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Karin de Langis

7 accepted papers

2025

How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs

ACL 2025long

Large language models (LLMs) exihibit increasingly sophisticated linguistic capabilities, yet the extent to which these behaviors reflect human-like cognition versus advanced pattern recognition remains an open question.In this study, we investigate how LLMs process the temporal meaning of linguisti…

Cited by 0SourcePDFScholar
2024

Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation

EMNLP 2024main

Textual style expresses a diverse set of information, including interpersonal dynamics (e.g., formality) and the author’s emotions or attitudes (e.g., disgust). An open question is how language models can be explicitly controlled so that they weave together target styles when generating text: for ex…

2023

infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information

ACL 2023long

The success of NLP systems often relies on the availability of large, high-quality datasets. However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy. Several methods for characterizing datasets based on model-driven meta-information (e.g., mode…

2021

Semantically-Aware Strategies for Stereo-Visual Robotic Obstacle Avoidance

ICRA 2021poster

Mobile robots in unstructured, mapless environments must rely on an obstacle avoidance module to navigate safely. The standard avoidance techniques estimate the locations of obstacles with respect to the robot but are unaware of the obstacles’ identities. Consequently, the robot cannot take advantag…

Cited by 9SourceScholar
2021

Towards Robust Visual Diver Detection Onboard Autonomous Underwater Robots: Assessing the Effects of Models and Data

IROS 2021

Deep neural networks are the leading solution to the object detection problem. However, challenges arise when applying these networks to the kind of real-time, first-person video data that a robotic platform must process: specifically, detections may not be consistent from frame to frame, and object

Cited by 7SourceScholar
2021

Towards Robust Visual Diver Detection Onboard Autonomous Underwater Robots: Assessing the Effects of Models and Data1

IROS 2021poster

Deep neural networks are the leading solution to the object detection problem. However, challenges arise when applying these networks to the kind of real-time, first-person video data that a robotic platform must process: specifically, detections may not be consistent from frame to frame, and object…

Cited by 6SourceScholar
2020

Realtime Multi-Diver Tracking and Re-identification for Underwater Human-Robot Collaboration

ICRA 2020poster

Autonomous underwater robots working with teams of human divers may need to distinguish between different divers, e.g., to recognize a lead diver or to follow a specific team member. This paper describes a technique that enables autonomous underwater robots to track divers in real time as well as to…

Cited by 26SourceScholar