Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 25·5 min read
Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
TL;DR
LLM-QL, a new model for dense retrieval, enhances the use of large language models (LLMs) by integrating query likelihood (QL) maximization. Evaluations show LLM-QL outperforms existing LLM-based retrievers on both in-domain and out-of-domain datasets.
✦ Why It Matters
Consider implementing LLM-QL to enhance your dense retrieval systems today.
Key Takeaways
- Explore LLM-QL for improved dense retrieval performance.
- Utilize query likelihood maximization in your models.
- Implement Attention Block to refine token attention.
- Experiment with Document Corruption for better predictions.
How It Works
LLM-QL enhances dense retrieval by maximizing query likelihood, which helps the model focus on relevant document tokens. The Attention Block prevents predictive tokens from attending to document tokens after the document's end, ensuring better contextual understanding.
Document Corruption introduces variability by masking parts of documents, which aids in training the model to predict missing information more effectively.