Zobrazeno 1 - 10
of 14 524
pro vyhledávání: '"Ramakrishna, P"'
Contrastive decoding (CD) (Li et al., 2023) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. Although CD is applied to various LMs and domains to enhance open-ended text generation, it is still uncl
Externí odkaz:
http://arxiv.org/abs/2411.01610
Autor:
Bu, Zhiqi, Jin, Xiaomeng, Vinzamuri, Bhanukiran, Ramakrishna, Anil, Chang, Kai-Wei, Cevher, Volkan, Hong, Mingyi
Machine unlearning has been used to remove unwanted knowledge acquired by large language models (LLMs). In this paper, we examine machine unlearning from an optimization perspective, framing it as a regularized multi-task optimization problem, where
Externí odkaz:
http://arxiv.org/abs/2410.22086
Autor:
Wang, Kai, Li, Zekai, Cheng, Zhi-Qi, Khaki, Samir, Sajedi, Ahmad, Vedantam, Ramakrishna, Plataniotis, Konstantinos N, Hauptmann, Alexander, You, Yang
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios. In this paper, we propose EDF (emphasizes the discriminative features)
Externí odkaz:
http://arxiv.org/abs/2410.17193
Autor:
Meng, Tao, Mehrabi, Ninareh, Goyal, Palash, Ramakrishna, Anil, Galstyan, Aram, Zemel, Richard, Chang, Kai-Wei, Gupta, Rahul, Peris, Charith
We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. Given a training corpus and control criteria formulated as a sequence-level constraint on model outputs, our method fine-tunes the LLM on the
Externí odkaz:
http://arxiv.org/abs/2410.05559
Understanding the electrical conductivity of warm dense hydrogen is critical for both fundamental physics and applications in planetary science and inertial confinement fusion. We demonstrate how to calculate the electrical conductivity using the con
Externí odkaz:
http://arxiv.org/abs/2409.15160
Let $n\geq 3$. We show that for every number field $K$ with $\zeta_p \notin K$, the absolute and tame Galois groups of $K$ satisfy the strong $n$-fold Massey property relative to $p$. Our work is based on an adapted version of the proof of the Theore
Externí odkaz:
http://arxiv.org/abs/2409.01028
Autor:
Phogat, Karmvir Singh, Puranam, Sai Akhil, Dasaratha, Sridhar, Harsha, Chetan, Ramakrishna, Shashishekar
Recent research has shown that smaller language models can acquire substantial reasoning abilities when fine-tuned with reasoning exemplars crafted by a significantly larger teacher model. We explore this paradigm for the financial domain, focusing o
Externí odkaz:
http://arxiv.org/abs/2408.12337
Autor:
Markowitz, Elan, Ramakrishna, Anil, Dhamala, Jwala, Mehrabi, Ninareh, Peris, Charith, Gupta, Rahul, Chang, Kai-Wei, Galstyan, Aram
Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. However, KGs and LLMs are often developed separately and must be integrated after training. We intro
Externí odkaz:
http://arxiv.org/abs/2407.21358
Autor:
Ramakrishna, Shreyas, Schmidt, Riaan P., Peshkov, Anton A., Franke-Arnold, Sonja, Surzhykov, Andrey, Fritzsche, Stephan
During recent years interest has been rising for applications of vector light beams towards magnetic field sensing. In particular, a series of experiments were performed to extract information about properties of static magnetic fields from absorptio
Externí odkaz:
http://arxiv.org/abs/2407.17991
In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies \cite{li-etal-2020-multi-encoder} have shown that the context encoder generates noise and makes the mo
Externí odkaz:
http://arxiv.org/abs/2407.03076