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Sujit Pal - Building Learning to Rank models for search using LLMs | PyData Global 2023
Discover how to build efficient Learning to Rank models for search using Pre-trained Large Language Models (LLMs). Learn about the benefits of transfer learning, pairwise models, and pre-training techniques for high-quality search results.
- Using transformers for search ranking can build learning to rank models
- Point-wise regression and pairwise models, such as RankNet, LambdaRank, and LambdaMart, are used
- Creating relevance judgments for queries can be costly and time-consuming, so using pre-trained LLMs can be useful
- Pre-training LLMs on a dataset can be valuable for search applications, even if labels are not provided
- Using Transformers for search ranking can improve learning to rank models and provide high-quality search results
- The results of the evaluation showed that pre-trained LLMs can be valuable for search applications
- The use of Transfer Learning in NLP-based search applications can provide high-quality search results
- Pairwise learning to rank models, such as RankNet, LambdaRank, and LambdaMart, can provide better results compared to point-wise models
- Pre-training language models for specific tasks or domain can provide high-quality models for search applications
- Learning to Rank models can be applied to different domains and are not limited to one specific domain.
- Using pre-trained language models for information retrieval can be beneficial and improve search ranking.
- There is a need to generate a lot of judgment data for pair-wise ranking, which is time-consuming and labor-intensive task.
- To make the judgment creation process more efficient, a strategy based on random sampling was used in this experiment.
- This approach leads to faster convergence and better results compared to collecting all judgment data.
- The results demonstrate that pre-trained language models can effectively learn to rank documents for complex search queries.