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ETH Zurich Researchers Introduced EventChat: A CRS Using ChatGPT as Its Core Language Model Enhancing Small and Medium Enterprises with Advanced Conversational Recommender Systems Aswin Ak Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Conversational Recommender Systems (CRS) are revolutionizing how users make decisions by offering personalized suggestions through interactive dialogue interfaces. Unlike traditional systems that present predetermined options, CRS allows users to dynamically input and refine their preferences, significantly reducing information overload. By incorporating feedback loops and… Read More »ETH Zurich Researchers Introduced EventChat: A CRS Using ChatGPT as Its Core Language Model Enhancing Small and Medium Enterprises with Advanced Conversational Recommender Systems Aswin Ak Artificial Intelligence Category – MarkTechPost

RoboMorph: Evolving Robot Design with Large Language Models and Evolutionary Machine Learning Algorithms for Enhanced Efficiency and Performance Asif Razzaq Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” The field of robotics is seeing transformative changes with the integration of generative methods like large language models (LLMs). These advancements enable the developing of sophisticated systems that autonomously navigate and adapt to various environments. The application of LLMs in robot design and control… Read More »RoboMorph: Evolving Robot Design with Large Language Models and Evolutionary Machine Learning Algorithms for Enhanced Efficiency and Performance Asif Razzaq Artificial Intelligence Category – MarkTechPost

Whispering Experts: Toxicity Mitigation in Pre-trained Language Models by Dampening Expert Neurons Apple Machine Learning Research

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​An important issue with Large Language Models (LLMs) is their undesired ability to generate toxic language. In this work, we show that the neurons responsible for toxicity can be determined by their power to discriminate toxic sentences, and that toxic language can be mitigated by… Read More »Whispering Experts: Toxicity Mitigation in Pre-trained Language Models by Dampening Expert Neurons Apple Machine Learning Research

Contrasting Multiple Representations with the Multi-Marginal Matching Gap Apple Machine Learning Research

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​Learning meaningful representations of complex objects that can be seen through multiple (k≥3kgeq 3k≥3) views or modalities is a core task in machine learning. Existing methods use losses originally intended for paired views, and extend them to kkk views, either by instantiating 12k(k−1)tfrac12k(k-1)21​k(k−1) loss-pairs, or… Read More »Contrasting Multiple Representations with the Multi-Marginal Matching Gap Apple Machine Learning Research

Revealing the Utilized Rank of Subspaces of Learning in Neural Networks Apple Machine Learning Research

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​In this work, we study how well the learned weights of a neural network utilize the space available to them. This notion is related to capacity, but additionally incorporates the interaction of the network architecture with the dataset. Most learned weights appear to be full… Read More »Revealing the Utilized Rank of Subspaces of Learning in Neural Networks Apple Machine Learning Research

On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions Apple Machine Learning Research

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​We investigate the out-of-domain generalization of random feature (RF) models and Transformers. We first prove that in the ‘generalization on the unseen (GOTU)’ setting, where training data is fully seen in some part of the domain but testing is made on another part, and for… Read More »On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions Apple Machine Learning Research

Samsung Researchers Introduce LoRA-Guard: A Parameter-Efficient Guardrail Adaptation Method that Relies on Knowledge Sharing between LLMs and Guardrail Models Mohammad Asjad Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Large Language Models (LLMs) have demonstrated remarkable proficiency in language generation tasks. However, their training process, which involves unsupervised learning from extensive datasets followed by supervised fine-tuning, presents significant challenges. The primary concern stems from the nature of pre-training datasets, such as Common Crawl,… Read More »Samsung Researchers Introduce LoRA-Guard: A Parameter-Efficient Guardrail Adaptation Method that Relies on Knowledge Sharing between LLMs and Guardrail Models Mohammad Asjad Artificial Intelligence Category – MarkTechPost

Branch-and-Merge Method: Enhancing Language Adaptation in AI Models by Mitigating Catastrophic Forgetting and Ensuring Retention of Base Language Capabilities while Learning New Languages Nikhil Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Language model adaptation is a crucial area in artificial intelligence, focusing on enhancing large pre-trained language models to work effectively across various languages. This research is vital for enabling these models to understand and generate text in multiple languages, which is essential for global… Read More »Branch-and-Merge Method: Enhancing Language Adaptation in AI Models by Mitigating Catastrophic Forgetting and Ensuring Retention of Base Language Capabilities while Learning New Languages Nikhil Artificial Intelligence Category – MarkTechPost