Papers by Jie Gong
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge distillation methods rely on intermediate layer features and golden labels, which require aligned model architecture and labeled data respectively. |
| Approach: | They propose a general language model distillation method that performs two-stage word prediction distillation and vocabulary compression, which is simple and shows extremely strong performance. |
| Outcome: | The proposed method outperforms 25 state-of-the-art methods on the SuperGLUE benchmark, achieving an average score that surpasses the best method by 3%. |
Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data (2026.acl-long)
Copied to clipboard
Shiping Yang, Jie Wu, Wenbiao Ding, Ning Wu, Shining Liang, Ming Gong, Hongzhi Li, Hengyuan Zhang, Angel X. Chang, Dongmei Zhang
| Challenge: | Existing studies on robustness to explicit noise (e.g., document semantics) but overlook implicit noise (spurious features). |
| Approach: | They propose a framework to quantify the robustness of RAGs against spurious features by integrating a data synthesis pipeline and a taxonomy. |
| Outcome: | The proposed framework quantifies the robustness of RALMs against spurious features. |
PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries (2026.findings-acl)
Copied to clipboard
| Challenge: | PlotGen-Bench evaluates vision-language models' ability to generate executable visualization code from plots under realistic and complex visualization requirements. |
| Approach: | They propose a benchmark to evaluate plot-to-code generation in vision-language models . they use Matplot, Matplos, Mat3D, Mat4D, and Mat4E to evaluate their performance . |
| Outcome: | The proposed benchmark covers 9 major categories, 30 subcategories, and 3 core tasks . it covers 2D, 3D and animated plots across 5 widely used visualization libraries. |
TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing communication topologies rely on spatio-temporal dialogues, which incur high latency and computation. |
| Approach: | They propose a framework for one-shot Topology generation with Diverse Interaction Modes that enables agents to construct heterogeneous communication without iterative coordination. |
| Outcome: | The proposed framework reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. |
GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)
Copied to clipboard
Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan
| Challenge: | Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models. |
| Approach: | They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. |
| Outcome: | The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages. |
RikiNet: Reading Wikipedia Pages for Natural Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Using Wikipedia pages to answer open-domain questions remains challenging in natural language understanding. |
| Approach: | They propose a model which reads Wikipedia pages for natural question answering . it uses a dynamic paragraph dual-attention reader and a cascaded answer predictor . |
| Outcome: | The proposed model outperforms the human model on the Natural Questions dataset . it achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks . |
Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing entity disambiguation methods struggle to capture explicit discourse-level dependencies, resulting in incoherent predictions at the abstract level. |
| Approach: | They propose an unsupervised variational autoencoder to extract latent topic vectors of context sentences to enhance coherence of entity predictions. |
| Outcome: | The proposed system achieves state-of-the-art on popular ED benchmarks with an average improvement of 1.3 F1 points. |
Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)
Copied to clipboard
Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li
| Challenge: | Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. |
| Approach: | They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching. |
| Outcome: | The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions. |
Extracting Military Event Temporal Relations via Relative Event Time Prediction and Virtual Adversarial Training (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing models for extracting event temporal relations typically compare the relative times of events directly, neglecting the contextual information between event pairs. |
| Approach: | They propose a temporal relationship extraction model based on relative event time prediction and virtual adversarial training, MFRV. |
| Outcome: | The proposed model can capture and infer temporal relationships and can be generalized by generating adversarial samples. |
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model (2023.acl-industry)
Copied to clipboard
| Challenge: | Existing knowledge distillation frameworks for language models are limited by memory and the use of complex distillation methods on larger-scale PLMs. |
| Approach: | They propose a general knowledge distillation framework that supports distillation on larger-scale PLMs using various distillation methods. |
| Outcome: | The proposed framework can support distillation on larger-scale PLMs and 25 mainstream methods on 8 NVIDIA A100 (40GB) GPUs. |
EventRelBench: A Comprehensive Benchmark for Evaluating Event Relation Understanding in Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing LLMs fail to capture event relationships, despite advances in NLP . a new benchmark is being developed to assess LLM's ability to extract event relationships . |
| Approach: | They propose a benchmark to assess LLMs' ability to extract event relations . EventRelBench comprises 35K diverse event relation questions . |
| Outcome: | The benchmark EventRelBench measures the performance of large language models on event relation extraction tasks. |
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for news headline generation focus on producing a single short sentence . et al., 2017; Gehrmann e.t., 2018; Zhong ee., 2019) focus on single-headline generation. |
| Approach: | They propose a method to generate multiple headlines with keyphrases of user interests . they propose generating multiple keyphrase-relevant headlines using a transformer decoder . |
| Outcome: | The proposed method achieves state-of-the-art in terms of quality and diversity. |
Instructed Language Models with Retrievers Are Powerful Entity Linkers (2023.emnlp-main)
Copied to clipboard
| Challenge: | Generative approaches powered by large language models have demonstrated emergent abilities in tasks that require complex reasoning abilities. |
| Approach: | They propose a sequence-to-sequence training objective with instruction-tuning that enables casual language models to perform entity linking over knowledge bases. |
| Outcome: | The proposed framework outperforms existing approaches with +6.8 F1 points gain on average and huge advantage in training data efficiency and compute consumption. |
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing data augmentation techniques for natural language processing tasks are difficult to design. |
| Approach: | They propose a controllable rewriting based question data augmentation method for machine reading comprehension, question generation and question-answering natural language inference tasks. |
| Outcome: | The proposed method generates high-quality, high-quality question data samples on machine reading comprehension, question generation, and question-answering natural language inference tasks. |