Benuraj Sharma’s Post

"Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet" Adly Templeton et al. A new technique to delve into the hidden mind of LLMs! 🧠 The interpretability team of Anthropic has made significant strides in AI safety by scaling sparse autoencoders to state-of-the-art transformers, including Claude 3 Sonnet, a medium-sized production model. This achievement highlights the ability to extract high-quality, interpretable features that respond to and cause abstract behaviors across various domains, with features encompassing concepts ranging from famous people and geographical locations to type signatures in code, demonstrating their multilingual and multimodal capabilities. The research, which utilized a technique called "dictionary learning", identified numerous features of particular interest due to their potential safety relevance. These include features related to security vulnerabilities, bias, deception, and dangerous content. While the presence of these features indicates a capacity for identifying harmful behaviors, the team emphasizes the preliminary nature of these findings and the need for further investigation to fully understand their implications and how they translate to real-world AI behavior. Key results of this study show that sparse autoencoders can produce interpretable features for large models and guide training through scaling laws. These features, being highly abstract and versatile, also reveal a systematic relationship between concept frequency and dictionary size. This research not only advances the interpretability of AI models but also provides a foundation for future work aimed at enhancing AI safety and understanding the potential risks associated with modern AI systems. #LLM #AiInterpretability #Autoencoders #AiResearch https://lnkd.in/g2_FcSNY

🔬 Exciting Breakthrough in AI Interpretability by Anthropic ! 🔬 Eight months ago, our team embarked on a challenging journey to scale sparse autoencoders for recovering monosemantic features from transformers. Today, we're thrilled to share a significant milestone: we've successfully extracted high-quality features from Claude 3 Sonnet, Anthropic's medium-sized production model! Our research has unveiled a fascinating diversity of highly abstract features that both respond to and influence behaviors. Some key findings include: - Features representing famous people, countries, cities, and type signatures in code. - Multilingual and multimodal features, bridging text and images across languages. - Abstract and concrete instantiations of ideas, such as code vulnerabilities and discussions on security. 📊 Key Results: 1. Interpretable Features: Sparse autoencoders are producing clear, interpretable features even for large models. 2. Scaling Laws: We've utilized scaling laws to guide our training, enhancing efficiency and effectiveness. 3. Abstract Generalization: The features generalize across multilingual, multimodal, concrete, and abstract references. 4. Systematic Relationships: A systematic relationship between concept frequency and dictionary size needed for feature resolution. 5. Influence on Behavior: These features can be used to steer large models, influencing behavior significantly. Importantly, the findings also touch on crucial safety concerns, revealing features related to deception, sycophancy, bias, and dangerous content. This breakthrough marks a significant step forward in AI safety and interpretability, paving the way for safer, more reliable AI systems. Kudos to the Anthropic interpretability team for this remarkable achievement! 🎉 Link to Paper: https://lnkd.in/ehH-eCdr #AISafety #MachineLearning #AIResearch #SparseAutoencoders #Transformers #Anthropic #Claude3Sonnet #TechInnovation #ArtificialIntelligence #AIInterpretability

Exciting news in the world of AI, with huge implications regarding explainability and transparency. Anthropic interpretability team is working to better understand how AI works. This is a potential game changer and a big step towards secure & responsible AI.

ideas for model risk management for LLMs!! follow ADAO 😀

Responsible and accountable AI models are essential as generative AI continues to revolutionize industries. The rise of this technology, however, presents challenges in understanding how these models make decisions. Mechanistic interpretability addresses this by going beyond traditional methods that track statistical relationships. A recent research work by a team from #Anthropic has involved visualizing which neurons activate in response to specific prompts, advancing monosemanticity—ensuring each neuron’s activation correlates to a single, clear function. This step is crucial for fostering more transparent and safer AI systems in various applications. This is a great progress toward interpreting the thinking mechanisms of large language models (LLMs). #GenerativeAI #AI #LLMs #GenerativeAI #Grainger #MachineLearning #DeepLearning #ExplainableAI #DataScience

Validating LLMs?! How about this: modifying certain features to change the behaviour of an LLM. Make it friendlier, more factual, change its context... how can we be in control? Here are two thoughts! - modify certain features/embeddings to determine if their values are appropriate? - define tests that the AI should pass, in order to 'pass' certain validation test. Here is the post qhere these two tiny ideas are based: https://lnkd.in/eaYkgSNM Follow ADAO for more content/info! #validation #ai #modelriskmanagement #llm

🚨🚨🚨🚨🚨 REALLY COOL INSIGHT INTO AI!!! Peeking Inside the Black Box: New Advances in Understanding How AIs Think The article from Anthropic discusses their research on deciphering the inner workings of their conversational AI system, Claude. Using a technique called sparse dictionary learning, they were able to identify millions of semantic building blocks or "features" that Claude uses for reasoning and generating text. For example, they found distinct features for concepts like the Golden Gate Bridge, computer code, famous people, and geography. By analyzing and intervening on these features, they gained insights into how Claude represents knowledge and makes inferences. The discovery of abstract, interpretable features sheds light on therepresentations and computations happening inside Claude's neural network "black box." The article continues to build on the approach by scaling it up dramatically to extract features from Anthropic's latest model, Claude 3 Sonnet. By training larger sparse auto encoders with more compute power, they were able to find even more sophisticated features corresponding to complex, multilingual, and multimodal concepts. The researchers analyze these features in depth to understand what they represent, how they generalize, and how they enable model capabilities. Intriguingly, they also find features that appear relevant to AI safety, like detecting deception orsecurity flaws in code. While preliminary, this demonstrates how interpretability could eventually help ensure AI systems behave safely and reliably. This represents exciting progress in elucidating the mechanisms by which large language models operate. Methodically decoding these models promises to enhance our ability to build more robust, trustworthy, and beneficial AI. #AI #artificalintelligence #SRED #RD #innovation #funding #fundingexpert #grants #JonIrwin #futuretech https://lnkd.in/gRVVCppY

Wild. Recent research demonstrating that sparse autoencoders might be remarkably helpful in removing the 'black box' reputation of LLMs by fostering increased interpretability. https://lnkd.in/dEcmE3kj #ai #llm #interpretability #responsibleai #aiethics #aiact

Reflections on Sparse Representations in Language Models Reading about the use of sparse autoencoders in language models revealed an intriguing balance between interpretability and efficiency. Sparse representations enhance feature disentanglement, aiding in understanding model behaviors, especially in AI safety and bias detection. However, for practical applications where interpretability is secondary, sparse representations might introduce computational redundancy and inefficiency. Thus, while sparse autoencoders offer valuable insights for research and safety, more compact representations could be preferable for deployment. Balancing these aspects is crucial, potentially through adaptive approaches that optimize for both interpretability during research and efficiency in real-world applications. https://lnkd.in/egPP7dEg

I've learned a fantastic paper about LLMs and how to research a group of neurons inside LLM responsible for various objects and how to tune it and put validation / modification to improve model #llm #ai #deeplearning https://lnkd.in/g6m4UZKG