaluation

Paper: https://arxiv.org/abs/2307.08621

Abstract:

In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modeling, which supports three computation paradigms, i.e., parallel, recurrent, and chunkwise recurrent. Specifically, the parallel representation allows for training parallelism. The recurrent representation enables low-cost O(1) inference, which improves decoding throughput, latency, and GPU memory without sacrificing performance. The chunkwise recurrent representation facilitates efficient long-sequence modeling with linear complexity, where each chunk is encoded parallelly while recurrently summarizing the chunks. Experimental results on language modeling show that RetNet achieves favorable scaling results, parallel training, low-cost deployment, and efficient inference. The intriguing properties make RetNet a strong successor to Transformer for large language models. Code will be available at this https URL.

Authors: Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei

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ReST uses a bootsrap-like method to produce its own extended dataset and trains on ever higher-quality subsets of it to improve its own reward. The method allows for re-using the same generated data multiple times and thus has an efficiency advantage with respect to Online RL techniques like PPO.

Paper: https://arxiv.org/abs/2308.08998

Abstract:

Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL), which we call Reinforced Self-Training (ReST). Given an initial LLM policy, ReST produces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms. ReST is more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner.

Authors: Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, Nando de Freitas

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Your regular irregular update on the world of Machine Learning.

References:

https://twitter.com/ylecun/status/1681336284453781505

https://ai.meta.com/llama/

https://about.fb.com/news/2023/07/llama-2-statement-of-support/

https://247wallst.com/special-report/2023/08/12/this-is-the-biggest-social-media-platform-ranking-the-worlds-largest-networking-sites/4/

https://github.com/Alpha-VLLM/LLaMA2-Accessory

https://together.ai/blog/llama-2-7b-32k?s=09&utm_source=pocket_saves

https://github.com/imoneoi/openchat

https://twitter.com/lmsysorg/status/1686794639469371393?s=09&t=sS3awkbavmSMSmwp64Ef4A&utm_source=pocket_saves

https://huggingface.co/lmsys/vicuna-13b-v1.5-16k

https://blog.google/outreach-initiatives/public-policy/google-microsoft-openai-anthropic-frontier-model-forum/

https://www.earthdata.nasa.gov/news/impact-ibm-hls-foundation-model?utm_source=pocket_reader

https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M

https://ai.meta.com/blog/generative-ai-text-images-cm3leon/

https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action?utm_source=twitter&utm_medium=social&utm_campaign=rt2

https://arxiv.org/abs/2307.14334

https://sites.research.google/med-palm/

https://open-catalyst.metademolab.com/?utm_source=twitter&utm_medium=organic_social&utm_campaign=opencatalyst&utm_content=card

https://open-catalyst.metademolab.com/demo

https://www.anthropic.com/index/claude-2?utm_source=pocket_reader

https://claude.ai/login

https://audiocraft.metademolab.com/?utm_source=pocket_saves

https://venturebeat.com/programming-development/stability-ai-launches-stablecode-an-llm-for-code-generation/

https://stability.ai/blog/stablecode-llm-generative-ai-coding

https://twitter.com/JeffDean/status/1686806525862608896?s=09&t=LG2z9ok9QExHbSy0fvBsxA&utm_source=pocket_saves

https://sites.research.google/open-buildings/

https://twitter.com/deliprao/status/1687283117873106946?s=09&t=1NmC-B55Z8IuF_HTuGOo7w&utm_source=pocket_saves

https://arxiv.org/pdf/2308.01320.pdf

https://twitter.com/javilopen/status/1687795349719547905?utm_source=pocket_saves

https://research.nvidia.com/labs/par/Perfusion/

https://ar5iv.labs.arxiv.org/html/2307.14936

https://www.linkedin.com/feed/update/urn:li:activity:7093463974750371840/?utm_source=pocket_saves

https://huggingface.co/syzymon/long_llama_3b_instruct

https://arxiv.org/abs/2307.03170

https://dynalang.github.io/

https://github.com/mlfoundations/open_flamingo

https://twitter.com/akshay_pachaar/status/1687079353937698816?s=09&t=fos8QSCsGEEM6dMflhq0Mg&utm_source=pocket_saves

https://github.com/OpenBMB/ToolBench

https://llm-attacks.org/

https://arstechnica.com/information-technology/2023/07/openai-discontinues-its-ai-writing-detector-due-to-low-rate-of-accuracy/

https://sites.google.com/view/steve-1

https://github.com/Shalev-Lifshitz/STEVE-1

https://erichartford.com/dolphin

https://huggingface.co/ehartford/dolphin-llama-13b

https://www.mosaicml.com/blog/long-context-mpt-7b-8k

https://twitter.com/camenduru/status/1688045780244848640?s=09&t=ubJ2Qtz-TG6Xo3_GMtt2Cw&utm_source=pocket_saves

