MIT-IBM Watson AI Lab

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…

Large language AI models are notorious for crashing or slowing down during lengthy human-AI dialogues, posing a major barrier to the effective use of chatbots in many applications. Now, a team of researchers from MIT and other institutions propose a novel solution - by modifying the key-value cache, or the 'conversation memory', they improved the…

MIT researchers have discovered a fault in the design of language machine-learning models that can cause AI chatbots' performance to drastically deteriorate during lengthy conversations. Essentially, when data stored in a chatbot's "memory" (known as the key-value cache) exceeds its capacity, the earliest data is removed, sometimes causing the chatbot to malfunction or slow down.…

Researchers from MIT and other institutions have developed a method that enables a chatbot to carry on unbroken conversation without crashing or losing performance. This method, named StreamingLLM, involves a tweak to the key-value cache, a form of "conversation memory", that helps AI operate. The team found when the cache became too full, the first…

Researchers from MIT and other institutions have discovered the key to why AI chatbot conversations can break down and developed a solution that enables continuous dialogue. The issue lies in the chatbot's key-value cache (akin to a conversational memory). In some models, earlier data points are discarded when the cache reaches its limit, causing the…

A team of researchers from MIT and other institutions have found a way to prevent chatbots driven by large language machine-learning models from collapsing during lengthy conversations. The failure typically occurs when the key-value cache, or "conversation memory", in some methods cannot contain more information than its capacity, resulting in the first data points being…

Research from MIT and other institutions has developed a method, called StreamingLLM, that enables AI chatbots to maintain continuous dialogue without crashing or slowing down. The technique tweaks the key-value cache or conversation memory at the core of large language models. Failure often occurs when this cache needs to store more information than it can…