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pro vyhledávání: '"Padhi, Inkit"'
Large language models (LLMs) have convincing performance in a variety of downstream tasks. However, these systems are prone to generating undesirable outputs such as harmful and biased text. In order to remedy such generations, the development of gua
Externí odkaz:
http://arxiv.org/abs/2407.06323
Autor:
Hou, Yufang, Pascale, Alessandra, Carnerero-Cano, Javier, Tchrakian, Tigran, Marinescu, Radu, Daly, Elizabeth, Padhi, Inkit, Sattigeri, Prasanna
Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs), such as hallucinations and outdated information. However, it remains unclear how LLMs handle knowledge conflicts ari
Externí odkaz:
http://arxiv.org/abs/2406.13805
Large language models (LLMs) have shown to pose social and ethical risks such as generating toxic language or facilitating malicious use of hazardous knowledge. Machine unlearning is a promising approach to improve LLM safety by directly removing har
Externí odkaz:
http://arxiv.org/abs/2406.11780
Autor:
Dognin, Pierre, Rios, Jesus, Luss, Ronny, Padhi, Inkit, Riemer, Matthew D, Liu, Miao, Sattigeri, Prasanna, Nagireddy, Manish, Varshney, Kush R., Bouneffouf, Djallel
Developing value-aligned AI agents is a complex undertaking and an ongoing challenge in the field of AI. Specifically within the domain of Large Language Models (LLMs), the capability to consolidate multiple independently trained dialogue agents, eac
Externí odkaz:
http://arxiv.org/abs/2403.12805
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
Autor:
Kazemnejad, Amirhossein, Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Das, Payel, Reddy, Siva
Length generalization, the ability to generalize from small training context sizes to larger ones, is a critical challenge in the development of Transformer-based language models. Positional encoding (PE) has been identified as a major factor influen
Externí odkaz:
http://arxiv.org/abs/2305.19466
Autor:
Belgodere, Brian, Dognin, Pierre, Ivankay, Adam, Melnyk, Igor, Mroueh, Youssef, Mojsilovic, Aleksandra, Navratil, Jiri, Nitsure, Apoorva, Padhi, Inkit, Rigotti, Mattia, Ross, Jerret, Schiff, Yair, Vedpathak, Radhika, Young, Richard A.
Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate unbiased, privacy-preserving data while maintaining fidelity to the o
Externí odkaz:
http://arxiv.org/abs/2304.10819
In consequential decision-making applications, mitigating unwanted biases in machine learning models that yield systematic disadvantage to members of groups delineated by sensitive attributes such as race and gender is one key intervention to strive
Externí odkaz:
http://arxiv.org/abs/2212.06803
Autor:
Melnyk, Igor, Chenthamarakshan, Vijil, Chen, Pin-Yu, Das, Payel, Dhurandhar, Amit, Padhi, Inkit, Das, Devleena
Antibodies comprise the most versatile class of binding molecules, with numerous applications in biomedicine. Computational design of antibodies involves generating novel and diverse sequences, while maintaining structural consistency. Unique to anti
Externí odkaz:
http://arxiv.org/abs/2210.07144