Large Language Model

An in-depth study by Innodata evaluated the performance of various large language models (LLMs) including Llama2, Mistral, Gemma, and GPT. The study assessed the models based on factuality, toxicity, bias, and propensity for hallucinations and used fourteen unique datasets designed to evaluate each model's safety. One of the main criteria was factuality, the ability of the…

Recent research by scientists at Ohio State University and Carnegie Mellon University has analyzed the limitations of large language models (LLMs), such as GPT-4, and their limitations in implicit reasoning. This refers to their ability to make accurate comparisons of internalized facts and properties, even when aware of the entities in question. The study focused…

The study of multilingual natural language processing (NLP) is rapidly progressing, seeking to create language models capable of interpreting and generating text in various languages. The central goal of this research is to improve global communication and access to information, making artificial intelligence technologies accessible across diverse linguistic backgrounds. However, creating such models brings significant challenges,…

Natural language processing (NLP) is a field in computer science that seeks to enable computers to interpret and generate human language. This has various applications such as machine translation and sentiment analysis. However, there are limitations and inefficiencies with conventional tokenizers employed in large language models (LLMs). These tokenizers break down text into subwords, demanding…

In the field of machine learning and artificial language modeling, Large Language Models (LLMs) are often used to analyze or interpret large chunks of data. Such models have the capability to support very long context windows; however, this approach is not without its challenges. Standard attention mechanisms, used to allocate computational resources, often suffer from…

Artificial Intelligence (AI) has revolutionized numerous industries, from customer service to content generation, by deploying large language models (LLMs) that can supply accurate and useful replies to human prompts. However, these models tend to favor longer responses, exhibiting an inherent length bias that complicates model evaluation. To balance response length with quality, researchers have developed Length-Instruction…