Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Rose, Carolyn"'
Autor:
Xie, Yiqing, Zhou, Wenxuan, Prakash, Pradyot, Jin, Di, Mao, Yuning, Fettes, Quintin, Talebzadeh, Arya, Wang, Sinong, Fang, Han, Rose, Carolyn, Fried, Daniel, Zhang, Hejia
Factuality evaluation aims to detect factual errors produced by language models (LMs) and hence guide the development of more factual models. Towards this goal, we train a factuality evaluator, FenCE, that provides LM generators with claim-level fact
Externí odkaz:
http://arxiv.org/abs/2410.18359
The task of automated code review has recently gained a lot of attention from the machine learning community. However, current review comment evaluation metrics rely on comparisons with a human-written reference for a given code change (also called a
Externí odkaz:
http://arxiv.org/abs/2409.19801
In the field of natural language processing, correction of performance assessment for chance agreement plays a crucial role in evaluating the reliability of annotations. However, there is a notable dearth of research focusing on chance correction for
Externí odkaz:
http://arxiv.org/abs/2407.11371
We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the
Externí odkaz:
http://arxiv.org/abs/2406.19545
Autor:
Naik, Atharva, Yin, Jessica Ruhan, Kamath, Anusha, Ma, Qianou, Wu, Sherry Tongshuang, Murray, Charles, Bogart, Christopher, Sakr, Majd, Rose, Carolyn P.
An advantage of Large Language Models (LLMs) is their contextualization capability - providing different responses based on student inputs like solution strategy or prior discussion, to potentially better engage students than standard feedback. We pr
Externí odkaz:
http://arxiv.org/abs/2404.18262
To adequately test modern code generation systems, evaluation benchmarks must execute and test the code generated by the system. However, these execution and testing requirements have largely limited benchmarks to settings where code is easily execut
Externí odkaz:
http://arxiv.org/abs/2404.00566
Autor:
Xie, Yiqing, Zhang, Sheng, Cheng, Hao, Liu, Pengfei, Gero, Zelalem, Wong, Cliff, Naumann, Tristan, Poon, Hoifung, Rose, Carolyn
Medical text generation aims to assist with administrative work and highlight salient information to support decision-making. To reflect the specific requirements of medical text, in this paper, we propose a set of metrics to evaluate the completenes
Externí odkaz:
http://arxiv.org/abs/2311.09581
One major challenge of translating code between programming languages is that parallel training data is often limited. To overcome this challenge, we present two data augmentation techniques, one that builds comparable corpora (i.e., code pairs with
Externí odkaz:
http://arxiv.org/abs/2311.00317
Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolding on the in-domain performance of several NLP tasks. We extend this work by exploring the impact of linguistic representa
Externí odkaz:
http://arxiv.org/abs/2307.03823
Autor:
Morales-Navarro, Luis, Kafai, Yasmin B., Castro, Francisco, Payne, William, DesPortes, Kayla, DiPaola, Daniella, Williams, Randi, Ali, Safinah, Breazeal, Cynthia, Lee, Clifford, Soep, Elisabeth, Long, Duri, Magerko, Brian, Solyst, Jaemarie, Ogan, Amy, Tatar, Cansu, Jiang, Shiyan, Chao, Jie, Rosé, Carolyn P., Vakil, Sepehr
Publikováno v:
Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023
Understanding how youth make sense of machine learning and how learning about machine learning can be supported in and out of school is more relevant than ever before as young people interact with machine learning powered applications everyday; while
Externí odkaz:
http://arxiv.org/abs/2305.02840