Papers by Lori Moon
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. |
GLUCOSE: GeneraLized and COntextualized Story Explanations (2020.emnlp-main)
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Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll
| Challenge: | Existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE’s rich inferential content. |
| Approach: | They propose a platform for crowdsourcing GLUCOSE data at scale that uses semi-structured templates to elicit causal explanations. |
| Outcome: | The proposed model can be trained on human-readable stories and build similar models on unseen stories. |
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions (C18-1)
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| Challenge: | Existing corpus of child-directed speech augments existing corpus for semantic role labels . sense and number of arguments were open to multiple interpretations due to rapidly changing discourse . |
| Approach: | They propose to augment an existing corpus of child-directed speech to provide supervised learning of semantic role labels. |
| Outcome: | The resulting corpus is a gold standard for supervised learning of semantic role labels in child-directed speech. |