Papers by Shuhao Guan
PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy (2025.acl-long)
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| Challenge: | Existing pipelines that combine document image restoration with semantic-aware post-OCR correction can improve text extraction from degraded images. |
| Approach: | They propose a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance both visual clarity and textual consistency. |
| Outcome: | The proposed pipeline reduces character error rates by 63.9-70.3% on 13,831 pages of real historical documents in English, French, and Spanish compared to OCR on raw images. |
CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)
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| Challenge: | Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. |
| Approach: | They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability. |
| Outcome: | The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights. |
LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection (2026.acl-long)
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| Challenge: | Current evaluation frameworks are static and vulnerable to benchmark data contamination . current models are ineffective at assessing reasoning under temporal uncertainty . |
| Approach: | They propose a live-based benchmark that simulates the real-world "fog of war" they propose evaluating models on their ability to reason with evolving, incomplete information . |
| Outcome: | The proposed model outperforms proprietary state-of-the-art models in classification and evidence mode . it also provides a component to monitor BDC explicitly . |
SSA: Semantic Contamination of LLM-Driven Fake News Detection (2025.emnlp-main)
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| Challenge: | Evaluating 45 variants of nine LLMs, we find LIAR2 accuracy climbs monotonically with injected contamination, while the SSA Factor escalates in near-perfect lock-step. |
| Approach: | They propose a framework that detects BDC risks across semantic to label level via entity shift perturbation and an interpretable metric, the SSA Factor. |
| Outcome: | The proposed framework detects BDC risks across semantic to label level via entity shift perturbation and interpretable metric, the SSA Factor. |
Advancing Post-OCR Correction: A Comparative Study of Synthetic Data (2024.findings-acl)
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| Challenge: | Optical Character Recognition (OCR) is a useful tool for preserving cultural heritage and supporting downstream tasks. |
| Approach: | They propose an algorithm that leverages computer vision feature detection algorithms to calculate glyph similarity for constructing post-OCR synthetic data. |
| Outcome: | The proposed method significantly reduces CERs in low-resource languages without manually annotating data. |
UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models (2025.acl-long)
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| Challenge: | Existing methods such as LoRA and VeRA use a low-rank approximation method that reduces the number of trainable parameters without compromising performance. |
| Approach: | They propose a parameter-efficient fine-tuning approach that leverages a low-rank approximation method that reduces the number of trainable parameters without compromising performance. |
| Outcome: | The proposed approach outperforms existing methods on GLUE and E2E benchmarks and is effective in instruction-tuning large language models and image classification models. |
Effective Synthetic Data and Test-Time Adaptation for OCR Correction (2024.emnlp-main)
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| Challenge: | Recent research has framed the post-OCR task as a Seq2Seq Neural Machine Translation (NMT) task. |
| Approach: | They propose a method for constructing post-OCR synthetic data with different noise levels using weak supervision. |
| Outcome: | The proposed method reduces CER by 68.67% without relying on manual annotations. |
DCR: Quantifying Data Contamination in LLMs Evaluation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) memorize evaluation data during training, inflating performance metrics and undermining genuine generalization assessment. |
| Approach: | They propose a framework to detect and quantify benchmark data contamination (BDC) by synthesizing contamination scores via a fuzzy inference system. |
| Outcome: | The proposed framework detects and quantifies BDC risk across semantic, informational, data, and label levels. |