In a recent study by Innodata, various large language models (LLMs) such as Llama2, Mistral, Gemma, and GPT were benchmarked for their performance in factuality, toxicity, bias, and propensity for hallucinations. The research introduced fourteen novel datasets designed to evaluate the safety of these models, focusing on their ability to produce factual, unbiased, and appropriate…
Large language models (LLMs) have demonstrated remarkable performance across various tasks, with reasoning capabilities being a crucial aspect of their development. However, the key elements driving these improvements remain unclear. Currently, the primary approaches to enhance reasoning involve increasing model size and expanding context length through techniques like chain of thought, retrieval augmented generation, and…
VLMs like LLaVA-Med have advanced significantly, offering multi-modal capabilities for biomedical image and data analysis, which could aid radiologists. However, these models face challenges, such as hallucinations and imprecision in responses, leading to potential misdiagnoses. With radiology departments experiencing increased workloads and radiologists facing burnout, the need for tools to mitigate these issues is pressing.…
Formal verification is crucial in software engineering to ensure program correctness through mathematical proof. One widely used technique for this purpose is bounded model checking (BMC), which involves verifying the correctness of a program within specified bounds. Python, a programming language favored for its simplicity and extensive libraries, particularly in fields like web development, image…
Multilingual natural language processing (NLP) is a rapidly advancing field that aims to develop language models capable of understanding & generating text in multiple languages. These models facilitate effective communication and information access across diverse linguistic backgrounds. This field’s importance lies in its potential to bridge the gap between different language speakers, making technological advancements…
The ability to automate and assist in coding has the potential to transform software development, making it faster and more efficient. However, ensuring these models produce helpful and secure code is the challenge. The intricate balance between functionality and safety is critical, especially when the generated code could be exploited maliciously. In practical applications, LLMs…
Generative Flow Networks (GFlowNets) address the complex challenge of sampling from unnormalized probability distributions in machine learning. By learning a policy on a constructed graph, GFlowNets facilitates efficient sampling through a series of steps, approximating the target probability distribution. This innovative approach sets GFlowNets apart from traditional methods by providing a robust framework for handling…