Categories: AI/ML News

Why editing the knowledge of LLMs post-training can create messy ripple effects

After the advent of ChatGPT, the readily available model developed by Open AI, large language models (LLMs) have become increasingly widespread, with many online users now accessing them daily to quickly get answers to their queries, source information or produce customized texts. Despite their striking ability to rapidly define words and generate written texts pertinent to a user’s queries, the answers given by these models are not always accurate and reliable.
AI Generated Robotic Content

Share
Published by
AI Generated Robotic Content

Recent Posts

Viggle Remake: open weights for recasting / editing whole videos.

Weights: https://huggingface.co/Viggle/Viggle-Remake App: https://viggle.ai/meme/app/home?ui=v2 Post: https://x.com/ViggleAI/status/2118012913793290414 We just released Viggle Remake on Hugging Face: open…

7 hours ago

How to Train Your own Decision Model like Jev using Unsloth

In this article, you will learn how to turn an open-weight language model into a…

7 hours ago

Use decision models with Amazon Bedrock AgentCore Evaluations

Some of the questions you ask when evaluating an agent are narrow and repeatable. Did…

7 hours ago

The Blue Wave Has Tech Bros Scrambling for Data Centers in Space

After Democrats won big in Texas and other AI infrastructure hot spots on Tuesday, data…

8 hours ago

Some AI bots work harder to defy stereotypes, study finds

Large language models (LLMs), the technology that powers tools like Gemini and ChatGPT, are more…

8 hours ago