Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process

핵심 개념

The author introduces the PEI framework to bridge explicit and implicit knowledge in multi-hop question answering, aligning with human reading processes.

초록

The PEI framework leverages prompts to connect explicit and implicit knowledge, improving multi-hop QA performance. Experimental results show comparable performance to state-of-the-art models on HotpotQA. Ablation studies confirm the efficacy of bridging explicit and implicit knowledge.
Psychological studies suggest a vital connection between explicit information in passages and human prior knowledge during reading. The proposed PEI model integrates type-specific reasoning via prompts, enhancing multi-hop QA performance. The framework incorporates insights from human cognition theories to improve reasoning abilities.

통계

Pre-trained language models leverage chains-of-thought (CoT) for human reasoning simulation.
Psychological studies highlight the connection between explicit information and human prior knowledge.
The PEI framework uses prompts to connect explicit and implicit knowledge for multi-hop QA.
Experimental results show comparable performance on HotpotQA.
Ablation studies confirm the efficacy of bridging explicit and implicit knowledge.

인용구

"Readers engage in comprehension by drawing upon both explicit information in text and pre-existing language knowledge."
"PEI model achieves comparable performance with state-of-the-art on HotpotQA."
"Our approach reduces reliance on explicit knowledge by allowing selective filtering of irrelevant information."

핵심 통찰 요약

Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process

by Guangming Hu... 게시일 arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19350.pdf

더 깊은 질문

How can the PEI framework be extended to other domains beyond multi-hop QA?

The PEI framework's principles of leveraging prompts to bridge explicit and implicit knowledge can be applied to various domains beyond multi-hop QA. For instance, in information retrieval systems, prompts could help connect user queries with relevant documents by incorporating prior knowledge and context. In medical diagnosis, prompts could assist in integrating patient symptoms with medical databases to aid healthcare professionals in making accurate diagnoses. Additionally, in financial analysis, prompts could facilitate the integration of historical data and market trends for more informed decision-making.

What potential biases or limitations exist when considering readers as language models?

When considering readers as language models, there are several potential biases and limitations that need to be taken into account. One bias is the assumption that all readers have similar levels of prior knowledge or background understanding on a given topic. This may not always be true and could lead to inaccuracies in reasoning processes based on implicit knowledge. Another limitation is the reliance on pre-trained language models (PLMs) for mimicking human cognition without fully capturing the nuances of human reasoning processes. PLMs may lack contextual understanding or emotional intelligence present in human cognition, leading to biased outcomes.

How can insights from human cognition theories be applied to enhance AI systems in different contexts?

Insights from human cognition theories can significantly enhance AI systems across various contexts by improving their interpretability, adaptability, and performance. By incorporating theories such as redundancy reduction during reading comprehension or drawing upon prior knowledge for inference tasks, AI systems can better mimic human-like reasoning processes. This approach can lead to more robust natural language processing algorithms capable of handling complex tasks like sentiment analysis or summarization effectively.