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Explore more posts
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Gregory Mermoud
Very insightful work by Anthropic’s interpretability team. And an amazing paper, with outstanding writing and figures. The idea is very simple: interpret LLMs by leveraging sparse autoencoders as surrogate models of the MLP of transformer blocks, which allow one to disambiguate the superposition of features captured by a single neuron. A simple idea, but a very careful and complex execution, as it is often the case in our line of work. The paper goes into many details and provide a large array of insights, although the gist of the implementation remains obfuscated due to the closed source nature of Claude. Too bad, because this is the kind of work that we need to better understand and eventually trust LLMs. This is demonstrated by the authors in the section ‘Influence on Behavior’, where they show that clamping some features to either high or low value during inference is “remarkably effective at modifying model outputs in specific, interpretable ways”. Hopefully this kind of work is going to be replicated and generalized to open-weights models, such that we have new ways to steer their behavior. https://lnkd.in/eVym7f_f #interpretability #xai #explainableai #steerableai #anthropic #claude #anthropic
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Dusty Chadwick
Hallucinations in LLMs are inevitable, no seriously! Josh Fritzsche and I have worked hard at Voze to find a way to tune, prompt and even plead 🙏 with LLMs to prevent them from the occasional 🍄 hallucination. It seemed like the better we have done the harder the edge cases have been to solve and eventually we realized that they tend to frequently happen when LLMs predict or make anticipations on "nothing". I remember at one point nearly going insane with frustration because when LLMs had a hallucination, they were EPIC! 😱 Then we pushed through it and benefited for the effort! How did we solve this common problem? We didn't (Not completely), we got better at identifying the issues as they happened. Once we identified in the data we were able to start looking for patterns. The same catalysts that cause it to happen once if left unchanged caused it over and over again. Our solution is astoundingly simple. Once you know that it's likely to happen..... let it! Accept it will 🍄, but also be preemptive in not focusing on it and use alternative data sources, generators or providers. Change the conditions that cause it but continue to monitor it as it happens. As Dan Caffee is fond of saying to me on a regular bases. "Dusty we need the system to be adaptive and provide mechanisms to learn from it's past." Well he is right! All we needed was data and the people that contribute to it's quality. Huge props 👏 to strong support from Kathy and Janelles audit teams. We were armed with a knowledge and a desire to learn from past mistakes. Armed with confidence that we could correct these problems we could focus on the data to prevent their impact going forward. When doing millions of LLM (Generative AI) calls regularly Josh and I have learned that hallucinations will happen. We also know exactly how we will handle and prevent it going forward! Failure is not doing anything and accepting defeat.
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Anthony Putignano
Ever wonder why some companies can ship software at lightning speeds while others seem stuck in the mud? 🚀⏳ . . . . . . . . . Daksh Gupta of Greptile collected real-world insights from engineers at many of the world's largest tech companies, unpacking the intricacies that either propel or impede the speed of shipping code. The big reveal? Older and larger companies tend to move more slowly because the costs of missteps become greater. In some cases, those costs are fairly non-negotiable (e.g. ensuring software for customers in production remains secure) while in other cases, the costs are more optional and cultural (e.g. wanting a lot of accurate estimates, forcing a manager's vs. maker's schedule, etc). #SoftwareEngineering #Tech #DevelopmentVelocity #Innovation #Productivity
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Hai Huang
This ICLR 2024 talk is a must-listen for those deploying LLMs for end users. Lilian Weng, head of OpenAI’s Safety Systems, gave an overview of what OpenAI is doing to ensure safety for millions of users. Safety is a system engineering effort and should be addressed at every layer of the system and at every phase of the model life cycle. The talk covers: 📌 Trade-offs between safety and usefulness and the importance of evaluating both simultaneously. 📌 How to measure various aspects of safety. 📌 Why refusals should be concise and what makes a good refusal in various scenarios. 📌 Hierarchical instruction tuning and how it helps address jail-breaking. 📌 Why taxonomy is difficult and how to make it work to detect unsafe content. This talk is part of a recent Latent Space Podcast covering ICLR 2024. The show is more than 4 hours long. Lilian Weng’s talk starts at 03:07:43. #artificialintelligence #machinelearning #deeplearning https://lnkd.in/eWmM2ZNT
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Rajeev S.
