Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Manuvinakurike, Ramesh"'
Autor:
Kumar, Shachi H, Sahay, Saurav, Mazumder, Sahisnu, Okur, Eda, Manuvinakurike, Ramesh, Beckage, Nicole, Su, Hsuan, Lee, Hung-yi, Nachman, Lama
Large Language Models (LLMs) have excelled at language understanding and generating human-level text. However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the m
Externí odkaz:
http://arxiv.org/abs/2408.03907
In this work, we develop a prompting approach for incremental summarization of task videos. We develop a sample-efficient few-shot approach for extracting semantic concepts as an intermediate step. We leverage an existing model for extracting the con
Externí odkaz:
http://arxiv.org/abs/2303.04361
Autor:
Su, Hsuan, Kumar, Shachi H, Mazumder, Sahisnu, Chen, Wenda, Manuvinakurike, Ramesh, Okur, Eda, Sahay, Saurav, Nachman, Lama, Chen, Shang-Tse, Lee, Hung-yi
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems. The traditional techniques treat knowledge as part of the input sequence for the dialogue system, prepending a set of knowledg
Externí odkaz:
http://arxiv.org/abs/2302.05888
Autor:
Manuvinakurike, Ramesh, Biswas, Sovan, Raffa, Giuseppe, Beckwith, Richard, Rhodes, Anthony, Shi, Meng, Mejia, Gesem Gudino, Sahay, Saurav, Nachman, Lama
Development of task guidance systems for aiding humans in a situated task remains a challenging problem. The role of search (information retrieval) and conversational systems for task guidance has immense potential to help the task performers achieve
Externí odkaz:
http://arxiv.org/abs/2211.01824
Recent temporal action segmentation approaches need frame annotations during training to be effective. These annotations are very expensive and time-consuming to obtain. This limits their performances when only limited annotated data is available. In
Externí odkaz:
http://arxiv.org/abs/2211.01311
Publikováno v:
Journal of Medical Internet Research, Vol 16, Iss 12, p e285 (2014)
BackgroundAutomated health behavior change interventions show promise, but suffer from high attrition and disuse. The Internet abounds with thousands of personal narrative accounts of health behavior change that could not only provide useful informat
Externí odkaz:
https://doaj.org/article/1ce6f955b1774de6b1f6a22c6ca9d2c0
Conversational agents have become an integral part of the general population for simple task enabling situations. However, these systems are yet to have any social impact on the diverse and minority population, for example, helping people with neurol
Externí odkaz:
http://arxiv.org/abs/2112.02246
Human ratings are one of the most prevalent methods to evaluate the performance of natural language processing algorithms. Similarly, it is common to measure the quality of sentences generated by a natural language generation model using human raters
Externí odkaz:
http://arxiv.org/abs/2104.05224
Autor:
Paetzel, Maike, Manuvinakurike, Ramesh
In an increasingly globalized world, geographic literacy is crucial. In this paper, we present a collaborative two-player game to improve people's ability to locate countries on the world map. We discuss two implementations of the game: First, we cre
Externí odkaz:
http://arxiv.org/abs/1909.00945
This work presents the task of modifying images in an image editing program using natural language written commands. We utilize a corpus of over 6000 image edit text requests to alter real world images collected via crowdsourcing. A novel framework c
Externí odkaz:
http://arxiv.org/abs/1812.01083