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Best Large Language Models & Frameworks of 2023

AssemblyAI

However, among all the modern-day AI innovations, one breakthrough has the potential to make the most impact: large language models (LLMs). These feats of computational linguistics have redefined our understanding of machine-human interactions and paved the way for brand-new digital solutions and communications.

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Large Language Models – Technical Overview

Viso.ai

What are Large Language Models (LLMs)? In generative AI, human language is perceived as a difficult data type. If a computer program is trained on enough data such that it can analyze, understand, and generate responses in natural language and other forms of content, it is called a Large Language Model (LLM).

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Linguistics-aware In-context Learning with Data Augmentation (LaiDA): An AI Framework for Enhanced Metaphor Components Identification in NLP Tasks

Marktechpost

Given the intricate nature of metaphors and their reliance on context and background knowledge, MCI presents a unique challenge in computational linguistics. Neural network models based on word embeddings and sequence models have shown promise in enhancing metaphor recognition capabilities.

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SQuARE: Towards Multi-Domain and Few-Shot Collaborating Question Answering Agents

ODSC - Open Data Science

One of the benefits of multi-agent systems, and in particular MetaQA, is that we can reuse pretrained agents already available in online model hubs, such as SQuARE. Moreover, combining expert agents is an immensely easier task to learn by neural networks than end-to-end QA. This makes multi-agent systems very cheap to train.

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ChatGPT4 still leads ChatBot/LLM Leaderboard

Bugra Akyildiz

In here, the distinction is that base models want to complete documents(with a given context) where assistant models can be used/tricked into performing tasks with prompt engineering. Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean.

LLM 52
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68 Summaries of Machine Learning and NLP Research

Marek Rei

It is probably good to also to mention that I wrote all of these summaries myself and they are not generated by any language models. Are Emergent Abilities of Large Language Models a Mirage? Do Large Language Models Latently Perform Multi-Hop Reasoning? Here we go. NeurIPS 2023. ArXiv 2024.

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Testing the Robustness of LSTM-Based Sentiment Analysis Models

John Snow Labs

Additionally, PyTorch’s flexibility and efficiency will enable us to fine-tune the model’s parameters and optimize its performance, ensuring precise sentiment classification for diverse textual inputs. Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks. abs/2005.03993 Andrew L. Maas, Raymond E.