Summary of Maple: Enhancing Review Generation with Multi-aspect Prompt Learning in Explainable Recommendation, by Ching-wen Yang et al.
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
by Ching-Wen Yang, Che Wei Chen, Kun-da Wu, Hao Xu, Jui-Feng Yao, Hung-Yu Kao
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 Explainable Recommendation task requires models to generate explanations justifying why an item is recommended to a user, given a pair of user and item inputs. Many existing models treat review-generation as a proxy for explainable recommendation, but they suffer from generality and hallucination issues. To address this, we propose the Multi-Aspect Prompt LEarner (MAPLE) model, which integrates aspect category as an input dimension to facilitate memorization of fine-grained aspect terms. MAPLE outperforms baseline review-generation models on two real-world restaurant review datasets in terms of text diversity, feature diversity, coherence, and factual relevance. We also integrate MAPLE with a Large-Language Model (LLM) as a retriever-reader framework, demonstrating enriched and personalized explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MAPLE is a new model that helps explain why you’re recommended something. It’s like saying “you might like this because…”. Right now, many models just try to generate reviews that sound good, but they don’t always make sense or are accurate. MAPLE does better by paying attention to specific details about what makes an item great. In tests on restaurant reviews, MAPLE did a great job of being clear and factual while also being creative. By combining MAPLE with another powerful tool called a Large-Language Model (LLM), we can get even more detailed and personalized explanations. |
Keywords
» Artificial intelligence » Attention » Hallucination » Large language model » Prompt
