Zobrazeno 1 - 10
of 201
pro vyhledávání: '"Dernoncourt, Franck"'
Large Language Models (LLMs) excel in various natural language processing tasks, but leveraging them for dense passage embedding remains challenging. This is due to their causal attention mechanism and the misalignment between their pre-training obje
Externí odkaz:
http://arxiv.org/abs/2408.03402
Autor:
Aponte, Ryan, Rossi, Ryan A., Guo, Shunan, Dernoncourt, Franck, Yu, Tong, Chen, Xiang, Mitra, Subrata, Lipka, Nedim
Large language models (LLMs) have been applied to a wide range of tasks, including text summarization, web navigation, and chatbots. They have benefitted from supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) followin
Externí odkaz:
http://arxiv.org/abs/2408.02861
Autor:
Eppalapally, Swetha, Dangi, Daksh, Bhat, Chaithra, Gupta, Ankita, Zhang, Ruiyi, Agarwal, Shubham, Bagga, Karishma, Yoon, Seunghyun, Lipka, Nedim, Rossi, Ryan A., Dernoncourt, Franck
Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly d
Externí odkaz:
http://arxiv.org/abs/2407.16073
Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models
Autor:
Nguyen, Minh, Dernoncourt, Franck, Yoon, Seunghyun, Deilamsalehy, Hanieh, Tan, Hao, Rossi, Ryan, Tran, Quan Hung, Bui, Trung, Nguyen, Thien Huu
We introduce an approach to identifying speaker names in dialogue transcripts, a crucial task for enhancing content accessibility and searchability in digital media archives. Despite the advancements in speech recognition, the task of text-based spea
Externí odkaz:
http://arxiv.org/abs/2407.12094
Autor:
Parmar, Mihir, Deilamsalehy, Hanieh, Dernoncourt, Franck, Yoon, Seunghyun, Rossi, Ryan A., Bui, Trung
Extractive summarization plays a pivotal role in natural language processing due to its wide-range applications in summarizing diverse content efficiently, while also being faithful to the original content. Despite significant advancement achieved in
Externí odkaz:
http://arxiv.org/abs/2407.04855
Autor:
Kumar, Ishita, Viswanathan, Snigdha, Yerra, Sushrita, Salemi, Alireza, Rossi, Ryan A., Dernoncourt, Franck, Deilamsalehy, Hanieh, Chen, Xiang, Zhang, Ruiyi, Agarwal, Shubham, Lipka, Nedim, Zamani, Hamed
Long-text generation is seemingly ubiquitous in real-world applications of large language models such as generating an email or writing a review. Despite the fundamental importance and prevalence of long-text generation in many practical applications
Externí odkaz:
http://arxiv.org/abs/2407.11016
FActScore has gained popularity as a metric to estimate the factuality of long-form texts generated by Large Language Models (LLMs) in English. However, there has not been any work in studying the behavior of FActScore in other languages. This paper
Externí odkaz:
http://arxiv.org/abs/2406.19415
Autor:
Wang, Yu, Rossi, Ryan A., Park, Namyong, Chen, Huiyuan, Ahmed, Nesreen K., Trivedi, Puja, Dernoncourt, Franck, Koutra, Danai, Derr, Tyler
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous domains. This training paradigm over diverse well-curated
Externí odkaz:
http://arxiv.org/abs/2406.05109
Aspect-Based Sentiment Analysis (ABSA) has experienced tremendous expansion and diversity due to various shared tasks spanning several languages and fields and organized via SemEval workshops and Germeval. Nonetheless, a few shortcomings still need t
Externí odkaz:
http://arxiv.org/abs/2405.20274
Autor:
Sharma, Sanat, Yoon, David Seunghyun, Dernoncourt, Franck, Sultania, Dewang, Bagga, Karishma, Zhang, Mengjiao, Bui, Trung, Kotte, Varun
Question answering (QA) has become an important application in the advanced development of large language models. General pre-trained large language models for question-answering are not trained to properly understand the knowledge or terminology for
Externí odkaz:
http://arxiv.org/abs/2404.14760