good findings on how to improve your handling of RAG applications when using long context LLMs https://lnkd.in/gSBjrtrs There are clear observations: Performance degrades at the number of needles (facts) increases from 1 to 10 Performance degrades as the context increases from 1000 to 120,000 tokens ... We can draw a few general insights, but further testing is needed: 1 No retrieval guarantees - Multiple facts are not guaranteed to be retrieved, especially as the number of needles and context size increases. 2 Different patterns of retrieval failure - GPT-4 fails to retrieve needles towards the start of documents as context length increases. 3 Prompting matters - Following insights mentioned here and here, specific prompt formulations may be needed to improve recall with certain LLMs. 4 Retrieval vs reasoning - Performance degrades when the LLM is asked to reason about the retrieved facts. #LLM #RAG #languagemodels #NLP
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Michael Gschwind
It's rare to get a shout-out in a CEO's earnings call -- I remember two times when @zuck mentioned our work on enabling GPU Inference and the impact it had on our business in general, and on Reels in particular (+30% viewing time in the first two weeks after release!). I think it's even more rare to get a shout-out in the earnings statement of the CEO of another company. But that just happened! With the ARM CEO’s earnings release statement here => https://lnkd.in/e8GyGygE with a shoutout to Llama and PyTorch/ExecuTorch. Even more amazing that PyTorch/ExecuTorch is the only framework called out! A big shoutout to the ExecuTorch team and the PyTorch team at large for delivering this amazing software stack that’s driving AI across the industry and is now making the dream of on-device AI a reality — it’s a true privilege and a great pleasure to work with them! I'm looking forward to the software optimizations being promised in the statement, in the hope that they will deliver additional quality and performance boosts in Executorch/PyTorch performance and running Llama3 on-device even better than it already does => https://lnkd.in/eA6AfCpu. A big thank you for the shout-out from ARM, and to the long running collaboration. I am looking forward to making on-device AI even better #together with our corporate partners and with the open source community, one optimization and one kernel at a time. If you haven't checked out ExecuTorch, this may be a good time to check out our GitHub repo => https://lnkd.in/g9uVqKgR, see how it works in this overview on PyTorch.Org => https://lnkd.in/e8CpE-eP and set up Executorch => https://lnkd.in/e68DzSrc Then, give it a spin and tell us how you are using ExecuTorch in the comments! And if yoou find ExecuTorch as exciting as we do, please join us and contribute your ideas and code to make high-quality on-device AI even better! #PyTorch #PT2 #ExecuTorch #LLMs #AcceleratedAI #MetaAI #Meta
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Nadeem Shabir
Very interesting article by Rohit Krishnan and featured on the Strange Loop that explores the inherent limitations of LLMs. LLMs are essentially complex algorithms trained on massive amounts of text data, allowing them to generate human-quality text, translate languages, and even write different kinds of creative content. However, Krishnan argues that despite their impressive capabilities, LLMs fall short in several key areas: - Logical Reasoning: LLMs excel at pattern recognition and statistical analysis within text, but they struggle with true logical reasoning. Imagine asking an LLM to prove a mathematical theorem or explain the cause-and-effect relationship between historical events. While it might generate text that sounds logical, it may not hold up under scrutiny. - - Example: You ask an LLM "If all men are mortal, and Socrates is a man, then is Socrates mortal?" It might respond with "Yes, of course," but it wouldn't necessarily understand the underlying logical structure of the syllogism. - Understanding Goals and Context: LLMs can process information and respond to prompts, but they often lack a deep understanding of the user's goals or the broader context of a conversation. Imagine having a complex discussion with an LLM about a philosophical concept. It might struggle to follow the nuances of the conversation or tailor its responses to your specific line of inquiry. - - Example: You ask an LLM "What is the meaning of life?" It might generate a series of interesting or poetic responses, but it wouldn't necessarily understand the philosophical weight of the question or your personal search for meaning. - Common Sense Reasoning: While LLMs can access and process vast amounts of information, they often lack the ability to apply common sense reasoning to new situations. Imagine asking an LLM to write a story about a robot chef. It might generate a detailed narrative, but it could struggle with basic concepts like the robot needing a power source or following safety protocols in the kitchen. - - Example: You ask an LLM to write a recipe for chocolate chip cookies. It might list out a series of ingredients and instructions, but it wouldn't necessarily understand the need to preheat the oven or the potential consequences of omitting baking powder. Krishnan emphasizes that LLMs are powerful tools with a wide range of applications. However, by understanding their limitations – particularly in areas like logical reasoning, goal comprehension, and common sense – we can leverage them more effectively and avoid setting unrealistic expectations which I think is very useful. https://lnkd.in/dQVVRm-s
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Jody Fleck
Who will get crushed by Claude LLM coming to iPhones? Will existing apps (and companies) get steamrolled by this? For those that don’t know Claude is a suite of 3 LLM models: Haiku, Sonnet and Opus. - Opus knocked ChatGPT off the leaderboard on capability when it was introduced. - It can hold a massive 160,000 words “enough for a user to paste in a weighty novel and ask follow-up questions” - Claude is multimodal so you will be able to take pictures or upload images from your iPhone and include that in your prompt Use cases * After a meeting, a business user could snap a photo of a whiteboard diagram and ask Claude to summarise the key points, making it easier to share and act upon important information. * Similarly, a consumer could take a picture of a plant they encounter on a hike and ask Claude to identify the species and provide more information about its characteristics and habitat. ** think the Picture This app ** What other uses do you see for this? https://lnkd.in/gdExfpx9
