Papers by Michal Shlapentokh-Rothman

2 papers
WebWISE: Unlocking Web Interface Control for LLMs via Sequential Exploration (2024.findings-naacl)

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Challenge: Prior work to control software has used reinforcement learning (RL), requiring many demonstrations and trials to learn simple interaction tasks.
Approach: They propose a Large Language Model to automatically perform web software tasks using click, scroll, and text in- put operations using filtered Document Object Models as observations.
Outcome: The proposed method performs better on the MiniWob++ benchmark with only one in-context example.
Visual Program Distillation with Template-Based Augmentation (2025.findings-emnlp)

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Challenge: Adapting visual programming to specialized tasks or domains remains challenging due to high annotation and inference costs.
Approach: They propose a low-cost visual program distillation method that can be used for models with at most 1 billion parameters and requires no human-generated program annotations.
Outcome: The proposed method can generate high-quality visual programs with no human-generated annotations with a relatively small amount of question/answer data.

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