The development and progress in the field of artificial intelligence (AI) are unending, with the recent emergence of the AI model, "gpt2-chatbot", generating significant interest within AI circles on Twitter. This model, known as a large language model (LLM), has incited considerable exploration and curiosity amongst AI developers and enthusiasts, who are constantly searching to…
French researchers have developed the first publicly available benchmark tool, 'DrBenchmark', to evaluate and standardize evaluation protocols for pre-trained masked language models (PLMs) in French, particularly in the biomedical field. Existing models lacked standardized protocols and comprehensive datasets, leading to inconsistent results and stalling progress in natural language processing (NLP) research.
The advent and advancement…
In the field of computational linguistics, large amounts of text data present a considerable challenge for language models, especially when specific details within large datasets need to be identified. Several models, like LLaMA, Yi, QWen, and Mistral, use advanced attention mechanisms to deal with long-context information. Techniques such as continuous pretraining and sparse upcycling help…
Emerging research from the New York University's Center for Data Science asserts that language models based on transformers play a key role in driving AI forward. Traditionally, these models have been used to interpret and generate human-like sequences of tokens, a fundamental mechanism used in their operational framework. Given their wide range of applications, from…
Machine learning models, as they become more complex, often begin to resemble "black boxes" where the decision-making process is unclear. This lack of transparency can hinder understanding and trust in decision-making, particularly in critical fields such as healthcare and finance. Traditional methods for making these models more transparent have often suffered from inconsistencies. One such…
Artificial intelligence face challenges in ensuring efficient processing of information by language models. A frequent issue is the slow response time of these models when generating text or answering questions, particularly inconvenient for real-time applications such as chatbots or voice assistants. Existing solutions to increase speed and incorporate optimization techniques are currently lacking in universal…
A recent Gartner poll highlighted that while 55% of organizations experiment with generative AI, only 10% have implemented it in production. The main barrier in transitioning to production is the erroneous outputs or 'hallucinations' produced by large language models (LLMs). These inaccuracies can create significant issues, particularly in applications that need accurate results, such as…