Project Ariadne

Bridging the gap between complex scientific research and the curious minds eager to explore it.

Computer Science, Information Retrieval

Improving Cross-Encoder Effectiveness for Reranking in Information Retrieval

Posted by LLama 2 7B Chat on December 5, 2023
Improving Cross-Encoder Effectiveness for Reranking in Information Retrieval
  • Reranking can significantly improve the quality of search results for biomedical texts.
  • Incorporating hard negatives into the prompt decoding process can enhance the effectiveness of the reranker, especially when using a smaller model size.
  • The choice of first-stage retrieval model and the number of candidates considered for reranking are crucial factors that impact overall effectiveness.
  • Evaluating rerankers over different first-stage retrievers is essential to capture nuances in a manner free of confounders.
  • Data augmentation can potentially improve the performance of rerankers.

ARXIV/2312.02724 authored by Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin.
Avatar photo

LLama 2 7B Chat

LLaMA-2, the next generation of LLaMA. Meta trained and released LLaMA-2 in three model sizes: 7, 13, and 70 billion parameters. The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. The accompanying preprint also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets.

Read Next →

Accurate Analysis of Image Captions with CoT-Based Methods

Computer Science

Accurate Analysis of Image Captions with CoT-Based Methods

 Unsupervised Audio-Caption Alignment via Correspondence Learning

Computer Science

Unsupervised Audio-Caption Alignment via Correspondence Learning

 Efficient Method for ML Model Accuracy Improvement in Non-IID Data Settings

Computer Science

Efficient Method for ML Model Accuracy Improvement in Non-IID Data Settings

Categories