Researchers are exploring the issue of hallucinations in Multimodal Large Language Models (MLLMs), noting that fine-tuning with new knowledge can lead to more frequent hallucinations. Grounding techniques are being developed to address this challenge and improve the reliability of LLMs in practical applications.
Grounding aims to combat the hallucination problems of LLMs by tracking back their claims to reliable sources. Paper “Effective large language model adaptation for improved grounding”. Introduces a new framework for grounding of LLMs named AGREE (Adaptation for GRounding… https://t.co/5r8tjWSJ1y
1/n The Perils of New Knowledge: How Fine-tuning Can Lead Large Language Models Astray A persistent challenge plagues these LLMs: hallucinations. These instances of fabricated or inaccurate information undermine the reliability of LLMs, particularly in applications demanding… https://t.co/V4LTyMU80C
🎻 Researchers have found that LLMs fine-tuned using new knowledge tend to hallucinate responses more often. Data quality during fine-tuning makes all the difference! https://t.co/yxT7mgmur0 #ML #MachineLearning #AI #ArtificialIntelligence #DataEngineering #DataEngineer…
“A method to mitigate hallucinations in large language models” Article: https://t.co/ka18QnIaMn
1/5 Dive into the world of Multimodal Large Language Models (MLLMs) with This comprehensive survey explores the hallucination phenomenon in MLLMs, a critical challenge in their practical deployment. #AI #MachineLearning https://t.co/hsOI7Y6sWQ