Zobrazeno 1 - 10
of 4 671
pro vyhledávání: '"MOHAN RAO, A."'
Autor:
Purbey, Jebish, Sharma, Drishti, Gupta, Siddhant, Murad, Khawaja, Pullakhandam, Siddartha, Kadiyala, Ram Mohan Rao
This paper presents the system description of our entry for the COLING 2025 RegNLP RIRAG (Regulatory Information Retrieval and Answer Generation) challenge, focusing on leveraging advanced information retrieval and answer generation techniques in reg
Externí odkaz:
http://arxiv.org/abs/2412.06009
Autor:
Purbey, Jebish, Gupta, Siddhant, Manali, Nikhil, Pullakhandam, Siddartha, Sharma, Drishti, Srivastava, Ashay, Kadiyala, Ram Mohan Rao
This paper presents the system description of our entry for the COLING 2025 FMD challenge, focusing on misinformation detection in financial domains. We experimented with a combination of large language models, including Qwen, Mistral, and Gemma-2, a
Externí odkaz:
http://arxiv.org/abs/2412.00549
Autor:
Purbey, Jebish, Pullakhandam, Siddartha, Mehreen, Kanwal, Arham, Muhammad, Sharma, Drishti, Srivastava, Ashay, Kadiyala, Ram Mohan Rao
This paper presents a detailed system description of our entry for the CHiPSAL 2025 shared task, focusing on language detection, hate speech identification, and target detection in Devanagari script languages. We experimented with a combination of la
Externí odkaz:
http://arxiv.org/abs/2411.06850
Autor:
Kadiyala, Ram Mohan Rao
With increasing usage of generative models for text generation and widespread use of machine generated texts in various domains, being able to distinguish between human written and machine generated texts is a significant challenge. While existing mo
Externí odkaz:
http://arxiv.org/abs/2410.16659
Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembl
Externí odkaz:
http://arxiv.org/abs/2410.15998
Autor:
Kadiyala, Ram Mohan Rao, Pullakhandam, Siddartha, Mehreen, Kanwal, Tippareddy, Subhasya, Srivastava, Ashay
This paper presents our system description and error analysis of our entry for NLLP 2024 shared task on Legal Natural Language Inference (L-NLI) \citep{hagag2024legallenssharedtask2024}. The task required classifying these relationships as entailed,
Externí odkaz:
http://arxiv.org/abs/2410.15990
Autor:
Kadiyala, Ram Mohan Rao
This paper presents a detailed system description of our entry for the WASSA 2024 Task 2, focused on cross-lingual emotion detection. We utilized a combination of large language models (LLMs) and their ensembles to effectively understand and categori
Externí odkaz:
http://arxiv.org/abs/2410.15974
This proposed model introduces novel deep learning methodologies. The objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks. Deep learning based solution framework is developed consisting of three app
Externí odkaz:
http://arxiv.org/abs/2310.16380
Publikováno v:
Journal of Ethnic Foods, Vol 11, Iss 1, Pp 1-15 (2024)
Abstract In recent decades, a global shift in lifestyle and the ubiquitous consumption of junk foods have led to dysbiosis and other metabolic disorders significantly impacting human health. Recent studies performed on traditional foods have shown se
Externí odkaz:
https://doaj.org/article/6d5a09dfeb4545c09c910a9200eab379
Publikováno v:
Journal of Ethnic Foods, Vol 11, Iss 1, Pp 1-15 (2024)
Abstract The human gut is inhabited by approximately 100 trillion of microflora, and there exists a reciprocal relationship between human health and the gut microbiota. The major reasons for the dysbiosis in the population of gut microbiota are attri
Externí odkaz:
https://doaj.org/article/4a958d5d7c854ca780afb2328a533b7b