Zobrazeno 1 - 10
of 6 640
pro vyhledávání: '"A A, Kush"'
Autor:
Ramos, Daniel, Mamede, Claudia, Jain, Kush, Canelas, Paulo, Gamboa, Catarina, Goues, Claire Le
Large Language Models (LLMs) have become integral to various software engineering tasks, including code generation, bug detection, and repair. To evaluate model performance in these domains, numerous bug benchmarks containing real-world bugs from sof
Externí odkaz:
http://arxiv.org/abs/2411.13323
Autor:
Knierim, Matilda, Jain, Sahil, Aydoğan, Murat Han, Mitra, Kenneth, Desai, Kush, Saran, Akanksha, Baraka, Kim
Agent learning from human interaction often relies on explicit signals, but implicit social cues, such as prosody in speech, could provide valuable information for more effective learning. This paper advocates for the integration of prosody as a teac
Externí odkaz:
http://arxiv.org/abs/2410.23554
Autor:
Kaushik, Kush, Mondal, Jiban, Bag, Ritesh Kumar, Sharma, Shagun, Salam, Abdul, Nandi, Chayan Kanti
Room temperature single photon sources (SPS) are crucial for developing the next generation quantum technologies. Quantum dots (QDs), recently, have been reported as promising materials as SPS at room temperature. By optimizing the single particle op
Externí odkaz:
http://arxiv.org/abs/2410.22053
Autor:
Guo, Hangzhi, Venkit, Pranav Narayanan, Jang, Eunchae, Srinath, Mukund, Zhang, Wenbo, Mingole, Bonam, Gupta, Vipul, Varshney, Kush R., Sundar, S. Shyam, Yadav, Amulya
The widespread adoption of large language models (LLMs) and generative AI (GenAI) tools across diverse applications has amplified the importance of addressing societal biases inherent within these technologies. While the NLP community has extensively
Externí odkaz:
http://arxiv.org/abs/2410.15467
Autor:
Sarukkai, Vishnu, Shacklett, Brennan, Majercik, Zander, Bhatia, Kush, Ré, Christopher, Fatahalian, Kayvon
Large Language Models (LLMs) have the potential to automate reward engineering by leveraging their broad domain knowledge across various tasks. However, they often need many iterations of trial-and-error to generate effective reward functions. This p
Externí odkaz:
http://arxiv.org/abs/2410.09187
Fine-tuning large language models (LLMs) on instruction datasets is a common way to improve their generative capabilities. However, instruction datasets can be expensive and time-consuming to manually curate, and while LLM-generated data is less labo
Externí odkaz:
http://arxiv.org/abs/2410.05224
Code generation models can help improve many common software tasks ranging from code completion to defect prediction. Most of the existing benchmarks for code generation LLMs focus on code authoring or code completion. Surprisingly, there has been fa
Externí odkaz:
http://arxiv.org/abs/2410.00752
Autor:
Dubey, Kush
Few-shot learning benchmarks are critical for evaluating modern NLP techniques. It is possible, however, that benchmarks favor methods which easily make use of unlabeled text, because researchers can use unlabeled text from the test set to pretrain t
Externí odkaz:
http://arxiv.org/abs/2410.00179
Autor:
Yu, Justin, Hari, Kush, Srinivas, Kishore, El-Refai, Karim, Rashid, Adam, Kim, Chung Min, Kerr, Justin, Cheng, Richard, Irshad, Muhammad Zubair, Balakrishna, Ashwin, Kollar, Thomas, Goldberg, Ken
Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Language-Embedded Gaussian Splat (LEGS): a detailed 3D scene representation
Externí odkaz:
http://arxiv.org/abs/2409.18108
Autor:
Rawat, Ambrish, Schoepf, Stefan, Zizzo, Giulio, Cornacchia, Giandomenico, Hameed, Muhammad Zaid, Fraser, Kieran, Miehling, Erik, Buesser, Beat, Daly, Elizabeth M., Purcell, Mark, Sattigeri, Prasanna, Chen, Pin-Yu, Varshney, Kush R.
As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal system
Externí odkaz:
http://arxiv.org/abs/2409.15398