Summary of Curiousllm: Elevating Multi-document Question Answering with Llm-enhanced Knowledge Graph Reasoning, by Zukang Yang et al.
CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning
by Zukang Yang, Zixuan Zhu, Xuan Zhu
First submitted to arxiv on: 13 Apr 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 This paper proposes an enhancement to Large Language Models (LLMs) for open-domain question answering, addressing challenges like hallucinations and knowledge cutoffs. The approach integrates a curiosity-driven reasoning mechanism into an LLM agent, allowing it to generate relevant follow-up questions and guide the information retrieval process more efficiently. The new Follow-upQA dataset is developed, featuring questions and supporting evidence as input, with follow-up questions serving as ground truths. Experimental results show that this enhanced model, CuriousLLM, significantly boosts LLM performance in multi-document question answering (MD-QA) without the need for costly fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make language models better at answering questions by giving them “follow-up” questions to ask. This makes it easier for the model to find the right answers. The researchers created a special dataset with questions and answers that they used to test their new approach, called CuriousLLM. It worked really well and might help us use language models in new ways. |
Keywords
* Artificial intelligence * Fine tuning * Question answering
