Zobrazeno 1 - 10
of 24 287
pro vyhledávání: '"A Gur"'
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
We show that for every polynomial q* there exist polynomial-size, constant-query, non-adaptive PCPs for NP which are perfect zero knowledge against (adaptive) adversaries making at most q* queries to the proof. In addition, we construct exponential-s
Externí odkaz:
http://arxiv.org/abs/2411.07972
We exhibit a total search problem whose communication complexity in the quantum SMP (simultaneous message passing) model is exponentially smaller than in the classical two-way randomized model. Moreover, the quantum protocol is computationally effici
Externí odkaz:
http://arxiv.org/abs/2411.03296
Autor:
Tsesmelis, Theodore, Palmieri, Luca, Khoroshiltseva, Marina, Islam, Adeela, Elkin, Gur, Shahar, Ofir Itzhak, Scarpellini, Gianluca, Fiorini, Stefano, Ohayon, Yaniv, Alali, Nadav, Aslan, Sinem, Morerio, Pietro, Vascon, Sebastiano, Gravina, Elena, Napolitano, Maria Cristina, Scarpati, Giuseppe, Zuchtriegel, Gabriel, Spühler, Alexandra, Fuchs, Michel E., James, Stuart, Ben-Shahar, Ohad, Pelillo, Marcello, Del Bue, Alessio
This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for
Externí odkaz:
http://arxiv.org/abs/2410.24010
Autor:
Suzgun, Mirac, Gur, Tayfun, Bianchi, Federico, Ho, Daniel E., Icard, Thomas, Jurafsky, Dan, Zou, James
As language models (LMs) become integral to fields like healthcare, law, and journalism, their ability to differentiate between fact, belief, and knowledge is essential for reliable decision-making. Failure to grasp these distinctions can lead to sig
Externí odkaz:
http://arxiv.org/abs/2410.21195
We study the complexity of testing properties of quantum channels. First, we show that testing identity to any channel $\mathcal N: \mathbb C^{d_{\mathrm{in}} \times d_{\mathrm{in}}} \to \mathbb C^{d_{\mathrm{out}} \times d_{\mathrm{out}}}$ in diamon
Externí odkaz:
http://arxiv.org/abs/2409.12566
Autor:
Furuta, Hiroki, Lee, Kuang-Huei, Gu, Shixiang Shane, Matsuo, Yutaka, Faust, Aleksandra, Zen, Heiga, Gur, Izzeddin
Many algorithms for aligning LLMs with human preferences assume that human preferences are binary and deterministic. However, human preferences can vary across individuals, and therefore should be represented distributionally. In this work, we introd
Externí odkaz:
http://arxiv.org/abs/2409.06691
Autor:
Hron, Jiri, Culp, Laura, Elsayed, Gamaleldin, Liu, Rosanne, Adlam, Ben, Bileschi, Maxwell, Bohnet, Bernd, Co-Reyes, JD, Fiedel, Noah, Freeman, C. Daniel, Gur, Izzeddin, Kenealy, Kathleen, Lee, Jaehoon, Liu, Peter J., Mishra, Gaurav, Mordatch, Igor, Nova, Azade, Novak, Roman, Parisi, Aaron, Pennington, Jeffrey, Rizkowsky, Alex, Simpson, Isabelle, Sedghi, Hanie, Sohl-dickstein, Jascha, Swersky, Kevin, Vikram, Sharad, Warkentin, Tris, Xiao, Lechao, Xu, Kelvin, Snoek, Jasper, Kornblith, Simon
While many capabilities of language models (LMs) improve with increased training budget, the influence of scale on hallucinations is not yet fully understood. Hallucinations come in many forms, and there is no universally accepted definition. We thus
Externí odkaz:
http://arxiv.org/abs/2408.07852
Autor:
Limonad, Lior, Fournier, Fabiana, Díaz, Juan Manuel Vera, Skarbovsky, Inna, Gur, Shlomit, Lazcano, Raquel
Publikováno v:
AIFin workshop at ECAI 2024
Large language models (LLMs) play a vital role in almost every domain in today's organizations. In the context of this work, we highlight the use of LLMs for sentiment analysis (SA) and explainability. Specifically, we contribute a novel technique to
Externí odkaz:
http://arxiv.org/abs/2407.19922
Autor:
Everett, Katie, Xiao, Lechao, Wortsman, Mitchell, Alemi, Alexander A., Novak, Roman, Liu, Peter J., Gur, Izzeddin, Sohl-Dickstein, Jascha, Kaelbling, Leslie Pack, Lee, Jaehoon, Pennington, Jeffrey
Robust and effective scaling of models from small to large width typically requires the precise adjustment of many algorithmic and architectural details, such as parameterization and optimizer choices. In this work, we propose a new perspective on pa
Externí odkaz:
http://arxiv.org/abs/2407.05872