Summary of Unsupervised Text Representation Learning Via Instruction-tuning For Zero-shot Dense Retrieval, by Qiuhai Zeng et al.
Unsupervised Text Representation Learning via Instruction-Tuning for Zero-Shot Dense Retrieval
by Qiuhai Zeng, Zimeng Qiu, Dae Yon Hwang, Xin He, William M. Campbell
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel unsupervised text representation learning technique for information retrieval (IR) systems. The proposed method, based on the dual-encoder retrieval framework, uses instruction-tuning to pre-trained large language models (LLMs). This approach generates synthetic queries and updates the LLM’s encoder-decoder architecture. By fine-tuning the model with these generated queries, the authors demonstrate significant improvements in zero-shot retrieval performance on several English and German datasets, including NDCG@10, MRR@100, and Recall@100. The proposed method outperforms competitive dense retrievers like mDPR, T-Systems, and mBART-Large, while using models with smaller sizes (at least 38% smaller). This technique has the potential to revolutionize information retrieval systems by reducing the need for labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores new ways to make computers better at finding relevant information. They developed a method that doesn’t require much training data, which can be expensive or hard to find. Instead, they use large language models and give them instructions on how to generate questions and summaries. By doing this, the model learns to represent text in a way that’s useful for searching. The authors tested their approach on several datasets and found it outperforms other methods. This could lead to big improvements in search engines and other applications where finding relevant information is important. |
Keywords
» Artificial intelligence » Encoder » Encoder decoder » Fine tuning » Instruction tuning » Recall » Representation learning » Unsupervised » Zero shot
