4 citations · 6 across the 4 of their papers we have counts for
5 papers
Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning
Zhijun Zhang, Qianlong Wang, Keyang Ding +5
Argument Mining (AM) is fundamentally constrained by the scarcity of high-quality structure-annotated datasets. While LLMs have shown promise in synthetic data generation, producin…
CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation
Jingqian Zhao, Bingbing Wang, Geng Tu +5
Data contamination poses a significant challenge to the fairness of LLM evaluations in natural language processing tasks by inadvertently exposing models to test data during traini…
Towards Real-World Stickers Use: A New Dataset for Multi-Tag Sticker Recognition
Bingbing Wang, Bin Liang, Chun-Mei Feng +6
In real-world conversations, the diversity and ambiguity of stickers often lead to varied interpretations based on the context, necessitating the requirement for comprehensively un…
Reply with Sticker: New Dataset and Model for Sticker Retrieval
Bin Liang, Bingbing Wang, Zhixin Bai +6
Using stickers in online chatting is very prevalent on social media platforms, where the stickers used in the conversation can express someone's intention/emotion/attitude in a viv…
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
Hongru Wang, Rui Wang, Fei Mi +5
Large Language Models (LLMs), such as \texttt{ChatGPT}, greatly empower dialogue systems with strong language understanding and generation capabilities. However, most of the previo…