Papers by Zilong Zheng
Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs (2025.emnlp-main)
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| Challenge: | Context faithfulness is essential for reliable reasoning in context-dependent scenarios. |
| Approach: | They propose a method that identifies and fine-tunes context-faithful experts . they propose 'context-faither fine- tuning' which selectively fine- tunes them . |
| Outcome: | The proposed method identifies experts with specialization in context utilization and improves context grounding. |
SHARP: Search-Based Adversarial Attack for Structured Prediction (2022.findings-naacl)
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| Challenge: | SHARP is a new attack method for structured prediction models that solves several challenges. |
| Approach: | They propose a black-box adversarial attack method that uses a search-based optimization problem to attack adversarials. |
| Outcome: | The proposed method performs more potent attack than pioneer arts on two structured prediction tasks. |
v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound (2026.acl-long)
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Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Song-Chun Zhu, Bo Zhao, Zilong Zheng
| Challenge: | Humor enriches our daily lives and appears in many forms, from jokes and cartoons to comedies and viral videos. |
| Approach: | They introduce a video humor understanding benchmark to test their ability to understand humor from visual cues. |
| Outcome: | The proposed video humor understanding benchmark is based on a collection of short videos . it features rich annotations and a study of environmental sound that can enhance humor . |
Adaptive Preference Optimization with Uncertainty-aware Utility Anchor (2025.findings-emnlp)
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| Challenge: | Offline preference optimization methods are efficient for large language models (LLMs) alignment. |
| Approach: | They propose an offline preference optimization framework that estimates uncertainties from preference data . the method enables training even in scenarios where the data is unpaired . |
| Outcome: | The proposed method enables training even in scenarios where the data is unpaired . |
LangSuit·E: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have shown inspiring achievements in constructing autonomous agents that rely on language descriptions as inputs. |
| Approach: | They propose a flexible and simulation-free testbed that simulates 6 representative embodied tasks in textual embodies. |
| Outcome: | The proposed testbed offers adaptability to diverse environments without multiple simulation engines and allows easy customization of communication and action strategies. |
GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning (2021.findings-acl)
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| Challenge: | a grammar-based dialogue dataset, GRICE, is designed to bring implicature into pragmatic reasoning in conversations . implicature recovery is a key component of open-ended dialogue reasoning . |
| Approach: | They propose a grammar-based dialogue dataset to bring implicature into pragmatic reasoning . they use a hierarchical grammar model to generate the entire dataset . |
| Outcome: | The proposed model shows a significant performance gap between baseline methods and human models . the model shows an overall performance boost in conversational reasoning . |
ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection (2025.findings-acl)
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Jiaqi Li, Xinyi Dong, Yang Liu, Zhizhuo Yang, Quansen Wang, Xiaobo Wang, Song-Chun Zhu, Zixia Jia, Zilong Zheng
| Challenge: | ReflectEvo-460k is a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks. |
| Approach: | They propose a pipeline that iteratively generates self-reflection for self-training and a large-scale reflection dataset with broadened instructions and diverse multi-domain tasks. |
| Outcome: | The proposed pipeline improves Llama-3 reasoning ability by up to 71.2% and Mistral by upto 44.4%. |
MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents (2026.findings-eacl)
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| Challenge: | Existing document-level information extraction systems operate at the sentence level or within narrow domains due to annotation constraints. |
| Approach: | They propose a large-scale universal dataset for multi-domain, document-level information extraction from long texts. |
| Outcome: | The proposed dataset integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains. |
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels (2024.acl-long)
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| Challenge: | Existing supervised learning methods rely on human annotations, but multi-label tasks pose challenges due to the specific domain knowledge and large class sets. |
| Approach: | They propose a framework that can be used to annotate a subset of positive classes from a multi-label dataset. |
| Outcome: | The proposed framework is generalized and effective across multiple tasks. |
Varying Sentence Representations via Condition-Specified Routers (2024.emnlp-main)
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| Challenge: | Existing sentences cannot account for different aspects of semantic similarity between two sentences. |
| Approach: | They propose a transformer-style framework that generates conditioned sentences . they propose 'conditional' STS, which measures similarity between two sentences based on condition sentences - a task that requires a sentence embedding model capable of generating distinct representations for the same sentence under different conditions. |
| Outcome: | The proposed framework is superior to existing models on two condition sentences . it can generate conditioned sentences while maintaining model parameters and computational efficiency . |
Are the Values of LLMs Structurally Aligned with Humans? A Causal Perspective (2025.findings-acl)
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Yipeng Kang, Junqi Wang, Yexin Li, Mengmeng Wang, Wenming Tu, Quansen Wang, Hengli Li, Tingjun Wu, Xue Feng, Fangwei Zhong, Zilong Zheng
| Challenge: | Current approaches to value alignment focus on a few core values, such as helpfulness, harmlessness, and honesty. |
| Approach: | They propose to use latent causal value graphs to guide two lightweight value-steering methods . role-based prompting and sparse autoencoder (SAE) steering are also used . |
| Outcome: | Experiments on Gemma-2B-IT and Llama3-8B- IT show that the proposed methods are effective and controllable. |
MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark (2024.findings-acl)
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Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, Kai Chen
