Zobrazeno 1 - 10
of 738
pro vyhledávání: '"Rohini, K."'
This study introduces 'clickbait spoiling', a novel technique designed to detect, categorize, and generate spoilers as succinct text responses, countering the curiosity induced by clickbait content. By leveraging a multi-task learning framework, our
Externí odkaz:
http://arxiv.org/abs/2405.04292
Autor:
Das, Souvik, Srihari, Rohini K.
State-of-the-art conversational AI systems raise concerns due to their potential risks of generating unsafe, toxic, unethical, or dangerous content. Previous works have developed datasets to teach conversational agents the appropriate social paradigm
Externí odkaz:
http://arxiv.org/abs/2402.00446
Answering questions over domain-specific graphs requires a tailored approach due to the limited number of relations and the specific nature of the domain. Our approach integrates classic logical programming languages into large language models (LLMs)
Externí odkaz:
http://arxiv.org/abs/2303.02206
Knowledge Graph(KG) grounded conversations often use large pre-trained models and usually suffer from fact hallucination. Frequently entities with no references in knowledge sources and conversation history are introduced into responses, thus hinderi
Externí odkaz:
http://arxiv.org/abs/2301.04449
Autor:
Rohini, K.
Publikováno v:
Malaysian Journal of Microbiology, Vol 6, Iss 1, Pp 94-98 (2010)
In the past 16s rRNA gene sequencing has been used to find out the evolutionary pattern and the phylogenetic relationship among bacteria. Despite its accuracy, 16S rRNA gene sequence analysis lacks widespread use beyond the large reference laboratori
Externí odkaz:
https://doaj.org/article/c728e7c5a8404c26aab5d43df8151f6c
Personalized response selection systems are generally grounded on persona. However, there exists a co-relation between persona and empathy, which is not explored well in these systems. Also, faithfulness to the conversation context plunges when a con
Externí odkaz:
http://arxiv.org/abs/2208.09601
Autor:
Pathak, Archita, Srihari, Rohini K.
While recent work on automated fact-checking has focused mainly on verifying and explaining claims, for which the list of claims is readily available, identifying check-worthy claim sentences from a text remains challenging. Current claim identificat
Externí odkaz:
http://arxiv.org/abs/2111.01706
In this paper, we present our Alexa Prize Grand Challenge 4 socialbot: Proto. Leveraging diverse sources of world knowledge, and powered by a suite of neural and rule-based natural language understanding modules, state-of-the-art neural generators, n
Externí odkaz:
http://arxiv.org/abs/2109.02513
The Covid-19 pandemic has caused a spur in the medical research literature. With new research advances in understanding the virus, there is a need for robust text mining tools which can process, extract and present answers from the literature in a co
Externí odkaz:
http://arxiv.org/abs/2108.01436
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