Scientists from the University of Science and Technology of China (USTC) and Tencent’s YouTu Lab have created a new tool named "Woodpecker." This tool tackles a well-known artificial intelligence (AI) issue called hallucination. In AI, hallucination happens when a model gives out data confidently, even if this data doesn't come from its training material. This issue is seen in large language models like OpenAI’s ChatGPT and Anthropic’s Claude.
The team has designed Woodpecker to address hallucination issues in multimodal large language models (MLLMs). These models, like GPT-4 and its visually enhanced variant GPT-4V, combine text-based language processing with other modalities such as vision.
According to the researchers, Woodpecker operates by utilizing three other AI models besides the one being corrected.
It uses three additional AI models - GPT-3.5 Turbo, Grounding DINO, and BLIP-2-FlanT5, to identify hallucinations and help the main model adjust its outputs to be more accurate.
Woodpecker’s correction process is structured in five stages, which are:
Key concept extraction
Question formulation
Visual knowledge validation
Visual claim generation
Hallucination correction
This method has demonstrated a notable accuracy improvement, marking a 30.66%/24.33% enhancement over the baseline models MiniGPT-4 and mPLUG-Owl. The researchers evaluated various MLLMs and concluded that Woodpecker could be effortlessly integrated with other MLLMs, hinting at its potential for broader application.
From a broader perspective, the advent of AI has paved the way for remarkable progress in medical science. Recently, AI has been a key player in unraveling complex biological data, which in turn has accelerated the pace of drug discovery.
For example, AI-driven tools have significantly expedited the identification and development of new treatment compounds for a plethora of diseases, including cancer.
Furthermore, AI has been instrumental in boosting diagnostic precision, cutting down on the time and resources needed to interpret medical imaging. The melding of AI into healthcare continues to grow, heralding a shift in patient care and medical research.
The achievements of AI, like the Woodpecker model, not only promise advancements in language model corrections but also highlight the potential of AI to contribute significantly across various sectors, including healthcare.
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