Bibliographic Details
| Title: |
Graph Elicitation for Guiding Multi-Step Reasoning in Large Language Models |
| Authors: |
Park, Jinyoung, Patel, Ameen, Khan, Omar Zia, Kim, Hyunwoo J., Kim, Joo-Kyung |
| Publication Year: |
2023 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| More Details: |
Chain-of-Thought (CoT) prompting along with sub-question generation and answering has enhanced multi-step reasoning capabilities of Large Language Models (LLMs). However, prompting the LLMs to directly generate sub-questions is suboptimal since they sometimes generate redundant or irrelevant questions. To deal with them, we propose a GE-Reasoning method, which directs LLMs to generate proper sub-questions and corresponding answers. Concretely, given an input question, we first prompt the LLM to generate knowledge triplets, forming a graph representation of the question. Unlike conventional knowledge triplets, our approach allows variables as head or tail entities, effectively representing a question as knowledge triplets. Second, for each triplet, the LLM generates a corresponding sub-question and answer along with using knowledge retrieval. If the prediction confidence exceeds a threshold, the sub-question and prediction are incorporated into the prompt for subsequent processing. This approach encourages that sub-questions are grounded in the extracted knowledge triplets, reducing redundancy and irrelevance. Our experiments demonstrate that our approach outperforms previous CoT prompting methods and their variants on multi-hop question answering benchmark datasets.
Comment: Preprint |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2311.09762 |
| Accession Number: |
edsarx.2311.09762 |
| Database: |
arXiv |
