GUARD-D-LLM: An LLM-Based Risk Assessment Engine for the Downstream uses of LLMs
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929388744867840 |
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| author | Narayanan, sundaraparipurnan Vishwakarma, Sandeep |
| author_facet | Narayanan, sundaraparipurnan Vishwakarma, Sandeep |
| contents | Amidst escalating concerns about the detriments inflicted by AI systems, risk management assumes paramount importance, notably for high-risk applications as demanded by the European Union AI Act. Guidelines provided by ISO and NIST aim to govern AI risk management; however, practical implementations remain scarce in scholarly works. Addressing this void, our research explores risks emanating from downstream uses of large language models (LLMs), synthesizing a taxonomy grounded in earlier research. Building upon this foundation, we introduce a novel LLM-based risk assessment engine (GUARD-D-LLM: Guided Understanding and Assessment for Risk Detection for Downstream use of LLMs) designed to pinpoint and rank threats relevant to specific use cases derived from text-based user inputs. Integrating thirty intelligent agents, this innovative approach identifies bespoke risks, gauges their severity, offers targeted suggestions for mitigation, and facilitates risk-aware development. The paper also documents the limitations of such an approach along with way forward suggestions to augment experts in such risk assessment thereby leveraging GUARD-D-LLM in identifying risks early on and enabling early mitigations. This paper and its associated code serve as a valuable resource for developers seeking to mitigate risks associated with LLM-based applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11851 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GUARD-D-LLM: An LLM-Based Risk Assessment Engine for the Downstream uses of LLMs Narayanan, sundaraparipurnan Vishwakarma, Sandeep Computers and Society Human-Computer Interaction Amidst escalating concerns about the detriments inflicted by AI systems, risk management assumes paramount importance, notably for high-risk applications as demanded by the European Union AI Act. Guidelines provided by ISO and NIST aim to govern AI risk management; however, practical implementations remain scarce in scholarly works. Addressing this void, our research explores risks emanating from downstream uses of large language models (LLMs), synthesizing a taxonomy grounded in earlier research. Building upon this foundation, we introduce a novel LLM-based risk assessment engine (GUARD-D-LLM: Guided Understanding and Assessment for Risk Detection for Downstream use of LLMs) designed to pinpoint and rank threats relevant to specific use cases derived from text-based user inputs. Integrating thirty intelligent agents, this innovative approach identifies bespoke risks, gauges their severity, offers targeted suggestions for mitigation, and facilitates risk-aware development. The paper also documents the limitations of such an approach along with way forward suggestions to augment experts in such risk assessment thereby leveraging GUARD-D-LLM in identifying risks early on and enabling early mitigations. This paper and its associated code serve as a valuable resource for developers seeking to mitigate risks associated with LLM-based applications. |
| title | GUARD-D-LLM: An LLM-Based Risk Assessment Engine for the Downstream uses of LLMs |
| topic | Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.11851 |