A naukri.com initiative
Towards Data Science
1M
284
Image Credit: Towards Data Science
Unraveling Large Language Model Hallucinations
- Large language model hallucinations, exemplified by the generation of fabricated information like the character Zyler Vance from a movie, are a common issue stemming from the training process.
- The training pipeline for language models involves stages such as pretraining, where models learn general language patterns, and supervised fine-tuning with conversation datasets to refine responses.
- The base model trained through pretraining predicts the next word in a sequence by sampling diverse internet text documents.
- Human-generated and annotated conversation datasets like OpenAssistant/oasst1 are utilized to further refine the pretrained model into an assistant capable of human-like responses.
- Mitigating hallucinations involves probing the model's knowledge boundaries, adding examples of unknown information to training sets, and using reinforcement learning with human feedback.
- Meta's Llama 3 series utilized a knowledge-probing technique to align model responses with factual data and improve correct answer rates, reducing hallucinations.
- Implementing mechanisms like special tokens for web searches in LLMs enhances their ability to provide factual responses by allowing access to external information sources.
- Training sets demonstrating how models can utilize web search tools effectively contribute to improving response accuracy and reducing hallucinations.
- Despite advancements, completely eliminating hallucinations from LLMs remains a challenge, emphasizing the importance of continued research in mitigating these issues for reliable knowledge representation.
- Strategies like knowledge probing and training models to use web search tools have shown promise in reducing hallucinations and improving response accuracy in language models.
Read Full Article
16 Likes
For uninterrupted reading, download the app