CALICO: Conversational Agent Localization via Synthetic Data Generation
We present CALICO, a method to fine-tune Large Language Models (LLMs) to localize conversational agent training data from one language to another. For slots (named entities), CALICO supports three operations: verbatim copy, literal translation, and localization, i.e. generating slot values more appropriate in the target language, such as city and airport names located in countries where the language is spoken. Furthermore, we design an iterative filtering mechanism to discard noisy generated samples, which we show boosts the performance of the downstream conversational agent. To prove the effectiveness of CALICO, we build and release a new human-localized (HL) version of the MultiATIS++ travel information test set in 8 languages. Compared to the original human-translated (HT) version of the test set, we show that our new HL version is more challenging. We also show that CALICO out-performs state-of-the-art LINGUIST (which relies on literal slot translation out of context) both on the HT case, where CALICO generates more accurate slot translations, and on the HL case, where CALICO generates localized slots which are closer to the HL test set.
Code (0)
등록된 구현이 없습니다.
Tasks
Synthetic Data GenerationTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirability of self-supervised representation lea…
3D Object DetectionAutonomous DrivingObjectobject-detection+2Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
This paper describes CALICO, a method for multi-camera calibration suitable for challenging contexts: stationary and mobile multi-camera systems, cameras without overlapping fields of view, and non-synchronized cameras. …
Camera CalibrationObject ReconstructionCALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models
Recent advances in Large Vision-Language Models (LVLMs) have sparked significant progress in general-purpose vision tasks through visual instruction tuning. While some works have demonstrated the capability of LVLMs to g…
Image SegmentationSegmentationSemantic SegmentationConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach
In this paper, we present ConvoGen: an innovative framework for generating synthetic conversational data using multi-agent systems. Our method leverages few-shot learning and introduces iterative sampling from a dynamica…
Conversation SummarizationFew-Shot Learningintent-classificationIntent ClassificationCALICO: Confident Active Learning with Integrated Calibration
The growing use of deep learning in safety-critical applications, such as medical imaging, has raised concerns about limited labeled data, where this demand is amplified as model complexity increases, posing hurdles for …
Active Learning