Zobrazeno 1 - 10
of 14 757
pro vyhledávání: '"MEHRABI, 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
Data is a crucial element in large language model (LLM) alignment. Recent studies have explored using LLMs for efficient data collection. However, LLM-generated data often suffers from quality issues, with underrepresented or absent aspects and low-q
Externí odkaz:
http://arxiv.org/abs/2410.05269
Autor:
Mehrabi, Niloufar, Boroujeni, Sayed Pedram Haeri, Hofseth, Jenna, Razi, Abolfazl, Cheng, Long, Kaur, Manveen, Martin, James, Amin, Rahul
Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection, thanks to their ability to access remote areas and transmit real-tim
Externí odkaz:
http://arxiv.org/abs/2410.10843
The time at which renewable (e.g., solar or wind) energy resources produce electricity cannot generally be controlled. In many settings, consumers have some flexibility in their energy consumption needs, and there is growing interest in demand-respon
Externí odkaz:
http://arxiv.org/abs/2409.07655
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:
Ghahremani, Tanaz, Hoseyni, Mohammad, Ahmadi, Mohammad Javad, Mehrabi, Pouria, Nikoofard, Amirhossein
Data augmentation is a key technique for addressing the challenge of limited datasets, which have become a major component in the training procedures of image processing. Techniques such as geometric transformations and color space adjustments have b
Externí odkaz:
http://arxiv.org/abs/2405.04442
Autor:
Le, Vy, Nissen, Jayson M., Tang, Xiuxiu, Zhang, Yuxiao, Mehrabi, Amirreza, Morphew, Jason W., Chang, Hua Hua, Van Dusen, Ben
In physics education research, instructors and researchers often use research-based assessments (RBAs) to assess students' skills and knowledge. In this paper, we support the development of a mechanics cognitive diagnostic to test and implement effec
Externí odkaz:
http://arxiv.org/abs/2404.00009
Autor:
Qiang, Yao, Nandi, Subhrangshu, Mehrabi, Ninareh, Steeg, Greg Ver, Kumar, Anoop, Rumshisky, Anna, Galstyan, Aram
Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However, their performance on sequence labeling tasks such as intent classifica
Externí odkaz:
http://arxiv.org/abs/2402.15833
Autor:
Mehrabi, Mohammad, Wager, Stefan
Doubly robust methods hold considerable promise for off-policy evaluation in Markov decision processes (MDPs) under sequential ignorability: They have been shown to converge as $1/\sqrt{T}$ with the horizon $T$, to be statistically efficient in large
Externí odkaz:
http://arxiv.org/abs/2402.08201
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies
Autor:
Ovalle, Anaelia, Mehrabi, Ninareh, Goyal, Palash, Dhamala, Jwala, Chang, Kai-Wei, Zemel, Richard, Galstyan, Aram, Pinter, Yuval, Gupta, Rahul
Gender-inclusive NLP research has documented the harmful limitations of gender binary-centric large language models (LLM), such as the inability to correctly use gender-diverse English neopronouns (e.g., xe, zir, fae). While data scarcity is a known
Externí odkaz:
http://arxiv.org/abs/2312.11779