Incident management for large cloud services is a complex and tedious process and requires significant amount of manual efforts from on-call engineers (OCEs). OCEs typically leverage data from different stages of the software development lifecycle [SDLC] (e.g., codes, configuration, monitor data, service properties, service dependencies,…
In-hand tool manipulation is an operation that not only manipulates a tool within the hand (i.e., in-hand manipulation) but also achieves a grasp suitable for a task after the manipulation. This study aims to achieve an in-hand tool manipulation skill through deep reinforcement learning. The…
Large language models (LLMs) have recently seen widespread adoption, in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting enormous investments by their owners. Moreover, the high cost of cloud-based deployment has driven interest towards deployment to edge devices,…
Root Cause Analysis (RCA) plays a pivotal role in the incident diagnosis process for cloud services, requiring on-call engineers to identify the primary issues and implement corrective actions to prevent future recurrences. Improving the incident RCA process is vital for minimizing service downtime, customer impact…
Information retrieval (IR) has a large literature on evaluation, dating back decades and forming a central part of the research culture. The largest proportion of this literature discusses techniques to turn a sequence of relevance labels into a single number, reflecting the system's performance: precision…
Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4eval) has recently attracted considerable attention in multiple research communities. For instance, LLM4eval models have been studied in the context of…
Much of the evaluation and tuning of a search system relies on relevance labels—annotations that say whether a document is useful for a given search and searcher. Ideally these come from real searchers, but it is hard to collect this data at scale, so typical…
The rapid progress in Large Language Models (LLMs) poses potential risks such as generating unethical content. Assessing LLMs' values can help expose their misalignment, but relies on reference-free evaluators, e.g., fine-tuned LLMs or close-source ones like GPT-4, to identify values reflected in generated responses. Nevertheless,…
AI for Domains (AID) Our mission The M365 Research organization's AI for Domains (AID) team is an applied research group focused on leveraging cutting-edge research to extend the capabilities, efficiency, reliability, and reduce risks associated with Copilot. We prioritize user privacy and confidentiality while unlocking…
Much of recent progress for natural language generation (NLG) has been in the context of English and, in general, high resource languages, however, Indian languages have yet to see similar paradigm shifts despite their speaking population comprising about a fifth of the world's population. Two…
A public framework for time-series forecasting with spiking neural networks (SNNs).
Software engineering activities such as package migration, fixing error reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks. Recent tools like GitHub…
