Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations

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 feasible to impose direct safety constraints on a...

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Veröffentlicht in:	arXiv.org 2024-08
Hauptverfasser:	Achintalwar, Swapnaja, Adriana Alvarado Garcia, Anaby-Tavor, Ateret, Baldini, Ioana, Berger, Sara E, Bhattacharjee, Bishwaranjan, Bouneffouf, Djallel, Chaudhury, Subhajit, Pin-Yu, Chen, Chiazor, Lamogha, Daly, Elizabeth M, Kirushikesh, D B, Rogério Abreu de Paula, Dognin, Pierre, Farchi, Eitan, Ghosh, Soumya, Hind, Michael, Horesh, Raya, Kour, George, Ja Young Lee, 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
Format:	Artikel
Sprache:	eng
Schlagworte:	Constraint modelling Detectors Large language models
Online-Zugang:	Volltext
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