Zobrazeno 1 - 10
of 4 769
pro vyhledávání: '"P Soman"'
Despite extensive research, the precise role of acoustic and semantic cues in complex speech perception tasks remains unclear. In this study, we propose a paradigm to understand the encoding of these cues in electroencephalogram (EEG) data, using mat
Externí odkaz:
http://arxiv.org/abs/2411.11308
Autor:
Soman, Karthik, Langdon, Andrew, Villouta, Catalina, Agrawal, Chinmay, Salta, Lashaw, Peetoom, Braian, Bellucci, Gianmarco, Buske, Orion J
Rare diseases present unique challenges in healthcare, often suffering from delayed diagnosis and fragmented information landscapes. The scarcity of reliable knowledge in these conditions poses a distinct challenge for Large Language Models (LLMs) in
Externí odkaz:
http://arxiv.org/abs/2411.02657
Autor:
Roychowdhury, Sujoy, Soman, Sumit, Ranjani, HG, Sharma, Avantika, Gunda, Neeraj, Bala, Sai Krishna
With the ubiquitous use of document corpora for question answering, one important aspect which is especially relevant for technical documents is the ability to extract information from tables which are interspersed with text. The major challenge in t
Externí odkaz:
http://arxiv.org/abs/2408.17008
This paper presents our findings on the automatic summarization of Java methods within Ericsson, a global telecommunications company. We evaluate the performance of an approach called Automatic Semantic Augmentation of Prompts (ASAP), which uses a La
Externí odkaz:
http://arxiv.org/abs/2408.09735
Autor:
Roychowdhury, Sujoy, Soman, Sumit, Ranjani, H G, Gunda, Neeraj, Chhabra, Vansh, Bala, Sai Krishna
Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks in various domains. However, RAG based on open-source LLM for specialized domains has challenges of evaluating generated
Externí odkaz:
http://arxiv.org/abs/2407.12873
One of the key predictions of the standard inflationary paradigm is the quantum mechanical generation of the transverse and traceless tensor fluctuations due to the rapid accelerated expansion of space, which later constitute a stochastic background
Externí odkaz:
http://arxiv.org/abs/2407.07956
Autor:
Roychowdhury, Sujoy, Soman, Sumit, Ranjani, H. G., Chhabra, Vansh, Gunda, Neeraj, Gautam, Shashank, Bandyopadhyay, Subhadip, Bala, Sai Krishna
A plethora of sentence embedding models makes it challenging to choose one, especially for technical domains rich with specialized vocabulary. In this work, we domain adapt embeddings using telecom, health and science datasets for question answering.
Externí odkaz:
http://arxiv.org/abs/2406.12336
Large language models have limited context capacity, hindering reasoning over long conversations. We propose the Hierarchical Aggregate Tree memory structure to recursively aggregate relevant dialogue context through conditional tree traversals. HAT
Externí odkaz:
http://arxiv.org/abs/2406.06124
Autor:
Squires, Matthew, Tao, Xiaohui, Elangovan, Soman, Acharya, U Rajendra, Gururajan, Raj, Xie, Haoran, Zhou, Xujuan
Suicide is a prominent issue in society. Unfortunately, many people at risk for suicide do not receive the support required. Barriers to people receiving support include social stigma and lack of access to mental health care. With the popularity of s
Externí odkaz:
http://arxiv.org/abs/2405.05795
Autor:
Squires, Matthew, Tao, Xiaohui, Elangovan, Soman, Gururajan, Raj, Xie, Haoran, Zhou, Xujuan, Li, Yuefeng, Acharya, U Rajendra
Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent. Emerging evidence suggests that artificial intelligence can predict rT
Externí odkaz:
http://arxiv.org/abs/2404.16913