Papers by Chun-Liang Li
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction (2022.acl-long)
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Chen-Yu Lee, Chun-Liang Li, Timothy Dozat, Vincent Perot, Guolong Su, Nan Hua, Joshua Ainslie, Renshen Wang, Yasuhisa Fujii, Tomas Pfister
| Challenge: | Form-like document understanding is a surging research topic due to its practical applications . form documents have unique challenges stemming from their structural characteristics . |
| Approach: | They propose a structure-aware sequence model that leverages spatial relationships between tokens in a form for more precise attention score calculation. |
| Outcome: | The proposed model outperforms existing methods with a more compact model size and less pre-training data. |
MUSCLE: A Model Update Strategy for Compatible LLM Evolution (2024.findings-emnlp)
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Jessica Echterhoff, Fartash Faghri, Raviteja Vemulapalli, Ting-Yao Hu, Chun-Liang Li, Oncel Tuzel, Hadi Pouransari
| Challenge: | Large Language Models (LLMs) are regularly updated to enhance performance, typically through changes in data or architecture. |
| Approach: | They propose a method to minimize the extent of instance regression in model updates by training a compatibility adapter that can enhance task fine-tuned language models. |
| Outcome: | The proposed approach reduces regressions by up to 40% when updating LLMs to newer versions while maintaining overall performance gains. |
Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization (2024.findings-acl)
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Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long Le, Abhishek Kumar, James Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister
| Challenge: | Large language models struggle to capture relevant information located in the middle of their input. |
| Approach: | They propose a calibration mechanism that allows the model to attend to contexts faithfully according to their relevance even when they are in the middle. |
| Outcome: | The proposed calibration mechanism mitigates this positional bias and improves retrieval-augmented generation performance. |
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (2023.findings-acl)
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister
| Challenge: | Deploying large language models (LLMs) is difficult because they are memory inefficient and compute-intensive for practical applications. |
| Approach: | They propose a mechanism that fine tunes or distills small models that outperform LLMs . they use human labels to fine tune models or LLM-generated labels to train models . |
| Outcome: | The proposed method outperforms LLMs by using fewer training examples compared to few-shot prompted models using substantially smaller model sizes. |
Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pre-training (2026.findings-eacl)
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Jeffrey Li, Joshua P Gardner, Doug Kang, Fangping Shi, Karanjeet Singh, Chun-Liang Li, Herumb Shandilya, David Leo Wright Hall, Oncel Tuzel, Percy Liang, Ludwig Schmidt, Hadi Pouransari, Fartash Faghri
| Challenge: | Existing open-source datasets predominantly apply a single fixed extractor to all webpages. |
| Approach: | They propose to take a Union over different extractors to improve model performance . they show that extractor choice can significantly impact downstream task performance based on content type . |
| Outcome: | The proposed approach can increase the token yield of DCLM-Baseline by 71% while maintaining benchmark performance. |
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction (2023.acl-long)
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Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat, Vincent Perot, Guolong Su, Xiang Zhang, Kihyuk Sohn, Nikolay Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua, Tomas Pfister
| Challenge: | Existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. |
| Approach: | They propose a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. |
| Outcome: | The proposed model achieves state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size. |
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction (2021.acl-short)
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Chen-Yu Lee, Chun-Liang Li, Chu Wang, Renshen Wang, Yasuhisa Fujii, Siyang Qin, Ashok Popat, Tomas Pfister
| Challenge: | Graph Convolutional Networks (GCNs) have limited ability to capture reading orders of given word-level node representations in a graph. |
| Approach: | They propose a new positional encoding technique to capture word-level nodes in a graph. |
| Outcome: | The proposed method improves existing GCNs with an 8.4% F1 score on two datasets and a large-scale payment dataset. |
CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)
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Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
| Challenge: | Recent work on generating diverse instructions and applying LLM to increase instruction complexity neglects downstream use cases. |
| Approach: | They propose a framework for generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. |
| Outcome: | Experiments on four open-domain instruction using the proposed framework validate the effectiveness of CodecLM over the current state-of-the-art. |