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AI Learns from AI: The Emergence of Social Learning Among Large Language Models

Unite.AI

Since OpenAI unveiled ChatGPT in late 2022, the role of foundational large language models (LLMs) has become increasingly prominent in artificial intelligence (AI), particularly in natural language processing (NLP). This suggests a future where AI can adapt to new challenges more autonomously.

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Large Language Model (LLM) Training Data Is Running Out. How Close Are We To The Limit?

Marktechpost

In the quickly developing fields of Artificial Intelligence and Data Science, the volume and accessibility of training data are critical factors in determining the capabilities and potential of Large Language Models (LLMs). The post Large Language Model (LLM) Training Data Is Running Out.

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TIGER-Lab Introduces MMLU-Pro Dataset for Comprehensive Benchmarking of Large Language Models’ Capabilities and Performance

Marktechpost

The evaluation of artificial intelligence models, particularly large language models (LLMs), is a rapidly evolving research field. Researchers are focused on developing more rigorous benchmarks to assess the capabilities of these models across a wide range of complex tasks.

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Decoding AI Cognition: Unveiling the Color Perception of Large Language Models through Cognitive Psychology Methods

Marktechpost

A groundbreaking study unveils an approach to peering into the minds of Large Language Models (LLMs), particularly focusing on GPT-4’s understanding of color. The challenge of interpreting AI models lies in their complexity and the opaque nature of their internal workings. Check out the Paper.

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Researchers from Meta AI Introduce a New AI Model to Critique Large Language Model Generations

Marktechpost

The ability of large language models (LLMs) to generate coherent, contextually relevant, and semantically meaningful text has become increasingly complex. Thus, techniques that continually assess and improve generations would be helpful toward more trustworthy language models.

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DeepMind and UCL’s Comprehensive Analysis of Latent Multi-Hop Reasoning in Large Language Models

Marktechpost

In an intriguing exploration spearheaded by researchers at Google DeepMind and University College London, the capabilities of Large Language Models (LLMs) to engage in latent multi-hop reasoning have been put under the microscope. Don’t Forget to join our Telegram Channel You may also like our FREE AI Courses….

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Meet WavJourney: An AI Framework For Compositional Audio Creation With Large Language Models

Marktechpost

As a powerful intermediary, natural language holds promise in enhancing comprehension and communication across diverse sensory domains. Large Language Models (LLMs) have exhibited impressive capabilities as agents, collaborating with various AI models to tackle multi-modal challenges.