Zobrazeno 1 - 10
of 1 007
pro vyhledávání: '"Raheja P"'
Autor:
Raheja, Tarun, Pochhi, Nilay
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis of recent
Externí odkaz:
http://arxiv.org/abs/2410.09097
Autor:
Li, Zelong, Xu, Shuyuan, Mei, Kai, Hua, Wenyue, Rama, Balaji, Raheja, Om, Wang, Hao, Zhu, He, Zhang, Yongfeng
Recent advancements in Large Language Models (LLMs) have shown significant progress in understanding complex natural language. One important application of LLM is LLM-based AI Agent, which leverages the ability of LLM as well as external tools for co
Externí odkaz:
http://arxiv.org/abs/2407.12821
We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-only CoEdIT model. Similar to CoEdIT, Spivavtor performs text editing tas
Externí odkaz:
http://arxiv.org/abs/2404.18880
Autor:
Lee, Mina, Gero, Katy Ilonka, Chung, John Joon Young, Shum, Simon Buckingham, Raheja, Vipul, Shen, Hua, Venugopalan, Subhashini, Wambsganss, Thiemo, Zhou, David, Alghamdi, Emad A., August, Tal, Bhat, Avinash, Choksi, Madiha Zahrah, Dutta, Senjuti, Guo, Jin L. C., Hoque, Md Naimul, Kim, Yewon, Knight, Simon, Neshaei, Seyed Parsa, Sergeyuk, Agnia, Shibani, Antonette, Shrivastava, Disha, Shroff, Lila, Stark, Jessi, Sterman, Sarah, Wang, Sitong, Bosselut, Antoine, Buschek, Daniel, Chang, Joseph Chee, Chen, Sherol, Kreminski, Max, Park, Joonsuk, Pea, Roy, Rho, Eugenia H., Shen, Shannon Zejiang, Siangliulue, Pao
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to
Externí odkaz:
http://arxiv.org/abs/2403.14117
We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent state-of-the-art text editing models for writing assistance. mEdIT models are trained by fine-tuning multi-lingual large, pre-trained language models (LLMs) via instruction tuning.
Externí odkaz:
http://arxiv.org/abs/2402.16472
With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of identifying discernible and unique linguistic properties in texts that w
Externí odkaz:
http://arxiv.org/abs/2402.10586
As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized. However, most research on controllable text
Externí odkaz:
http://arxiv.org/abs/2402.04914
In recent times, large language models (LLMs) have shown impressive performance on various document-level tasks such as document classification, summarization, and question-answering. However, research on understanding their capabilities on the task
Externí odkaz:
http://arxiv.org/abs/2311.09182
Autor:
Kumar, Dhruv, Raheja, Vipul, Kaiser-Schatzlein, Alice, Perry, Robyn, Joshi, Apurva, Hugues-Nuger, Justin, Lou, Samuel, Chowdhury, Navid
We present Speakerly, a new real-time voice-based writing assistance system that helps users with text composition across various use cases such as emails, instant messages, and notes. The user can interact with the system through instructions or dic
Externí odkaz:
http://arxiv.org/abs/2310.16251
Large Language Models are cognitively biased judges. Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-context learning. In this work, we assemble 15 LLMs of four different size
Externí odkaz:
http://arxiv.org/abs/2309.17012