Papers by Narutatsu Ri
Reranking-based Generation for Unbiased Perspective Summarization (2025.findings-acl)
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| Challenge: | Existing evaluation frameworks rely on traditional metrics for measuring key attributes such as coverage and faithfulness without verifying their applicability. |
| Approach: | They propose to use human annotations to measure perspective summary quality and reranking-based methods yield strong results. |
| Outcome: | The proposed methods show that they perform well with synthetically generated and reranking-labeled data. |
Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution (2025.coling-main)
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| Challenge: | Recent authorship attribution methods learn authorship representations of text in a latent, uninterpretable space, which hinders their usability in real-world applications. |
| Approach: | They propose a method for interpreting latent authorship representations by identifying representative points in the latent space and leveraging large language models to generate informative natural language descriptions of the writing style associated with each point. |
| Outcome: | The proposed method outperforms baseline methods on the authorship attribution task by +20% on average when aided with explanations from the method. |
Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies (2023.findings-emnlp)
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Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, Dragomir Radev
| Challenge: | In-context learning (ICL) is a new approach to natural language processing tasks that rely on large language models to make predictions based on context . recent studies have shown that neural symbolic design is the preferred choice for question answering systems because of its limited working memory and unreliable long-term memory. |
| Approach: | They propose to extend in-context learning to question answering tasks that utilize structured knowledge sources and to explore various prompt design strategies for employing LLMs. |
| Outcome: | The proposed approach outperforms the state-of-the-art system by 2.5 points and the best fine-tuned system by 5.1 points on the Spider dataset. |