| Domain | Title | Authors | arXiv ID | Submission Date | URL | Summary |
|---|---|---|---|---|---|---|
| Healthcare | Extrinsically-Focused Evaluation of Omissions in Medical Summarization | Elliot Schumacher, et al. | 2311.08303v1 | 2023-11-14 | Link | This paper introduces a novel evaluation framework for medical summarization tasks, focusing on the significance of omissions and their impact on clinical decision-making. |
| Education | MC^2: A Multilingual Corpus of Minority Languages in China | Chen Zhang, et al. | 2311.08348v1 | 2023-11-14 | Link | The authors present a new multilingual corpus that includes several minority languages in China, aiming to facilitate linguistic research and the development of language technologies for these underrepresented languages. |
| Robotics | Large Language Models for Robotics: A Survey | Fanlong Zeng, et al. | 2311.07226v1 | 2023-11-13 | Link | This survey paper reviews the current state of research on the integration of large language models in robotics, discussing potential applications, challenges, and future directions. |
| General AI | On-the-Fly Fusion of Large Language Models and Machine Translation | Hieu Hoang, et al. | 2311.08306v1 | 2023-11-14 | Link | The paper proposes a novel approach to dynamically combine large language models with machine translation models to improve translation quality without extensive retraining. |
| Ethics/Safety | SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models | Bertie Vidgen, et al. | 2311.08370v1 | 2023-11-14 | Link | The researchers introduce a comprehensive test suite designed to identify and mitigate safety risks in large language models, with a focus on ethical and societal implications. |
| Natural Language Processing | How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions | Houquan Zhou, et al. | 2311.08287v1 | 2023-11-14 | Link | This study evaluates the syntactic understanding of large language models by designing a series of natural language questions that target specific syntactic phenomena. |