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Dan Selman
Over the past few weeks I’ve been researching, and building a framework that combines the power of Large Language Models for text parsing and transformation with the precision of structured data queries over Knowledge Graphs for explainable data retrieval. In this third article of the series I will show you how to combine structured and unstructured semantic queries, and use LLMs to orchestrate question answering over a knowledge graph. https://lnkd.in/e2xvGkWd #LLM #Chat #OpenAI #KnowledgeGraphs #Neo4J #Concerto
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Yixin (Bethany) Wang
Happy to be speaking at Arize:Observe 2024 about GenAI Evaluation with a few wonderful colleagues from Google Cloud. 📅 For GenAI apps, answering the question of "How to measure success?" is non-trivial yet key to iterative improvement. If you are a developer, come talk to me. We would love to brainstorm, learn your thoughts and unique approach towards GenAI model and application evaluation. 🧑💻 Arize AI #GenAIEvaluation
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Tanmay Dhote
How are the best Large Language Models ranked? Large Language Models (LLMs) have opened up incredible new possibilities, but ensuring they align with human preferences remains a challenge. That's where Chatbot Arena comes in. Managed by a team at UC Berkeley, this crowdsourced open platform evaluates LLMs based on human preferences. Chatbot Arena ranks different language models using ELO ratings, similar to the ranking system in chess. Users can visit the platform, enter a question, and receive responses from two anonymous models. By selecting the preferred response, ELO scores are calculated based on win rates, with higher scores indicating better performance. Currently, the proprietary models, like the GPT models from OpenAI, and the Claude series from Anthropic and Gemini from Google, dominate the top rankings. This highlights that open-source models have yet to catch up to their closed counterparts. Chatbot Arena has already become one of the most widely cited LLM leaderboards, referenced by top LLM developers and companies. Chatbot arena is dependent on community participation, so you can always contribute by casting your vote.
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Pravir Malik, Ph.D.
Nice to have 'Top New Release' status on Amazon for the latest book (my 25th) that explores an alternative paradigm for quantum computation. Illustrations (111) by Dr. Narendra Madhav Joshi. My take is that we owe it to ourselves at this early stage of the industry's lifecycle to explore alternative approaches. Here is a brief overview: "This book attempts to reveal something of the vastly different and fundamentally creative quality of computing that must accompany any computation involving the quantum-levels. To linearly project digital computing laurels manifest as increasing speeds and the ability to process vaster amounts of information, as the inevitable trajectory of quantum computing is perhaps, in the aphorism attributed to the Buddha, to look only at the finger and to miss the moon and the sky that it is pointing to. The cover figure highlights all that is being missed, summarized as an egg-like structure synonymous with the term ‘Hiranyagarbha’ in Sanskrit. This depicts the womb of creation abundant with many layers and patterns reflecting luminescence, due to the constant superposition and entanglement inherent to the quantum computation that derives from a light-centered interpretation of quantum dynamics." The book is available here: https://lnkd.in/gXu4H9uW #quantumcomputation #quantumtech #technologyleadership #cosmology #systemsthinking
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Anshul Bhide
A couple of recent college graduates from India against AI finetuning giants like Anyscale and Run:ai? I met Arko C at a NASSCOM event last October. He was building Xylem AI (YC S24) , a platform that allows you to train and deploy LLMs in production. So naturally most Indian VCs rejected him. Because how could a team of three recent college grads compete with the likes SV startups like Anyscale that have raised hundreds of millions of dollars, have teams of experienced AI researchers and a stash of H100 GPUs? Arko hustled and closed two Fortune 500 companies for paid contracts. I personally know unicorns that haven't managed to do this. He then got into YC in the last round in May. There's a lot to still be proved out, but Arko exemplifies how valuable grit is in building startups. I’m doing a webinar with him tomorrow on challenges of using LLMs in production. Link to register in the comments. Disclaimer: I'm a small investor in Xylem.
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Prateek Raj
My sense is that NVIDIA's dominance in AI compute is likely to be disrupted 1st in inference (if and when it happens). Mark Zuckerberg made a very interesting point in his conversation with Dwarkesh Patel recently. He said - Meta's ratio of inference to training compute is much higher than most other companies leveraging AI. This is because Meta serves extremely large user communities across Facebook, Instagram, WhatsApp, etc., resulting in huge volumes of inference requests. In contrast, companies focused mainly on research and development may have lower ratios of inference to training compute. The scale of Meta's user base means inference optimization is more important for them in order to efficiently serve real-time requests from billions of users. This was one factor in Meta's decision to develop custom silicon optimized for inference to reduce GPU consumption and free up more expensive GPU resources for training models. I suspect that all the large cos will reduce their dependencies on NVIDIA as they scale their AI inference compute. As of now, the four cos - Amazon Web Services (AWS), Meta, Google, and Microsoft - account for >1/3rd of NVIDIA’s data centre business, and all of them have their custom AI chip initiatives. I don't know what opportunities will be created for early-stage companies in this area, but I will be doing a deep dive here. Let me know who I should be speaking with for these initiatives.
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