PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation
with GPT-4 in Cloud Incident Root Cause Analysis
- URL: http://arxiv.org/abs/2309.05833v3
- Date: Fri, 29 Sep 2023 16:25:16 GMT
- Title: PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation
with GPT-4 in Cloud Incident Root Cause Analysis
- Authors: Dylan Zhang, Xuchao Zhang, Chetan Bansal, Pedro Las-Casas, Rodrigo
Fonseca, Saravan Rajmohan
- Abstract summary: Large language models (LLMs) are used to help humans identify the root causes of cloud incidents.
We propose to perform confidence estimation for the predictions to help on-call engineers make decisions on whether to adopt the model prediction.
We show that our method is able to produce calibrated confidence estimates for predicted root causes, validate the usefulness of retrieved historical data and the prompting strategy.
- Score: 17.362895895214344
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Major cloud providers have employed advanced AI-based solutions like large
language models to aid humans in identifying the root causes of cloud
incidents. Despite the growing prevalence of AI-driven assistants in the root
cause analysis process, their effectiveness in assisting on-call engineers is
constrained by low accuracy due to the intrinsic difficulty of the task, a
propensity for LLM-based approaches to hallucinate, and difficulties in
distinguishing these well-disguised hallucinations. To address this challenge,
we propose to perform confidence estimation for the predictions to help on-call
engineers make decisions on whether to adopt the model prediction. Considering
the black-box nature of many LLM-based root cause predictors, fine-tuning or
temperature-scaling-based approaches are inapplicable. We therefore design an
innovative confidence estimation framework based on prompting
retrieval-augmented large language models (LLMs) that demand a minimal amount
of information from the root cause predictor. This approach consists of two
scoring phases: the LLM-based confidence estimator first evaluates its
confidence in making judgments in the face of the current incident that
reflects its ``grounded-ness" level in reference data, then rates the root
cause prediction based on historical references. An optimization step combines
these two scores for a final confidence assignment. We show that our method is
able to produce calibrated confidence estimates for predicted root causes,
validate the usefulness of retrieved historical data and the prompting strategy
as well as the generalizability across different root cause prediction models.
Our study takes an important move towards reliably and effectively embedding
LLMs into cloud incident management systems.
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