Zobrazeno 1 - 10
of 46 088
pro vyhledávání: '"Ramakrishna, A."'
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
Autor:
Yaldiz, Duygu Nur, Bakman, Yavuz Faruk, Buyukates, Baturalp, Tao, Chenyang, Ramakrishna, Anil, Dimitriadis, Dimitrios, Zhao, Jieyu, Avestimehr, Salman
Uncertainty estimation (UE) of generative large language models (LLMs) is crucial for evaluating the reliability of generated sequences. A significant subset of UE methods utilize token probabilities to assess uncertainty, aggregating multiple token
Externí odkaz:
http://arxiv.org/abs/2406.11278
Autor:
Arora, Daman, Sonwane, Atharv, Wadhwa, Nalin, Mehrotra, Abhav, Utpala, Saiteja, Bairi, Ramakrishna, Kanade, Aditya, Natarajan, Nagarajan
A common method to solve complex problems in software engineering, is to divide the problem into multiple sub-problems. Inspired by this, we propose a Modular Architecture for Software-engineering AI (MASAI) agents, where different LLM-powered sub-ag
Externí odkaz:
http://arxiv.org/abs/2406.11638
Decoding methods for large language models (LLMs) usually struggle with the tradeoff between ensuring factuality and maintaining diversity. For example, a higher p threshold in the nucleus (top-p) sampling increases the diversity but decreases the fa
Externí odkaz:
http://arxiv.org/abs/2406.07735