Papers by Hong Xie
Reconstruction Attack on Instance Encoding for Language Understanding (2021.emnlp-main)
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| Challenge: | Existing private learning schemes which protect data privacy can be used to train models using instance encoding. |
| Approach: | They propose to recover the private training data and use it to break a private learning scheme TextHide. |
| Outcome: | The proposed attack would advance privacy-preserving machine learning in the context of natural language processing. |
Reflect, Rewrite, Repeat: How Simple Arithmetic Enables Advanced Reasoning in Small Language Models (2026.findings-eacl)
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Mengdie Flora Wang, Haochen Xie, Mun Young Kim, Baishali Chaudhury, Meghana Ashok, Suren Gunturu, Sungmin Hong, Jae Oh Woo
| Challenge: | Recent advances in language model reasoning require computationally intensive reinforcement learning and massive datasets. |
| Approach: | They propose a framework that combines Direct Preference Optimization and Supervised Fine-Tuning with selective guidance from larger models and iteratively refining solutions through a "reflect, rewrite, repeat" cycle. |
| Outcome: | The proposed framework shows significant performance improvements across arithmetic, symbolic and cognitive reasoning benchmarks. |
SubDocTrans: Enhancing Document-level Machine Translation with Plug-and-play Multi-granularity Knowledge Augmentation (2025.findings-emnlp)
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| Challenge: | Document translations generated by large language models suffer from poor consistency, weak coherence, and omission errors. |
| Approach: | They propose a document-level machine translation framework that extracts knowledge from documents to produce high-quality translations. |
| Outcome: | The proposed framework improves consistency and coherence, reduces omission errors, and mitigates hallucinations. |
Understanding and Patching Compositional Reasoning in LLMs (2024.findings-acl)
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| Challenge: | LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. |
| Approach: | They propose a lightweight method to patch compositional reasoning errors via editing the located MHSA modules in LLMs. |
| Outcome: | The proposed method can be used to patch compositional reasoning errors using MHSA modules located within the layers of the LLMs. |
Adaptive Textual Label Noise Learning based on Pre-trained Models (2023.findings-emnlp)
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| Challenge: | Existing approaches to learning with noisy labels are limited due to the time and labor costs involved. |
| Approach: | They propose an adaptive warm-up and hybrid training frameworks to learn with noisy labels based on pre-trained models. |
| Outcome: | The proposed approach performs comparable or even surpasses state-of-the-art methods in various noise scenarios, including scenarios with the mixture of multiple types of noise. |
Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are fundamentally reshaping the scientific landscape, transitioning the role of AI from passive tools to active partners within a new paradigm of Human-AI collaboration. |
| Approach: | They propose a benchmark to evaluate the ability of Large Language Models to improve papers with human feedback. |
| Outcome: | The proposed benchmark tests the skills of Large Language Models (LLMs) on paper interpretation, experimental implementation, and paper formulation, using authors’ camera-ready versions as natural human baselines. |
The Model Agreed, But Didn’t Learn: Diagnosing Surface Compliance in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models internalize vast world knowledge as parametric memory, yet inherit the staleness and errors of their source corpora. |
| Approach: | They propose a framework that subjects models to discriminative self-assessment under diverse contextual pressures to scrutinize subtle behavioral nuances induced by memory modifications. |
| Outcome: | The proposed framework achieves high benchmarks without overwriting internal beliefs, while recursive modifications accumulate representational residues, triggering cognitive instability and permanently diminishing the reversibility of the model’s memory state. |
Exploring the Choice Behavior of Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly being adopted across various domains where they help to make choices. |
| Approach: | They construct a virtual QA platform that includes three different experimental conditions, with four models from GPT and Llama series participating in repeated experiments. |
| Outcome: | The proposed model includes three experimental conditions and four models from GPT and Llama series. |
Differentially Private Instance Encoding against Privacy Attacks (2022.naacl-srw)
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| Challenge: | TextHide is a proposed privacy-enhancing technology to protect the training data from privacy attacks. |
| Approach: | They propose to encode training data via instance encoding in natural language domain without theoretic privacy guarantee. |
| Outcome: | The proposed scheme can defend against privacy attacks while ensuring learning utility (as a trade-off). |
CoAlign: Uncertainty Calibration of LLM for Geospatial Repartition (2025.acl-industry)
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| Challenge: | Existing methods to optimize geospatial repartition rely on manual adjustments by experts or algorithmic optimization using limited offline operational metrics. |
| Approach: | They propose a framework that calibrates LLM uncertainty to enable robust geospatial repartition by integrating historical data with LLM-generated candidates. |
| Outcome: | The proposed framework calibrates LLM uncertainty to enable robust geospatial repartition by integrating historical data with LLM-generated candidates. |