Zobrazeno 1 - 10
of 357
pro vyhledávání: '"Galstyan, Aram"'
A popular method for Neural Architecture Search (NAS) is based on growing networks via small local changes to the network's architecture called network morphisms. These methods start with a small seed network and progressively grow the network by add
Externí odkaz:
http://arxiv.org/abs/2411.05855
Understanding long-form video content presents significant challenges due to its temporal complexity and the substantial computational resources required. In this work, we propose an agent-based approach to enhance both the efficiency and effectivene
Externí odkaz:
http://arxiv.org/abs/2410.20252
Autor:
Ko, Jongwoo, Dingliwal, Saket, Ganesh, Bhavana, Sengupta, Sailik, Bodapati, Sravan, Galstyan, Aram
Direct alignment algorithms (DAAs), such as direct preference optimization (DPO), have become popular alternatives for Reinforcement Learning from Human Feedback (RLHF) due to their simplicity, efficiency, and stability. However, the preferences used
Externí odkaz:
http://arxiv.org/abs/2410.09362
Autor:
Lawton, Neal, Padmakumar, Aishwarya, Gaspers, Judith, FitzGerald, Jack, Kumar, Anoop, Steeg, Greg Ver, Galstyan, Aram
QLoRA reduces the memory-cost of fine-tuning a large language model (LLM) with LoRA by quantizing the base LLM. However, quantization introduces quantization errors that negatively impact model performance after fine-tuning. In this paper we introduc
Externí odkaz:
http://arxiv.org/abs/2410.14713
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:
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:
Jia, Jinghan, Komma, Abi, Leffel, Timothy, Peng, Xujun, Nagesh, Ajay, Soliman, Tamer, Galstyan, Aram, Kumar, Anoop
In task-oriented conversational AI evaluation, unsupervised methods poorly correlate with human judgments, and supervised approaches lack generalization. Recent advances in large language models (LLMs) show robust zeroshot and few-shot capabilities a
Externí odkaz:
http://arxiv.org/abs/2406.17304
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:
Yan, Tianyi Lorena, Wang, Fei, Huang, James Y., Zhou, Wenxuan, Yin, Fan, Galstyan, Aram, Yin, Wenpeng, Chen, Muhao
Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks. However, current LLMs exhibit limited robustness to unseen instructions, generating inconsistent outputs when the sam
Externí odkaz:
http://arxiv.org/abs/2402.11138