Papers by Pengfei Yu
Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction (2023.findings-eacl)
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| Challenge: | Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. |
| Approach: | They propose a model architecture that jointly encodes a source language input sentence with its translation to the target language during training and takes a target language sentence with it as input during evaluation. |
| Outcome: | The proposed model architecture can integrate machine translation to improve event extraction while adding machine-translated data yields unstable performance due to representational gap. |
Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models (2025.acl-demo)
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Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, Kang Liu
| Challenge: | Existing interpretation methods only support tasks with specific inputs, limiting their practical applications. |
| Approach: | They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs. |
| Outcome: | The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs. |
Why Does New Knowledge Create Messy Ripple Effects in LLMs? (2024.emnlp-main)
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| Challenge: | Existing research has focused on post-training knowledge editing (KE) for language models to ensure that knowledge remains accurate and up-to-date. |
| Approach: | They propose to use a GradSim indicator to detect when and why updated knowledge ripples in language models. |
| Outcome: | The proposed indicator GradSim shows that LMs that fail to handle ripple effects have low GradSIM. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
EVEDIT: Event-based Knowledge Editing for Deterministic Knowledge Propagation (2024.emnlp-main)
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| Challenge: | Existing knowledge editing approaches only operate on (subject, relation, object) triple . current methods are limited to (substance, relation) triple, causing low confidence in their answers. |
| Approach: | They propose a task of event-based knowledge editing that pairs facts with event descriptions to improve model confidence. |
| Outcome: | The proposed method improves model confidence by 55.6% while maintaining the naturalness of generation. |
UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition (2024.findings-emnlp)
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| Challenge: | Table structure recognition technology is a critical tool for processing and analyzing large volumes of tabular data. |
| Approach: | They propose a framework for table structure parsing based on the image-to-text model and a vision guider to refine the model’s capability to understand textual semantics in table images. |
| Outcome: | The proposed framework improves on a dataset of PubTabNet, PubTables1M, WTW, and iFLYTAB and will be made publicly available. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)
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| Challenge: | Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. |
| Approach: | They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. |
| Outcome: | The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans. |
Lifelong Event Detection with Knowledge Transfer (2021.emnlp-main)
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| Challenge: | Traditional supervised Information Extraction (IE) methods can extract structured knowledge elements from unstructured data, but it is limited to a pre-defined target ontology. |
| Approach: | They propose a new lifelong event detection framework that is generalizable to other IE tasks and updates old knowledge with new event types’ mentions using a self-training loss. |
| Outcome: | The proposed framework outperforms baselines with a 5.1% gain in the F1 score and can boost the F2 score for over 30% on some new long-tail rare event types with few training instances. |
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)
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Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
| Challenge: | a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation. |
| Approach: | They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements . |
| Outcome: | The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements . |
Unlearning Bias in Language Models by Partitioning Gradients (2023.findings-acl)
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| Challenge: | Recent research has shown that large-scale pretrained language models exhibit issues relating to racism, sexism, religion bias, and toxicity in general. |
| Approach: | They propose a gray-box method for debiasing pretrained masked language models using partitioned contrastive gradient unlearning (PCGU) aims to optimize only the weights that contribute most to a specific domain of bias by computing a first-order approximation based on the gradients of contrastive sentence pairs. |
| Outcome: | The proposed method is low-cost and can pinpoint the sources of social bias in large pretrained language models. |
InFoBench: Evaluating Instruction Following Ability in Large Language Models (2024.findings-acl)
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Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu
| Challenge: | Existing methods for evaluating Large Language Models (LLMs) ability to follow instructions have not been able to provide a detailed analysis of their compliance with instructions. |
| Approach: | They propose a new metric for evaluating Large Language Models' ability to follow instructions and a benchmark for DRFR. |
| Outcome: | The proposed metric and benchmark compared with traditional scoring methods and explores annotation sources including human experts, crowd-sourced workers, and GPT-4. |
Defining a New NLP Playground (2023.findings-emnlp)
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Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, Heng Ji
| Challenge: | Recent explosion of performance of large language models (LLMs) has changed the field more abruptly and seismically than any other shift in the field’s 80 year history. |
| Approach: | They propose 20+ PhD-dissertation-worthy research directions to define a new NLP playground by combining theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
| Outcome: | The proposed research will cover theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation (2024.acl-long)
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| Challenge: | Existing sources of story premises are limited by a lack of diversity, uneven quality, and high costs that make them difficult to scale. |
| Approach: | They propose a method which breaks down story premises into modules like background and persona for automated design and generation. |
| Outcome: | The proposed framework excels in diversity, fascination, completeness, and originality compared to those induced from large language models and captured from public datasets. |
Shorten the Long Tail for Rare Entity and Event Extraction (2023.eacl-main)
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| Challenge: | Existing approaches for the long-tailed learning problem seek to manipulate the training data by re-balancing, augmentation or introducing extra prior knowledge. |
| Approach: | They propose to transform infrequent candidate mention representation with the average mention representation in the training dataset to handle the generalization challenge. |
| Outcome: | The proposed framework can generalize to rare or unseen expressions of entities or events, especially for rare types without sufficient training examples. |
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention (D18-1)
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| Challenge: | Existing methods for relation extraction use knowledge graphs to automatically label training data . but, it suffers from the wrong labeling problem because not all sentences containing two entities can express their relations in KGs . |
| Approach: | They propose a distant supervision approach to automatically label training instances . they integrate hierarchical information of relations into distantly supervised relation extraction . |
| Outcome: | The proposed model outperforms baseline models on a large-scale dataset. |
From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization (2026.findings-acl)
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| Challenge: | Existing research on PTQ spans three primary directions. |
| Approach: | They conduct a systematic analysis of post-training quantization failures using PTQ . they show that targeted repair can mitigate Signal Degradation but remains ineffective for Computation Collapse . |
| Outcome: | The proposed method mitigates Signal Degradation but remains ineffective for Computation Collapse. |
Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models (2026.findings-acl)
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| Challenge: | Existing scaling laws focus on general performance, overlooking crucial fine-grained factors and how quantization differentially impacts diverse knowledge capabilities. |
| Approach: | They propose a framework that unifies model size, bit-width, and fine-grained factors into memorization, application, and reasoning. |
| Outcome: | The proposed framework shows strong fit and cross-architecture consistency on 293 different PTQ configurations. |
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems (2023.findings-acl)
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| Challenge: | Existing approaches to decode a given context-candidate pair are expensive and time-consuming. |
| Approach: | They propose a new paradigm that keeps full attention over each pair while only encoding the context once. |
| Outcome: | The proposed paradigm achieves new state-of-the-art results on four benchmark datasets with high computational efficiency. |
The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination (2025.findings-acl)
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Yuji Zhang, Sha Li, Cheng Qian, Jiateng Liu, Pengfei Yu, Chi Han, Yi R. Fung, Kathleen McKeown, ChengXiang Zhai, Manling Li, Heng Ji
| Challenge: | Hallucination is a persistent challenge in large language models where even with rigorous quality control, models often generate distorted facts. |
| Approach: | They propose a new framework to quantify factual hallucinations by modeling knowledge overshadowing. |
| Outcome: | The proposed framework improves model factuality on Overshadow (27.9%), MemoTrap (13.1%) and NQ-Swap (18.3%). |
SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)
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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang
| Challenge: | Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences. |
| Approach: | They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context. |
| Outcome: | The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs. |