Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their further evolution is often hampered by the scarcity of high-quality training data and the heavy reliance of traditional methods on expert-labeled data.This reliance sets a ceiling on LLM performance and is particularly challenging in low data resource scenarios where extensive supervision is unavailable.To address this issue, we propose a novel paradigm named LANCE (LANguage models as Continuous self-Evolving data engineers) that enables LLMs to train themselves by autonomously generating, cleaning, reviewing, and annotating data with preference information.Our approach demonstrates that LLMs can serve as continuous self-evolving data engineers, significantly reducing the time and cost of post-training data construction.Through iterative fine-tuning on Qwen2 series models, we validate the effectiveness of LANCE across various tasks, showing that it can maintain high-quality data generation and continuously improve model performance.Across multiple benchmark dimensions, LANCE results in an average score enhancement of 3.64 for Qwen2-7B and 1.75 for Qwen2-7B-Instruct.This autonomous data construction paradigm not only lessens reliance on human experts or external models but also ensures data aligns with human preferences, offering a scalable path for LLM self-improvement, especially in contexts with limited supervisory data.

The detection of telecom fraud faces significant challenges due to the lack of high-quality multimodal training data that integrates audio signals with reasoning-oriented textual analysis. To address this gap, we present TeleAntiFraud-28k, the first open-source audio-text slow-thinking dataset specifically designed for automated telecom fraud analysis. Our dataset is constructed through three strategies: (1) Privacy-preserved text-truth sample generation using automatically speech recognition-transcribed call recordings (with anonymized original audio), ensuring real-world consistency through text-to-speech model regeneration; (2) Semantic enhancement via large language model based self-instruction sampling on authentic ASR outputs to expand scenario coverage; (3) Multi-agent adversarial synthesis, which simulates emerging fraud tactics through predefined communication scenarios and fraud typologies, enriches the conversation samples. The generated dataset contains 28,511 rigorously processed audio-text pairs with a total audio duration of more than 307 hours, complete with detailed annotations for fraud reasoning. The dataset is divided into three tasks: scenario classification, fraud detection, fraud type classification. Furthermore, we construct TeleAntiFraud-Bench, a standardized evaluation benchmark comprising proportionally sampled instances from TeleAntiFraud-28k, to facilitate systematic testing of model performance, reasoning capabilities, and thought processes on telecom fraud detection tasks. We also contribute a supervised fine-tuning model based on Qwen2-Audio, trained on the TeleAntiFraud-28k training set, while open-sourcing the data processing framework to enable community-driven dataset expansion. This work establishes a foundational framework for multimodal anti-fraud research while addressing critical challenges in data privacy and scenario diversity. The code of this paper is publicly available at https://github.com/JimmyMa99/TeleAntiFraud.

Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, SWangLing SWangLing, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Responding with multimodal content has been recognized as one of the essential functionalities of intelligent conversational agents. However, existing research on multimodal dialogues primarily focuses on two topics: (1) textual response generation that ground the conversation on a given image; and (2) visual response selection based on the dialogue context. In light of the aforementioned gap, we propose mulTImodal GEnerator for dialogue Response (TIGER), a unified generative model framework for multimodal dialogue response generation. Through extensive experiments, TIGER has demonstrated new state-of-the-art results, providing users with an enhanced conversational experience. A multimodal dialogue system based on TIGER is available at https://github.com/friedrichor/TIGER. A video demonstrating the system is available at https://www.youtube.com/watch?v=Kd0CMwDs8Rk.