AI hallucinations refer to instances where AI systems, particularly language models, generate outputs that are inconsistent, nonsensical, or even entirely fabricated. This issue is especially prevalent in AI systems that rely on external data sources, such as Retrieval-Augmented Generation (RAG)...
While large language models excel in mimicking human-like content generation, they also pose risks of producing confusing or erroneous responses, often stemming from poor data quality. Poor data quality is the primary hurdle for companies embarking on generative AI projects, according to...
Augmented Shelf | Issue 2 | March 19, 2024 Welcome to Augmented Shelf, a wrap-up of the week’s AI news, trends and research that are forging the future of work. Evil Geniuses Vs. ChatDev To evaluate the vulnerability of LLM-based agents, researchers at Tsinghua University in Beijing, China, have...
Large Language Models (LLMs) rely on extensive memory to store and manipulate vast datasets, a key factor that allows them to learn from past inputs and improve their linguistic abilities over time. But what if, alongside remembering, LLMs could also benefit from adaptive forgetting? The notion of...
Whether it’s through text-based chatbots on a website or voice-activated assistants in our homes and smartphones, conversational AI is becoming an integral part of our daily lives. By simulating human-like conversations, these advanced AI systems are breaking down the barriers between...