Keyframer: Empowering Animation Design using Large Language Models
Large language models (LLMs) have the potential to impact a wide range of creative domains, as exemplified in popular text-to-image generators like DALL·E and Midjourney. However, the application of LLMs to motion-based visual design has not yet been explored and presents novels challenges such as how users might effectively describe motion in natural language. Further, many existing generative design tools lack support for iterative refinement of designs beyond prompt engineering. In this paper, we present Keyframer, a design tool that leverages the code generation capabilities of LLMs to…
We propose a general-purpose approach for improving the ability of Large Language Models (LLMs) to intelligently and adaptively gather information from a user or other external source using the framework of sequential Bayesian experimental design (BED). This enables LLMs to act as effective multi-turn conversational agents and interactively interface with…
This paper was accepted at the Learning from Time Series for Health workshop at NeurIPS 2025. Sensor data streams provide valuable information around activities and context for downstream applications, though integrating complementary information can be challenging. We show that large language models (LLMs) can be used for late fusion for…
Current Large Language Models (LLMs) are predominantly designed with English as the primary language, and even the few that are multilingual tend to exhibit strong English-centric biases. Much like speakers who might produce awkward expressions when learning a second language, LLMs often generate unnatural outputs in non-English languages, reflecting English-centric…