Summary of Perltqa: a Personal Long-term Memory Dataset For Memory Classification, Retrieval, and Synthesis in Question Answering, by Yiming Du et al.
PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering
by Yiming Du, Hongru Wang, Zhengyi Zhao, Bin Liang, Baojun Wang, Wanjun Zhong, Zezhong Wang, Kam-Fai Wong
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 PerLTQA is a new type of dataset for question-answering tasks that combines semantic and episodic memories to simulate real-life conversations. This innovative approach leverages world knowledge, historical information, preferences, social relationships, events, and dialogues to create personalized memories. The PerLTQA dataset features two types of memory and includes 8,593 questions for 30 characters, making it a comprehensive benchmark for testing the use of personalized memories in Large Language Models (LLMs). To integrate these memories effectively, we propose a novel framework consisting of Memory Classification, Memory Retrieval, and Memory Synthesis. We evaluate this framework using five LLMs and three retrievers, demonstrating that BERT-based classification models outperform LLMs like ChatGLM3 and ChatGPT in memory classification tasks. Our study highlights the importance of effective memory integration in question-answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PerLTQA is a special kind of dataset that helps computers understand how to have conversations by using memories from long ago. This new approach combines lots of different kinds of information, like what we know about the world and our personal experiences, to make conversations feel more realistic. The dataset has over 8,500 questions for 30 characters, making it a big challenge for computer programs called Large Language Models (LLMs). To help these computers use memories better, we came up with a new way of combining and organizing memories. We tested this approach using different LLMs and showed that some models are much better at understanding memories than others. |
Keywords
» Artificial intelligence » Bert » Classification » Question answering
