This paper addresses the challenge of effectively evaluating language models (LMs). Evaluation is crucial for assessing model capabilities, tracking scientific progress, and informing model selection. Traditional benchmarks often fail to highlight novel performance trends and are sometimes too easy for advanced models, providing little room for growth. The research identifies three key desiderata that existing…
Bioptimus, a French startup known for its innovative contributions to the medical field, has unveiled its latest groundbreaking project: H-optimus-0. This development marks a significant milestone in artificial intelligence (AI) for pathology. Launched less than five months after the company’s inception, H-optimus-0 stands as the world’s largest open-source AI foundation model specifically designed for pathology.…
In the realm of Large language models (LLMs), there has been a significant transformation in text generation, prompting researchers to explore their potential in audio synthesis. The challenge lies in adapting these models for text-to-speech (TTS) tasks while maintaining high-quality output. Current methodologies, such as neural codec language models like VALL-E, face several limitations. These…
In a notable tribute to Cleopatra, Mistral AI has announced the release of Codestral Mamba 7B, a cutting-edge language model (LLM) specialized in code generation. Based on the Mamba2 architecture, this new model marks a significant milestone in AI and coding technology. Released under the Apache 2.0 license, Codestral Mamba 7B is available for free…
Large vision-language models (LVLMs) are very good at tasks that require visual understanding and language processing. However, they are always ready to provide answers, which makes them passive answer providers. LVLMs often give detailed and confident responses, even when the question is not clear or impossible to answer. For example, LLaVA, one of the best…
Researchers are struggling with the challenge of causal discovery in heterogeneous time-series data, where a single causal model cannot capture diverse causal mechanisms. Traditional methods for causal discovery from time-series data, based on structural causal models, conditional independence tests, and Granger causality, typically assume a uniform causal structure across the entire dataset. However, real-world scenarios…
Generating comprehensive and detailed outlines for long-form articles, such as those on Wikipedia, poses a significant challenge. Traditional approaches often do not capture the full depth of a topic, leading to articles that are either too shallow or poorly organized. The core problem lies in the ability of systems to ask the right questions and…
Telecommunications involves the transmission of information over distances to communicate. It encompasses various technologies like radio, television, satellite, and the internet, enabling voice, data, and video transmission. This field is crucial for modern communication, supporting global connectivity and data exchange. Innovations in this field continuously improve communication systems’ speed, reliability, and efficiency, which are foundational…