Zobrazeno 1 - 10
of 18 562
pro vyhledávání: '"A. Ramamurthy"'
Autor:
Ramamurthy, Rajkumar, Rajeev, Meghana Arakkal, Molenschot, Oliver, Zou, James, Rajani, Nazneen
Large language models (LLMs) often fail to synthesize information from their context to generate an accurate response. This renders them unreliable in knowledge intensive settings where reliability of the output is key. A critical component for relia
Externí odkaz:
http://arxiv.org/abs/2411.03300
Mechanistic interpretability aims to provide human-understandable insights into the inner workings of neural network models by examining their internals. Existing approaches typically require significant manual effort and prior knowledge, with strate
Externí odkaz:
http://arxiv.org/abs/2410.16484
Autor:
Trivedi, Prapti, Gulati, Aditya, Molenschot, Oliver, Rajeev, Meghana Arakkal, Ramamurthy, Rajkumar, Stevens, Keith, Chaudhery, Tanveesh Singh, Jambholkar, Jahnavi, Zou, James, Rajani, Nazneen
LLM-as-a-judge models have been used for evaluating both human and AI generated content, specifically by providing scores and rationales. Rationales, in addition to increasing transparency, help models learn to calibrate its judgments. Enhancing a mo
Externí odkaz:
http://arxiv.org/abs/2410.05495
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
Autor:
Chauhan, Mihir, Satbhai, Abhishek, Hashemi, Mohammad Abuzar, Ali, Mir Basheer, Ramamurthy, Bina, Gao, Mingchen, Lyu, Siwei, Srihari, Sargur
Handwriting Verification is a critical in document forensics. Deep learning based approaches often face skepticism from forensic document examiners due to their lack of explainability and reliance on extensive training data and handcrafted features.
Externí odkaz:
http://arxiv.org/abs/2407.21788
Our proposed spin valve prototype showcases a sophisticated design featuring a two-dimensional graphene bilayer positioned between layers of ${CrBr}_3$ ferromagnetic insulators. In this model, proximity coupling plays a pivotal role, influencing the
Externí odkaz:
http://arxiv.org/abs/2407.07602
This study analyzes the application of code-generating Large Language Models in the creation of immutable Solidity smart contracts on the Ethereum Blockchain. Other works have previously analyzed Artificial Intelligence code generation abilities. Thi
Externí odkaz:
http://arxiv.org/abs/2407.11019
The ability to summarize and organize knowledge into abstract concepts is key to learning and reasoning. Many industrial applications rely on the consistent and systematic use of concepts, especially when dealing with decision-critical knowledge. How
Externí odkaz:
http://arxiv.org/abs/2405.20163
Autor:
Chauhan, Mihir, Hashemi, Mohammad Abuzar, Satbhai, Abhishek, Ali, Mir Basheer, Ramamurthy, Bina, Gao, Mingchen, Lyu, Siwei, Srihari, Sargur
We present SSL-HV: Self-Supervised Learning approaches applied to the task of Handwriting Verification. This task involves determining whether a given pair of handwritten images originate from the same or different writer distribution. We have compar
Externí odkaz:
http://arxiv.org/abs/2405.18320