Papers by Kun Peng
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2 (2021.acl-srw)
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| Challenge: | Experimental results show that pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
| Approach: | They conduct experiments on an English essay dataset using Chinese-GPT2 . they find that the model can generate better continuations by learning to generate the in the fine-tuning stage. |
| Outcome: | The pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)
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Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu
| Challenge: | Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets. |
| Approach: | They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity. |
| Outcome: | Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets. |
C-World: A Computer Use Agent Environment Creator (2026.acl-long)
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Ziqiao Xi, Shuang Liang, Qi Liu, Jiaqing Zhang, Letian Peng, Fang Nan, Meshal Nayim, Tianhui Zhang, Rishika Mundada, Lianhui Qin, Biwei Huang, Kun Zhou
| Challenge: | C-World enables users to build agent environments on demand. |
| Approach: | They propose a system that enables users to build agent environments on demand. |
| Outcome: | The proposed system outperforms baselines on 119k samples and achieves Spearman = 0.883 ranking correlation with real execution. |
Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)
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| Challenge: | Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications. |
| Approach: | They propose a prototype-based emotion transfer framework that can be used in real-world applications. |
| Outcome: | The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation. |
TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice (2026.acl-long)
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| Challenge: | Large Language Models excel in general domains but lack real-world practical capabilities. |
| Approach: | They propose a benchmark for Chinese taxation practice that combines 10 traditional application tasks with 3 pioneering real-world scenarios. |
| Outcome: | The proposed benchmark combines 10 traditional tasks with 3 pioneering real-world scenarios. |
LLM-Enhanced Query Generation and Retrieval Preservation for Task-Oriented Dialogue (2025.findings-acl)
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| Challenge: | Existing knowledge retrieval methods for task-oriented dialogues are limited by data scarcity and lack of data to annotate. |
| Approach: | They propose an LLM-enhanced model of query-guided knowledge retrieval for task-oriented dialogue . they propose to select the most relevant knowledge from retrieved top-K records and incorporate them as prompts to guide a generator in response generation. |
| Outcome: | The proposed model outperforms state-of-the-art in three benchmarks on three standard benchmarks. |
Deriving Character Logic from Storyline as Codified Decision Trees (2026.acl-long)
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| Challenge: | Existing behavioral profiles are unstructured, weakly validated, and unusable . existing models are weakly valid, leading to brittle agent behavior . Using codified decision trees, we show that CDT outperforms previous methods . |
| Approach: | They propose a data-driven framework that induces an executable decision structure from narrative data. |
| Outcome: | The proposed framework outperforms human-written profiles and prior profiles on multiple benchmarks. |
SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting (2023.findings-emnlp)
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| Challenge: | Experimental results show that SGP-TOD provides state-of-the-art zero-shot performance . prevailing approach for creating task bots is to fine-tune pre-trained language models . |
| Approach: | They propose a Schema-Guided Prompting for building Task-Oriented Dialog systems . they use predefined task schema and dialog policy to instruct fixed LLMs to generate appropriate responses . |
| Outcome: | The proposed system outperforms few-shot approaches on multiwoz, RADDLE, and STAR datasets. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
CORES: Code-Oriented Reasoning for Complex Text-to-SQL and Generalizable TableQA (2026.findings-acl)
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Meng Zhang, Ruochun Jin, Yuanxi Peng, Wenjing Yang, Haotian Wang, Liting Sun, Kun Hu, Silin Yang, Zhang Ke-di
| Challenge: | Text-to-SQL models struggle with complex analytical tasks such as generating simple SQL queries. |
| Approach: | They propose a text-to-sql model that leverages Python as a procedural reasoning pivot to enhance both complex SQL generation and tabular reasoning. |
| Outcome: | The proposed model outperforms baseline models on six text-to-SQL benchmarks by 6.44% on average while maintaining good capability on three tableQA benchmarks. |