The Technology Innovation Institute (TII) in Abu Dhabi has launched "Falcon," a ground-breaking collection of language models. They're available under the Apache 2.0 license, with Falcon-40B being the first "fully open" model that's equivalent in capabilities to numerous proprietary alternatives. This innovation marks a significant step forward in the field, presenting a wealth of opportunities…
The increasing demand for financial data analysis and management has propelled the expansion of question-answering (QA) systems powered by artificial intelligence (AI). These systems improve customer service, aid in risk management, and provide personalized stock recommendations, thus requiring a comprehensive understanding of financial data. This data's complexity, domain-specific terminology, market instability, and decision-making processes make…
The rapid growth of digital text in different languages and scripts presents significant challenges for natural language processing (NLP), particularly with transliterated data where performance often degrades. Current methods, such as pre-trained models like XLM-R and Glot500, are capable of handling text in original scripts but struggle with transliterated versions. This not only impacts their…
Advances in artificial intelligence (AI) technology have led to the development of a pioneering methodology, known as retrieval-augmented generation (RAG), which fuses the capabilities of retrieval-based technology with generative modeling. This process allows computers to create relevant, high-quality responses by leveraging large datasets, thereby improving the performance of virtual assistants, chatbots, and search systems.
One of…
Researchers from Columbia University and Databricks Mosaic AI have conducted a comparative study of full finetuning and Low-Rank Adaptation (LoRA), a parameter-efficient finetuning method, in large language models (LLMs). The efficient finetuning of LLMs, which can contain billions of parameters, is an ongoing challenge due to the substantial GPU memory required. This makes the process…
Recent research suggests that incorporating demonstrating examples, or in-context learning (ICL), significantly enhances large language models' (LLM's) and large multimodal models' (LMM's) performance. Studies have shown improvements in LLM performance with increased in-context examples, particularly in out-of-domain tasks. These findings are driven by newer models such as GPT-4o and Gemini 1.5 Pro, which include longer…
The world of artificial intelligence (AI) and machine learning continues to evolve at a rapid pace, with OpenAI leading the charge. Their latest development is the introduction of GPT-4o, an optimized version of the widely used GPT-4, part of the Generative Pre-trained Transformer model series renowned for its natural language processing capabilities.
GPT-4 boasts enhanced contextual…
The world of Artificial Intelligence (AI) has taken another step forward with the introduction of the recent Yi-1.5-34B model by 01.AI. This model is considered a significant upgrade over prior versions, providing a bridge between the capabilities of the Llama 3 8B and the 70B models.
The distinguishing features of the Yi-1.5-34B include improvements in multimodal…
Large language models (LLMs) have been successful in areas like natural language tasks and following instructions, yet they have limitations when dealing with non-textual data such as images and audio. But presently, an approach integrating textual LLMs with speech encoders in one training setup could revolutionize this. One option is multimodal audio-language models, proving advantageous…
Recent multimodal foundation models are often limited in their ability to fuse various modalities, as they typically utilize distinct encoders or decoders for each modality. This structure limits their capability to effectively integrate varied content types and create multimodal documents with interwoven sequences of images and text.
Meta researchers, in response to this limitation, have…
