Ruixuan Xiao


2024

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On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
Lin Long | Rui Wang | Ruixuan Xiao | Junbo Zhao | Xiao Ding | Gang Chen | Haobo Wang
Findings of the Association for Computational Linguistics ACL 2024

Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation.

2023

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FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models
Ruixuan Xiao | Yiwen Dong | Junbo Zhao | Runze Wu | Minmin Lin | Gang Chen | Haobo Wang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks. While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention. It is still underexplored how to reduce the annotation cost in the LLMs era. To bridge this, we revolutionize traditional active learning and propose an innovative collaborative learning framework FreeAL to interactively distill and filter the task-specific knowledge from LLMs. During collaborative training, an LLM serves as an active annotator inculcating its coarse-grained knowledge, while a downstream SLM is incurred as a student to filter out high-quality in-context samples to feedback LLM for the subsequent label refinery. Extensive experiments on eight benchmark datasets demonstrate that FreeAL largely enhances the zero-shot performances for both SLM and LLM without any human supervision.