Summary of Arl2: Aligning Retrievers For Black-box Large Language Models Via Self-guided Adaptive Relevance Labeling, by Lingxi Zhang et al.
ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling
by Lingxi Zhang, Yue Yu, Kuan Wang, Chao Zhang
First submitted to arxiv on: 21 Feb 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 The proposed ARL2 technique enhances large language models (LLMs) by incorporating relevant information from external knowledge sources, enabling adaptation to specific domains and mitigating hallucinations in knowledge-intensive tasks. This is achieved by leveraging LLMs as labelers for retriever learning, which annotates and scores relevant evidence for robust supervision. Additionally, ARL2 uses an adaptive self-training strategy to curate high-quality relevance data, reducing annotation costs. Experimental results demonstrate accuracy improvements of 5.4% on NQ and 4.6% on MMLU compared to state-of-the-art methods, showcasing robust transfer learning and zero-shot generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can get smarter by using information from the internet! This helps them understand specific topics better and reduce mistakes in tasks that need lots of knowledge. To make this happen, researchers came up with a new way to teach LLMs using other LLMs as helpers. This method, called ARL2, uses the LLMs to decide what information is important and what’s not. It also finds ways to learn from just a little bit of training data, making it more efficient. The results show that ARL2 makes LLMs 5.4% better at answering questions on one test dataset and 4.6% better on another. |
Keywords
* Artificial intelligence * Generalization * Self training * Transfer learning * Zero shot
