Zobrazeno 1 - 10
of 4 743
pro vyhledávání: '"Padhi P"'
Publikováno v:
Exploratory Animal and Medical Research, Vol 13, Iss 2, Pp 231-242 (2023)
Pesticides are the ubiquitous xenobiotics. Ethion, an organophosphorus pesticide, induces adverse effects in animals upon ingestion. Quercetin, a potent antioxidant, has very low water solubility. As a result, this study aimed to synthesize and ana
Externí odkaz:
https://doaj.org/article/64d61beddea348d1b66dce0cb0f75439
Autor:
Lee, Bruce W., Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Miehling, Erik, Dognin, Pierre, Nagireddy, Manish, Dhurandhar, Amit
LLMs have shown remarkable capabilities, but precisely controlling their response behavior remains challenging. Existing activation steering methods alter LLM behavior indiscriminately, limiting their practical applicability in settings where selecti
Externí odkaz:
http://arxiv.org/abs/2409.05907
Autor:
Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Sattigeri, Prasanna, Nagireddy, Manish, Dognin, Pierre, Varshney, Kush R.
Aligning large language models (LLMs) to value systems has emerged as a significant area of research within the fields of AI and NLP. Currently, this alignment process relies on the availability of high-quality supervised and preference data, which c
Externí odkaz:
http://arxiv.org/abs/2408.10392
Training state-of-the-art (SOTA) deep learning models requires a large amount of data. The visual information present in the training data can be misused, which creates a huge privacy concern. One of the prominent solutions for this issue is perceptu
Externí odkaz:
http://arxiv.org/abs/2407.06570
Autor:
Padhi, Sudev Kumar, Ali, Sk. Subidh
Recent developments in Deep Neural Network (DNN) based watermarking techniques have shown remarkable performance. The state-of-the-art DNN-based techniques not only surpass the robustness of classical watermarking techniques but also show their robus
Externí odkaz:
http://arxiv.org/abs/2407.06552
Large language models (LLMs) have convincing performance in a variety of downstream tasks. However, these systems are prone to generating undesirable outputs such as harmful and biased text. In order to remedy such generations, the development of gua
Externí odkaz:
http://arxiv.org/abs/2407.06323
In this work, we investigate two salient chaotic features, namely Lyapunov exponent and butterfly velocity, in the context of an asymptotically Lifshitz black hole background with an arbitrary critical exponent. These features are computed using thre
Externí odkaz:
http://arxiv.org/abs/2406.18319
The landscape of fake media creation changed with the introduction of Generative Adversarial Networks (GAN s). Fake media creation has been on the rise with the rapid advances in generation technology, leading to new challenges in Detecting fake medi
Externí odkaz:
http://arxiv.org/abs/2406.18278
In this paper, we investigate pulsating string solutions within the context of a two-parameter $\chi$-deformed $\mathcal{R} \times S^2$ background. We derive the energy and oscillation number relation for the deformed $\mathcal{R} \times S^2$ in a sh
Externí odkaz:
http://arxiv.org/abs/2406.17449
Autor:
Hou, Yufang, Pascale, Alessandra, Carnerero-Cano, Javier, Tchrakian, Tigran, Marinescu, Radu, Daly, Elizabeth, Padhi, Inkit, Sattigeri, Prasanna
Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs), such as hallucinations and outdated information. However, it remains unclear how LLMs handle knowledge conflicts ari
Externí odkaz:
http://arxiv.org/abs/2406.13805