Výsledky vyhledávání - "Thiagarajan Sridhar"

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Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models

Autor: Chow, Yinlam, Tennenholtz, Guy, Gur, Izzeddin, Zhuang, Vincent, Dai, Bo, Thiagarajan, Sridhar, Boutilier, Craig, Agarwal, Rishabh, Kumar, Aviral, Faust, Aleksandra

Recent studies have indicated that effectively utilizing inference-time compute is crucial for attaining better performance from large language models (LLMs). In this work, we propose a novel inference-aware fine-tuning paradigm, in which the model i

Externí odkaz: http://arxiv.org/abs/2412.15287

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Finetuning Language Models to Emit Linguistic Expressions of Uncertainty

Autor: Chaudhry, Arslan, Thiagarajan, Sridhar, Gorur, Dilan

Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. Despite their broad utility, LLMs tend to generate information that conflicts with real-world facts, and their persuasive style can make these in

Externí odkaz: http://arxiv.org/abs/2409.12180

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Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach

Autor: Jurenka, Irina, Kunesch, Markus, McKee, Kevin R., Gillick, Daniel, Zhu, Shaojian, Wiltberger, Sara, Phal, Shubham Milind, Hermann, Katherine, Kasenberg, Daniel, Bhoopchand, Avishkar, Anand, Ankit, Pîslar, Miruna, Chan, Stephanie, Wang, Lisa, She, Jennifer, Mahmoudieh, Parsa, Rysbek, Aliya, Ko, Wei-Jen, Huber, Andrea, Wiltshire, Brett, Elidan, Gal, Rabin, Roni, Rubinovitz, Jasmin, Pitaru, Amit, McAllister, Mac, Wilkowski, Julia, Choi, David, Engelberg, Roee, Hackmon, Lidan, Levin, Adva, Griffin, Rachel, Sears, Michael, Bar, Filip, Mesar, Mia, Jabbour, Mana, Chaudhry, Arslan, Cohan, James, Thiagarajan, Sridhar, Levine, Nir, Brown, Ben, Gorur, Dilan, Grant, Svetlana, Hashimshoni, Rachel, Weidinger, Laura, Hu, Jieru, Chen, Dawn, Dolecki, Kuba, Akbulut, Canfer, Bileschi, Maxwell, Culp, Laura, Dong, Wen-Xin, Marchal, Nahema, Van Deman, Kelsie, Misra, Hema Bajaj, Duah, Michael, Ambar, Moran, Caciularu, Avi, Lefdal, Sandra, Summerfield, Chris, An, James, Kamienny, Pierre-Alexandre, Mohdi, Abhinit, Strinopoulous, Theofilos, Hale, Annie, Anderson, Wayne, Cobo, Luis C., Efron, Niv, Ananda, Muktha, Mohamed, Shakir, Heymans, Maureen, Ghahramani, Zoubin, Matias, Yossi, Gomes, Ben, Ibrahim, Lila

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every

Externí odkaz: http://arxiv.org/abs/2407.12687

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Sample Efficient Deep Reinforcement Learning via Local Planning

Autor: Yin, Dong, Thiagarajan, Sridhar, Lazic, Nevena, Rajaraman, Nived, Hao, Botao, Szepesvari, Csaba

The focus of this work is sample-efficient deep reinforcement learning (RL) with a simulator. One useful property of simulators is that it is typically easy to reset the environment to a previously observed state. We propose an algorithmic framework,

Externí odkaz: http://arxiv.org/abs/2301.12579

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