https://github.com/IDEA-Research/DWPose

https://twitter.com/tri_dao/status/1680987577913065472?s=09&t=Q181vFmM6d3nDq-5BwfDeg&utm_source=pocket_saves

https://tridao.me/publications/flash2/flash2.pdf

https://thehackernews.com/2023/07/wormgpt-new-ai-tool-allows.html

https://www.tomshardware.com/news/ai-steals-data-with-keystroke-audio

https://arxiv.org/pdf/2308.01074.pdf

https://www.foxnews.com/politics/ai-test-flight-air-force-unmanned-wingman-aircraft

https://www.theverge.com/2023/8/2/23817406/white-castle-soundhound-ai-sliders

https://www.google.com/search?sca_esv=556495916&q=food+delivery+bot+kicked&tbm=vid&source=lnms&sa=X&ved=2ahUKEwjZ6PDPrdmAAxUThf0HHWzrBGgQ0pQJegQIChAB&cshid=1691920142432720&biw=2327&bih=1180&dpr=2.2

https://www.youtube.com/watch?v=--n_NhmXnfc

https://www.thesun.co.uk/tech/20793591/coop-delivery-robots-cambridge-kicked-by-workers-tiktok/

An interview with Sergey Shykevich, Threat Intelligence Group Manager at Check Point, about how models like ChatGPT have impacted the realm of cyber crime.

https://threatmap.checkpoint.com/

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A prime example of intellectual dishonesty of journalists and AI critics.

Article: https://gizmodo.com/paknsave-ai-savey-recipe-bot-chlorine-gas-1850725057

My Recipe AI: https://github.com/yk/recipe-ai

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An interview with DeepFloyd members Misha Konstantinov and Daria Bakshandaeva on the release of the model IF, an open-source model following Google's implementation of Imagen.

References:

https://www.deepfloyd.ai/deepfloyd-if

https://huggingface.co/DeepFloyd

https://twitter.com/_gugutse_

https://twitter.com/_bra_ket

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A new paper claims to use GPT-4 to solve 100% of a set of MIT university exercises. Some people are skeptic and their investigations reveal more than one problem with this paper...

OUTLINE:

0:00 - ChatGPT gives out Windows 10 keys

0:30 - MIT exam paper

2:50 - Prompt engineering

5:30 - Automatic grading

6:45 - Response by other MIT students

8:30 - Unsolvable questions

10:50 - Duplicates

13:30 - Cascading the heuristics

22:40 - Other problems

29:25 - OpenLLaMA 13B published

References:

https://twitter.com/immasiddtweets/status/1669721470006857729/photo/1https://arxiv.org/abs/2306.08997https://arxiv.org/pdf/2306.08997.pdfhttps://flower-nutria-41d.notion.site/No-GPT4-can-t-ace-MIT-b27e6796ab5a48368127a98216c76864https://github.com/idrori/MITQ/commit/3feee1026318e537c0ad27968001ef76e4a36890https://twitter.com/hardmaru/status/1670246674760077312https://twitter.com/giffmana/status/1670258748286472193https://twitter.com/T3816440886465/status/1670127224131862531https://twitter.com/qrdl/status/1669856336652414977https://www.chegg.com/homework-help/questions-and-answers/consider-mdp-set-possible-states-mathcal-s-0-1-2-3-set-possible-actions-mathcal-b-c--rewar-q111042613https://github.com/openlm-research/open_llamahttps://huggingface.co/openlm-research/open_llama_13b

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Watermarking the outputs of generative models is usually done as a post-processing step on the model outputs. Tree-Ring Watermarks are applied in the latent space at the beginning of a diffusion process, which makes them nearly undetectable, robust to strong distortions, and only recoverable by the model author. It is a very promising technique with applications potentially beyond watermarking itself.

OUTLINE:

0:00 - Introduction & Overview

1:30 - Why Watermarking?

4:20 - Diffusion Models Recap

13:40 - Inverting Diffusion Models

17:05 - Tree-Ring Watermarking

26:15 - Effects of Tree-Ring Watermarks

30:00 - Experimental Results

32:40 - Limitations

34:40 - Conclusion

Paper: https://arxiv.org/abs/2305.20030

Abstract:

Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-hoc modifications to images after sampling, Tree-Ring Watermarking subtly influences the entire sampling process, resulting in a model fingerprint that is invisible to humans. The watermark embeds a pattern into the initial noise vector used for sampling. These patterns are structured in Fourier space so that they are invariant to convolutions, crops, dilations, flips, and rotations. After image generation, the watermark signal is detected by inverting the diffusion process to retrieve the noise vector, which is then checked for the embedded signal. We demonstrate that this technique can be easily applied to arbitrary diffusion models, including text-conditioned Stable Diffusion, as a plug-in with negligible loss in FID. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed. Code is available at this https URL.

Authors: Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein

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