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Kiran Kate

4 accepted papers

2025

NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls

EMNLP 2025

The resurgence of autonomous agents built using large language models (LLMs) to solve complex real-world tasks has brought increased focus on LLMs’ fundamental ability of tool or function calling. At the core of these agents, an LLM must plan, execute, and respond using external tools, APIs, and cus

2023

CodeStylist: A System for Performing Code Style Transfer Using Neural Networks

AAAI 2023technical

Code style refers to attributes of computer programs that affect their readability, maintainability, and performance. Enterprises consider code style as important and enforce style requirements during code commits. Tools that assist in coding style compliance and transformations are highly valuable.…

Cited by 5SourcePDFScholar
2023

Towards large language model-based personal agents in the enterprise: Current trends and open problems

EMNLP 2023long findings

There is an emerging trend to use large language models (LLMs) to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal. This functionality could, among other use cases, be used to build personal assistants for knowledge workers. While there are impressive…

Cited by 0SourceScholar
2021

Pipeline Combinators for Gradual AutoML

NeurIPS 2021poster

Automated machine learning (AutoML) can make data scientists more productive. But if machine learning is totally automated, that leaves no room for data scientists to apply their intuition. Hence, data scientists often prefer not total but gradual automation, where they control certain choices and…

Cited by 30SourcePDFScholar