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Strong and weak alignment of large language models with human values

Strong and weak alignment of large language models with human values

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Why Keeping Humans in the Loop Is Critical for Trustworthy AI

Why Keeping Humans in the Loop Is Critical for Trustworthy AI

(solarseven/Shutterstock) As the global generative AI rollout unfolds, companies are grappling with a host of ethical and governance concerns: Should my employees fear for their jobs? How do I ensure the AI models are adequately and transparently trained? What do I do about hallucinations and toxicity? While it’s not a silver bullet, keeping humans in the AI loop is a good way to address a decent cross-section of AI worries. It’s remarkable how much progress has been made in generative AI since OpenAI shocked the world with the launch of ChatGPT just a year-and-a-half ago. While other AI trends have…
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MDS-GNN: A Mutual Dual-Stream Graph Neural Network on Graphs with Incomplete Features and Structure

MDS-GNN: A Mutual Dual-Stream Graph Neural Network on Graphs with Incomplete Features and Structure

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Clustering-friendly Representation Learning for Enhancing Salient Features

Clustering-friendly Representation Learning for Enhancing Salient Features

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Batching BPE Tokenization Merges

Batching BPE Tokenization Merges

arXiv:2408.04653v1 Announce Type: new Abstract: The Byte Pair Encoding algorithm can be safely batched to merge hundreds of pairs of tokens at a time when building up a tokenizer's vocabulary. This technique combined with reducing the memory footprint of text used in vocabulary training make it feasible to train a high quality tokenizer on a basic laptop. This paper presents BatchBPE, an open-source pure Python implementation of these concepts, with the goal of making experimenting with new tokenization strategies more accessible especially in compute- and memory-constrained contexts. BatchBPE's usefulness and malleability are demonstrated through the training of several token vocabularies…
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Long Context RAG Performance of LLMs

Long Context RAG Performance of LLMs

Retrieval Augmented Generation (RAG) is  the most widely adopted generative AI use case among our customers. RAG enhances the accuracy of LLMs by retrieving information from external sources such as unstructured documents or structured data.  With the availability of LLMs with longer context lengths like Anthropic Claude (200k context length), GPT-4-turbo (128k context length) and Google Gemini 1.5 pro (2 million context length), LLM app developers are able to feed more documents into their RAG applications. Taking longer context lengths to the extreme, there is even a debate about whether long context language models will eventually subsume RAG workflows. Why…
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Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

arXiv:2408.04883v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation requires models to effectively integrate visual representations with open-vocabulary semantic labels. While Contrastive Language-Image Pre-training (CLIP) models shine in recognizing visual concepts from text, they often struggle with segment coherence due to their limited localization ability. In contrast, Vision Foundation Models (VFMs) excel at acquiring spatially consistent local visual representations, yet they fall short in semantic understanding. This paper introduces ProxyCLIP, an innovative framework designed to harmonize the strengths of both CLIP and VFMs, facilitating enhanced open-vocabulary semantic segmentation. ProxyCLIP leverages the spatial feature correspondence from VFMs as a form of proxy…
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Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference

Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Google Cloud Research Shows Strong ROI for Early Adopters

Google Cloud Research Shows Strong ROI for Early Adopters

(TierneyMJ/Shutterstock) Google Cloud shared new global research on how select global enterprises are achieving breakout success with GenAI. The ROI of Gen AI global survey commissioned by Google emphasizes that GenAI is more than just a new technology; it is a ‘key driver of business transformation’. The report is based on a survey conducted by Google Cloud and the National Research Group. The survey, which includes 2,500 business leaders from global enterprises with revenues exceeding $10 million, focuses on GenAI’s ability to impact two categories: direct financial impact and the broader business advantages gained from deploying GenAI in production.  According…
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