Summary of Q-peft: Query-dependent Parameter Efficient Fine-tuning For Text Reranking with Large Language Models, by Zhiyuan Peng et al.
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
by Zhiyuan Peng, Xuyang Wu, Qifan Wang, Sravanthi Rajanala, Yi Fang
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel query-dependent parameter efficient fine-tuning (Q-PEFT) method is introduced for text reranking in large language models (LLMs). Q-PEFT leverages queries to extract contextual clues from concatenated documents, guiding LLMs to generate more document-specific synthetic queries. This approach improves reranking performance by avoiding limitations of fixed prompts and low adaptation ability. The proposed method replaces traditional retrieval mechanisms with a multi-head attention layer for end-to-end training. Extensive experiments on four public datasets demonstrate the effectiveness of Q-PEFT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at helping us find what we need online! Researchers found ways to make these models learn new skills without retraining the whole thing, which is great news! But they still had some problems: their learned prompts didn’t change for different documents, and they weren’t very good at adapting to new tasks. A team came up with a new way to fine-tune these models called Q-PEFT. It uses queries to help the model learn what’s important in each document, making it better at finding the right answers. |
Keywords
* Artificial intelligence * Fine tuning * Multi head attention * Parameter efficient
