Human-computer interaction

Researchers from MIT and other locations have developed a solution to an issue with chatbot performance deterioration following continuous dialogue with a human - a problem attributed to the memory degradation in large language machine-learning models. Their solution, termed StreamingLLM, works by retaining key data points in the memory cache, enabling a chatbot to continue…

A team of researchers, including those from the Massachusetts Institute of Technology (MIT), have created a system called StreamingLLM that allows chatbots to maintain ongoing dialogues without suffering from performance issues. The method involves a reconfiguration of the model's key-value cache—a form of memory storage—that commonly leads to models failing when the cache is overloaded…

Researchers from MIT and other institutions have proposed a solution to the challenge of AI systems losing the context of conversation in extended dialogues. Large language models such as ChatGPT, which enable the functioning of chatbots, often struggle to retain information from long conversations, resulting in rapid performance deterioration. The team has developed a method…

A group of researchers from MIT and other institutions have pinpointed a key issue that causes performance degradation in AI chatbots during long conversations and have developed a simple solution to rectify it. Large language machine-learning models such as the ChatGPT use key-value cache to store data. However, when the cache needs to hold more…

Researchers from MIT and other institutions have devised an innovative solution to prevent chatbots from crashing during prolonged dialogues. The method, known as StreamingLLM, makes a simple adjustment to the key-value cache, essentially the 'conversation memory,' of well-developed machine-learning models. By ensuring the first few data points don't get bumped out, the chatbot can maintain…

A team of researchers from MIT and other institutions has developed a method to stop the performance deterioration in AI language models involved in continuous dialogue, like the AI chatbot, ChatGPT. Named StreamingLLM, the solution revolves around a modification in the machine’s key-value cache, acting as a conversation memory. Conventionally, when the cache overflows, the…

Researchers from MIT have devised a method called StreamingLLM which enables chatbots to maintain long, uninterrupted dialogues without crashing or performance dips. It involves a modification to the key-value cache at the core of many large language models which serves as a conversation memory, ensuring the initial data points remain present. The method facilitates a…

Researchers from MIT and other institutions have developed a method to prevent chatbot performance from deteriorating during prolonged human-AI interactions. The method, called StreamingLLM, is based on a slight modification to the crucial key-value cache (KV Cache) that is central to large language models employed by many AI-driven platforms. The KV Cache, similar to a…

Researchers from the Massachusetts Institute of Technology (MIT) and partner organizations have developed a solution to address a key issue limiting the effectiveness of AI chatbots. Large language machine-learning models, such as ChatGPT, often crash or slow down during extended rounds of dialogue with humans. The study identified the cause of this problem as the…

Researchers from MIT and other institutions have developed a solution to maintain continuous human-AI interactions without the chatbot crashing or slowing down. The solution, known as StreamingLLM, involves tweaking the key-value cache (like a conversation memory) that forms the heart of many large language models. Under the conventional setup, the cache, when filled beyond its…