Libiao Peng


2024

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CharacterGLM: Customizing Social Characters with Large Language Models
Jinfeng Zhou | Zhuang Chen | Dazhen Wan | Bosi Wen | Yi Song | Jifan Yu | Yongkang Huang | Pei Ke | Guanqun Bi | Libiao Peng | JiaMing Yang | Xiyao Xiao | Sahand Sabour | Xiaohan Zhang | Wenjing Hou | Yijia Zhang | Yuxiao Dong | Hongning Wang | Jie Tang | Minlie Huang
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track

Character-based dialogue (CharacterDial) has become essential in the industry (e.g., Character.AI), enabling users to freely customize social characters for social interactions. However, the generalizability and adaptability across various conversational scenarios inherent in customizing social characters still lack public industrial solutions. To address these challenges, by dissecting well-rounded social characters composed of both inherent social profiles and external social behaviors, we manually collect a large-scale Chinese corpus featuring characters with diverse categories and behaviors, and develop CharacterGLM models alongside well-designed refinement methods. Extensive experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparably to GPT-4. We will release our data and models for local development and deployment.

2022

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CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
Chujie Zheng | Jinfeng Zhou | Yinhe Zheng | Libiao Peng | Zhen Guo | Wenquan Wu | Zheng-Yu Niu | Hua Wu | Minlie Huang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Dialogue contradiction is a critical issue in open-domain dialogue systems. The contextualization nature of conversations makes dialogue contradiction detection rather challenging. In this work, we propose a benchmark for Contradiction Detection in Chinese Conversations, namely CDConv. It contains 12K multi-turn conversations annotated with three typical contradiction categories: Intra-sentence Contradiction, Role Confusion, and History Contradiction. To efficiently construct the CDConv conversations, we devise a series of methods for automatic conversation generation, which simulate common user behaviors that trigger chatbots to make contradictions. We conduct careful manual quality screening of the constructed conversations and show that state-of-the-art Chinese chatbots can be easily goaded into making contradictions. Experiments on CDConv show that properly modeling contextual information is critical for dialogue contradiction detection, but there are still unresolved challenges that require future research.