The BRAG series is a set of high-performance Retrieval Augmented Generation (RAG) models developed by Maximalists AI Researcher. They are a small language model designed to be a low-cost alternative for AI-driven language processing, proving effective in artificial intelligence due to their affordability and cost-effectiveness. They were created to meet the need for more powerful…
Improving Text Embeddings in Compact Language Models: A Comparative Refinement Method using MiniCPM.
Researchers from Tsinghua University have developed an approach to improve the performance of smaller language models such as MiniCPM, Phi-2, and Gemma by enhancing their text embeddings. By applying contrastive fine-tuning using the NLI dataset, the researchers significantly improved the text embedding quality across various benchmarks. In particular, MiniCPM showed a significant 56.33% performance improvement,…
Artificial intelligence (AI) applications are becoming increasingly complicated, involving multiple interactive tasks and components that must be coordinated for effective and efficient performance. Traditional methods of managing this complex orchestration, such as Directed Acyclic Graphs (DAGs) and query pipelines, often fall short in dynamic and iterative processes.
To overcome these limitations, LlamaIndex has introduced…
Advancements in Large Language Models (LLMs) have notably benefitted the development of artificial intelligence, particularly in creating agent-based systems. These systems are designed to interact with various environments and carry out actions to meet specific goals. One of the significant challenges includes the creation of elaborate planning environments and tasks, most of which currently rely…
Large Language Models (LLMs) have significantly contributed to the enhancement of conversational systems today, generating increasingly natural and high-quality responses. But with their matured growth have come certain challenges, particularly the need for up-to-date knowledge, a proclivity for generating non-factual orhallucinated content, and restricted domain adaptability. These limitations have motivated researchers to integrate LLMs with…
Large Language Models (LLMs) are pivotal for advancing machines' interactions with human language, performing tasks such as translation, summarization, and question-answering. However, evaluating their performance can be daunting due to the need for substantial computational resources.
A major issue encountered while evaluating LLMs is the significant cost of using large benchmark datasets. Conventional benchmarks like HELM…
Israeli tech startup aiOla has launched Whisper-Medusa, a significant development in speech recognition tech relying on artificial intelligence (AI). Whisper-Medusa expands on the Whisper model developed by international AI research lab OpenAI and delivers a 50% boost to processing speed, pushing the boundaries of automatic speech recognition (ASR). Whisper-Medusa differs from the original Whisper in…
Speech recognition technology, a rapidly evolving area of machine learning, allows computers to understand and transcribe human languages. This technology is pivotal for services including virtual assistants, automated transcription, and language translation tools. Despite recent advancements, developing universal speech recognition systems that cater to all languages, particularly those that are less common and understudied, remains…
Large Language Model (LLM) agents are seeing a vast number of applications across various sectors including customer service, coding, and robotics. However, as their usage expands, the need for their adaptability to align with diverse consumer specifications has risen. The main challenge is to develop LLM agents that can successfully adopt specific personalities, enabling them…