Papers by Rose Yu
CaTS-Bench: Can Language Models Describe Time Series? (2026.findings-acl)
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| Challenge: | Existing time series captioning benchmarks rely on fully synthetic or generic captions . authors propose a pipeline for generating high-fidelity synthetic captions, which is validated . |
| Approach: | They propose a benchmark for Context-aware Time Series reasoning across 11 diverse domains . they evaluate leading Vision-Language Models on their benchmark . |
| Outcome: | The proposed benchmark evaluates 1746 human-rewritten captions and shows they perform better than open-source models. |
MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization (2024.findings-emnlp)
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| Challenge: | Current RL-based prompt tuning techniques focus on maximizing the average of reward functions, which does not necessarily lead to prompts that achieve balance across rewards. |
| Approach: | They compare RL-based discrete prompt optimization techniques with a new set of target language models to find prompts that maximize an average of rewards. |
| Outcome: | The proposed methods perform better on two NLP tasks, style transfer and machine translation, and achieve a better balance of all rewards. |