Zobrazeno 1 - 10
of 65 053
pro vyhledávání: '"or?a, A."'
Autor:
Reuben, Maor, Slobodin, Ortal, Elyshar, Aviad, Cohen, Idan-Chaim, Braun-Lewensohn, Orna, Cohen, Odeya, Puzis, Rami
Human-like personality traits have recently been discovered in large language models, raising the hypothesis that their (known and as yet undiscovered) biases conform with human latent psychological constructs. While large conversational models may b
Externí odkaz:
http://arxiv.org/abs/2409.19655
Consider a scenario where a harmfulness detection metric is employed by a system to filter unsafe responses generated by a Large Language Model. When analyzing individual harmful and unethical prompt-response pairs, the metric correctly classifies ea
Externí odkaz:
http://arxiv.org/abs/2408.12259
Autor:
Carelli, Mishel, Grumberg, Orna
This work proposes a novel approach for automatic verification and synthesis of infinite-state reactive programs with respect to ${CTL}^*$ specifications, based on translation to Existential Horn Clauses (EHCs). $CTL^*$ is a powerful temporal logic,
Externí odkaz:
http://arxiv.org/abs/2408.11502
Large Language Models (LLMs) are used for many tasks, including those related to coding. An important aspect of being able to utilize LLMs is the ability to assess their fitness for specific usages. The common practice is to evaluate LLMs against a s
Externí odkaz:
http://arxiv.org/abs/2407.19772
Autor:
Murphy, Orla A., Schulz, Juliana
Multi-dimensional data frequently occur in many different fields, including risk management, insurance, biology, environmental sciences, and many more. In analyzing multivariate data, it is imperative that the underlying modelling assumptions adequat
Externí odkaz:
http://arxiv.org/abs/2407.05896
We introduce a novel LLM based solution design approach that utilizes combinatorial optimization and sampling. Specifically, a set of factors that influence the quality of the solution are identified. They typically include factors that represent pro
Externí odkaz:
http://arxiv.org/abs/2405.13020
Recent advances in generative artificial intelligence have enabled the creation of high-quality synthetic data that closely mimics real-world data. This paper explores the adaptation of the Stable Diffusion 2.0 model for generating synthetic datasets
Externí odkaz:
http://arxiv.org/abs/2405.02698
Efficient optimization of operating room (OR) activity poses a significant challenge for hospital managers due to the complex and risky nature of the environment. The traditional "one size fits all" approach to OR scheduling is no longer practical, a
Externí odkaz:
http://arxiv.org/abs/2403.09791
Autor:
Achintalwar, Swapnaja, Garcia, Adriana Alvarado, Anaby-Tavor, Ateret, Baldini, Ioana, Berger, Sara E., Bhattacharjee, Bishwaranjan, Bouneffouf, Djallel, Chaudhury, Subhajit, Chen, Pin-Yu, Chiazor, Lamogha, Daly, Elizabeth M., DB, Kirushikesh, de Paula, Rogério Abreu, Dognin, Pierre, Farchi, Eitan, Ghosh, Soumya, Hind, Michael, Horesh, Raya, Kour, George, Lee, Ja Young, Madaan, Nishtha, Mehta, Sameep, Miehling, Erik, Murugesan, Keerthiram, Nagireddy, Manish, Padhi, Inkit, Piorkowski, David, Rawat, Ambrish, Raz, Orna, Sattigeri, Prasanna, Strobelt, Hendrik, Swaminathan, Sarathkrishna, Tillmann, Christoph, Trivedi, Aashka, Varshney, Kush R., Wei, Dennis, Witherspooon, Shalisha, Zalmanovici, Marcel
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (training cost, API access, data availability, etc.), it may not always be
Externí odkaz:
http://arxiv.org/abs/2403.06009
Autor:
Achintalwar, Swapnaja, Baldini, Ioana, Bouneffouf, Djallel, Byamugisha, Joan, Chang, Maria, Dognin, Pierre, Farchi, Eitan, Makondo, Ndivhuwo, Mojsilovic, Aleksandra, Nagireddy, Manish, Ramamurthy, Karthikeyan Natesan, Padhi, Inkit, Raz, Orna, Rios, Jesus, Sattigeri, Prasanna, Singh, Moninder, Thwala, Siphiwe, Uceda-Sosa, Rosario A., Varshney, Kush R.
The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In contrast, in this article, we present an approach and architecture that
Externí odkaz:
http://arxiv.org/abs/2403.09704