Summary of Detectors For Safe and Reliable Llms: Implementations, Uses, and Limitations, by Swapnaja Achintalwar et al.
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
by Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Kirushikesh DB, Rogério Abreu de Paula, Pierre Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Nishtha Madaan, Sameep Mehta, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspooon, Marcel Zalmanovici
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an alternative solution to addressing risks in large language models (LLMs), such as non-faithful output, biased, and toxic generations. It presents a library of detectors, compact classification models that label various harms, and discusses multiple uses for these detectors, including acting as guardrails for AI governance. The authors also highlight the challenges they faced during detector development and their future plans to improve their reliability and scope. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to create a reliable solution to address risks in large language models. It develops a library of compact detectors that can identify various harms, making it an efficient alternative to imposing direct safety constraints on deployed models. The authors show how these detectors can be used as guardrails for AI governance and discuss the challenges they faced during their development. |
Keywords
* Artificial intelligence * Classification
