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AutoGen: Powering Next Generation Large Language Model Applications

Unite.AI

Large Language Models (LLMs) are currently one of the most discussed topics in mainstream AI. These models are AI algorithms that utilize deep learning techniques and vast amounts of training data to understand, summarize, predict, and generate a wide range of content, including text, audio, images, videos, and more.

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Salesforce AI Research Introduces CodeTF: A One-Stop Transformer Library For Code Large Language Models (CodeLLM)

Marktechpost

Deep learning models have recently demonstrated promising results in more difficult code intelligence tasks, such as code generation, code completion, code summarization, and code retrieval. These models are particularly Transformer-based large language models (LLMs) pretrained on large-scale code data (“Code LLMs”).

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Microsoft Researchers Introduce PromptBench: A Pytorch-based Python Package for Evaluation of Large Language Models (LLMs)

Marktechpost

In the ever-evolving large language models (LLMs), a persistent challenge has been the need for more standardization, hindering effective model comparisons and impeding the need for reevaluation. The absence of a cohesive and comprehensive framework has left researchers navigating a disjointed evaluation terrain.

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This AI Research from UCLA Indicates Large Language Models (such as GPT-3) have Acquired an Emergent Ability to Find Zero-Shot Solutions to a Broad Range of Analogy Problems

Marktechpost

With the advancement in Deep Learning and Large Language Models (LLMs), LLMs are extensively tested and studied for analogical reasoning. Advanced language models possess the capacity for independent reason and abstract pattern recognition, serving as human intelligence’s foundational principle.

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Meet LLM Surgeon: A New Machine Learning Framework for Unstructured, Semi-Structured, and Structured Pruning of Large Language Models (LLMs)

Marktechpost

The recent advancements in Artificial Intelligence have enabled the development of Large Language Models (LLMs) with a significantly large number of parameters, with some of them reaching into billions (for example, LLaMA-2 that comes in sizes of 7B, 13B, and even 70B parameters).

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A New Research from Google DeepMind Challenges the Effectiveness of Unsupervised Machine Learning Methods in Knowledge Elicitation from Large Language Models

Marktechpost

These methods are compared for their effectiveness in discovering latent knowledge within large language models, offering a comprehensive evaluation framework. A random baseline, using a probe with randomly initialized parameters, acts as a floor method. If you like our work, you will love our newsletter.

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Meet Eureka: A Human-Level Reward Design Algorithm Powered by Large Language Model LLMs

Marktechpost

Large Language Models (LLMs) are great at high-level planning but need to help master low-level tasks like pen spinning. Also, don’t forget to join our 32k+ ML SubReddit , 40k+ Facebook Community, Discord Channel , and Email Newsletter , where we share the latest AI research news, cool AI projects, and more.