3 accepted papers
We build a comprehensive online evaluation benchmark for language-conditioned multi-step task execution on mobile interfaces. Our benchmark strives to evaluate the multi-step planning, reasoning, and visual grounding capabilities of agents, using mobile user interfaces as a concrete testbed. To buil…
Automatic summarization methods are efficient but can suffer from low quality. In comparison, manual summarization is expensive but produces higher quality. Can humans and AI collaborate to improve summarization performance? In similar text generation tasks (e.g., machine translation), human-AI coll…
Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans’ roles, experience, and needs when interacting with or being assisted by AI. From a human-centered perspective, we map the design opp…