Papers with Cora
From Generating Answers to Building Explanations: Integrating Multi-Round RAG and Causal Modeling for Scientific QA (2025.naacl-industry)
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Victor Barres, Clifton James McFate, Aditya Kalyanpur, Kailash Karthik Saravanakumar, Lori Moon, Natnael Seifu, Abraham Bautista-Castillo
| Challenge: | Application of Large Language Models to complex causal question answering can be stymied by their opacity and propensity for hallucination. |
| Approach: | They propose a causal QA approach that combines iterative RAG with a formal model of causation. |
| Outcome: | The proposed approach is implemented into a Collaborative Research Assistant (Cora) and evaluated in the life sciences domain. |
Language is All a Graph Needs (2024.findings-eacl)
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| Challenge: | Existing work on integrating graph problems into generative language modeling framework remains limited. |
| Approach: | They propose an LLM with instructions based on natural language to perform graph tasks. |
| Outcome: | The proposed model surpasses all GNN baselines on ogbn-arxiv, Cora and PubMed datasets and sheds light on generative LLMs as new foundation model for graph machine learning. |
Judge and Improve: Towards a Better Reasoning of Knowledge Graphs with Large Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to integrating graph and language models face two key limitations: achieving robust semantic alignment and ensuring interpretability in outputs. |
| Approach: | They propose a framework to integrate graph and language modalities while enhancing transparency. |
| Outcome: | Extensive experiments on three benchmark datasets show that the proposed framework surpasses existing methods in efficiency and generates outputs that are significantly more interpretable. |