| Challenge: | Recent advances in large language models have showcased significant improvements in mathematics, but traditional benchmarks like GSM8k offer a unidimensional perspective. |
| Approach: | MathBench is a benchmark that rigorously assesses the mathematical capabilities of large language models. |
| Outcome: | MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills. |
Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)
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Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
| Challenge: | Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios . |
| Approach: | They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text . |
| Outcome: | The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task . |
MindAgent: Emergent Gaming Interaction (2024.findings-naacl)
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Ran Gong, Qiuyuan Huang, Xiaojian Ma, Yusuke Noda, Zane Durante, Zilong Zheng, Demetri Terzopoulos, Li Fei-Fei, Jianfeng Gao, Hoi Vo
| Challenge: | Large foundation models (LFMs) can perform complex scheduling in a multi-agent system and can coordinate agents to complete complex tasks that require extensive collaboration. |
| Approach: | They propose a gaming-based infrastructure that evaluates LFMs' planning and coordination capabilities in the context of gaming interaction. |
| Outcome: | The proposed infrastructure can be deployed in a customized VR version of Cuisineworld and adapted in the “Minecraft” domain. |
Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field (2023.acl-long)
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| Challenge: | Existing approaches to joint Information Extraction (IE) neglect cross-instance or cross-task dependencies. |
| Approach: | They propose a joint IE framework that formulates joint 'conditional random field' to model cross-instance interactions . they incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method . |
| Outcome: | The proposed approach improves on three IE tasks compared with baseline and prior work. |
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)
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Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo
| Challenge: | Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution. |
| Approach: | They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work. |
Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge (2024.emnlp-main)
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| Challenge: | despite advances in multimodal large language models, the challenge of interpreting long-form videos remains a challenge . despite advancements in video-language benchmarks, the inefficiency in temporal grounding and limited pre-trained context window size remains . |
| Approach: | They propose a framework that bootstraps MLLMs with advanced temporal grounding capabilities and broadens their contextual scope. |
| Outcome: | The proposed framework significantly enhances the temporal capabilities of existing MLLMs. |
Rethinking Dictionaries and Glyphs for Chinese Language Pre-training (2023.findings-acl)
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| Challenge: | Large-scale pre-trained language models (PLMs) such as BERT and GPT have revolutionized various research fields in natural language processing (NLP) |
| Approach: | They propose a new learning paradigm that enhances the semantics understanding ability of Chinese PLMs with dictionary knowledge and structure of Chinese characters. |
| Outcome: | The proposed model improves on both modern Chinese understanding benchmark CLUE and ancient Chinese understanding. |
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (2023.emnlp-main)
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| Challenge: | Document-level Relation Extraction (DocRE) is a task that aims to extract relations from a long context. |
| Approach: | They propose an automated annotation method that integrates an LLM and a natural language inference module to generate relation triples. |
| Outcome: | The proposed method can extract relations from document-level relation datasets with minimal human effort. |
Boosting LLM Agents with Recursive Contemplation for Effective Deception Handling (2024.findings-acl)
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Shenzhi Wang, Chang Liu, Zilong Zheng, Siyuan Qi, Shuo Chen, Qisen Yang, Andrew Zhao, Chaofei Wang, Shiji Song, Gao Huang
| Challenge: | Recent advances in large language models (LLMs) have led to significant success in using LLMs as agents. |
| Approach: | They propose a cognitive framework that incorporates first-order and second-order perspective transitions into LLMs to enhance their ability to identify and counteract deceptive information. |
| Outcome: | The proposed framework enhances LLMs’ ability to identify and counteract deceptive information without extra fine-tuning and data. |
VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions (2023.acl-long)
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| Challenge: | Existing benchmarks for video-grounded dialogues neglect the intrinsic attributes of multimodal dialogues, such as scene and topic transitions. |
| Approach: | They propose to use a large scale video-grounded scene&topic AwaRe dialogue dataset to study video-based dialogue understanding. |
| Outcome: | The proposed dataset shows that multimodal information and segments are important in video-grounded dialogue understanding and generation. |
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)
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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo
| Challenge: | Existing approaches focus on textual data and voting records to induce political actors' stances. |
| Approach: | They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances. |
| Outcome: | The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection. |
Look Both Ways and No Sink: Converting LLMs into Text Encoders without Training (2025.acl-long)
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| Challenge: | Existing methods for converting large language models into powerful text encoders require extensive training on large datasets. |
| Approach: | They propose a training-free approach that enables bidirectional attention and suppresses the attention sink phenomenon, resulting in superior performance. |
| Outcome: | The proposed approach enables bidirectional attention and suppresses the attention sink phenomenon, resulting in superior performance. |
Reinforced Query Reasoners for Reasoning-intensive Retrieval Tasks (2025.emnlp-main)
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| Challenge: | Existing methods for retrieval of information excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks. |
| Approach: | They propose a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval. |
| Outcome: | The proposed model outperforms existing models on a BRIGHT benchmark with BM25 retrievers. |