Researchers developed a simple yet effective solution for a puzzling problem that can worsen the performance of large language models such as ChatGPT.


Copyright: – “A New Way To Let AI Chatbots Converse All Day Without Crashing”


SwissCognitive_Logo_RGBWhen a human-AI conversation involves many rounds of continuous dialogue, the powerful large language machine-learning models that drive chatbots like ChatGPT sometimes start to collapse, causing the bots’ performance to rapidly deteriorate.

A team of researchers from MIT and elsewhere has pinpointed a surprising cause of this problem and developed a simple solution that enables a chatbot to maintain a nonstop conversation without crashing or slowing down.

Their method involves a tweak to the key-value cache (which is like a conversation memory) at the core of many large language models. In some methods, when this cache needs to hold more information than it has capacity for, the first pieces of data are bumped out. This can cause the model to fail.

By ensuring that these first few data points remain in memory, the researchers’ method allows a chatbot to keep chatting no matter how long the conversation goes.

The method, called StreamingLLM, enables a model to remain efficient even when a conversation stretches on for more than 4 million words. When compared to another method that avoids crashing by constantly recomputing part of the past conversations, StreamingLLM performed more than 22 times faster.

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This could allow a chatbot to conduct long conversations throughout the workday without needing to be continually rebooted, enabling efficient AI assistants for tasks like copywriting, editing, or generating code.

“Now, with this method, we can persistently deploy these large language models. By making a chatbot that we can always chat with, and that can always respond to us based on our recent conversations, we could use these chatbots in some new applications,” says Guangxuan Xiao, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on StreamingLLM.

Xiao’s co-authors include his advisor, Song Han, an associate professor in EECS, a member of the MIT-IBM Watson AI Lab, and a distinguished scientist of NVIDIA; as well as Yuandong Tian, a research scientist at Meta AI; Beidi Chen, an assistant professor at Carnegie Mellon University; and senior author Mike Lewis, a research scientist at Meta AI. The work will be presented at the International Conference on Learning Representations.

A puzzling phenomenon

Large language models encode data, like words in a user query, into representations called tokens. Many models employ what is known as an attention mechanism that uses these tokens to generate new text.[…]

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