Papers by Zhiqiang Ma
“What is the value of templates?” Rethinking Document Information Extraction Datasets for LLMs (2024.findings-emnlp)
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Ran Zmigrod, Pranav Shetty, Mathieu Sibue, Zhiqiang Ma, Armineh Nourbakhsh, Xiaomo Liu, Manuela Veloso
| Challenge: | Existing work on prompt-response datasets for visually rich document understanding (VRDU) is labor-intensive. |
| Approach: | They propose a set of questions that are transformed from a key information extraction template to a prompt-response format using a plethora of bespoke templates. |
| Outcome: | The proposed datasets are compared with baseline models on K2Q with zero-shot prompting. |
DocLLM: A Layout-Aware Generative Language Model for Multimodal Document Understanding (2024.acl-long)
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Dongsheng Wang, Natraj Raman, Mathieu Sibue, Zhiqiang Ma, Petr Babkin, Simerjot Kaur, Yulong Pei, Armineh Nourbakhsh, Xiaomo Liu
| Challenge: | Documents with rich layouts are a significant portion of enterprise corpora and document AI is still a challenge. |
| Approach: | They propose a lightweight extension to traditional large language models for reasoning over visual documents that takes into account both textual semantics and spatial layout. |
| Outcome: | The proposed model outperforms existing large language models on 14 out of 16 datasets and generalizes well to 4 out of 5 previously unseen datasets. |
ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images (2026.eacl-long)
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Mathieu Sibue, Andrés Muñoz Garza, Samuel Mensah, Pranav Shetty, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso
| Challenge: | Existing models for structured information extraction are limited by narrow entity ontologies, simple queries, or homogeneous document types. |
| Approach: | They propose a benchmark dataset for structured Information Extraction (IE) from document images . they analyze open and closed VLMs on this benchmark . |
| Outcome: | The proposed model can perform fine-grained structured extraction across document types and schemas. |
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks (2023.emnlp-industry)
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| Challenge: | Recent large language models such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models . however, their applicability and effectiveness in specific domains like finance needs a better understanding . |
| Approach: | They conduct empirical studies to compare the performance of ChatGPT and GPT-4 on financial text analytical problems using eight benchmark datasets from five categories of tasks. |
| Outcome: | The proposed models outperform the state-of-the-art models on a wide range of financial text analytical tasks. |
Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation (2024.findings-emnlp)
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| Challenge: | Language models can memorize detailed information and patterns, but raise privacy concerns . ANADP reduces the performance gap between regular and DP fine-tuning while maintaining the privacy constraints. |
| Approach: | They propose an algorithm that allocates additive noise based on the importance of model parameters to reduce the performance gap between regular fine-tuning and traditional DP fine- tuning. |
| Outcome: | The proposed algorithm narrows the performance gap between regular fine-tuning and traditional DP fine- tuning while maintaining privacy constraints. |
How Far Can LLMs Improve from Experience? Measuring Test-Time Learning Ability in LLMs with Human Comparison (2025.emnlp-main)
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| Challenge: | Existing benchmarks primarily assess static knowledge, while intelligence also entails the ability to rapidly learn from experience. |
| Approach: | They propose to use semantic games to evaluate test-time learning . they recruit eight human participants to complete the same task . |
| Outcome: | The proposed framework compares model performance under limited and cumulative experience settings and contains four forms of experience representation. |
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering (2022.emnlp-main)
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| Challenge: | Recent advances in large pre-trained language models have brought the NLP field into a new era. |
| Approach: | They propose a large-scale dataset to study the chain of numerical reasoning in conversational question answering. |
| Outcome: | The proposed dataset should push forward the exploration of real-world, complex reasoning tasks as the next research focus. |
CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation (2025.acl-long)
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Santosh T.y.s.s, Youssef Tarek Elkhayat, Oana Ichim, Pranav Shetty, Dongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh, Xiaomo Liu
| Challenge: | LLMs can provide key benefits to the Legal domain, but their adoption has been hindered by their tendency to generate unfaithful, ungrounded, or hallucinatory outputs. |
| Approach: | They propose a Confidence-guided copy-based decoding strategy that dynamically interpolates the model produced vocabulary distribution with a distribution derived based on copying from the context. |
| Outcome: | The proposed method outperforms existing context-aware decoding methods on five legal benchmarks. |
The State of the Art of Large Language Models on Chartered Financial Analyst Exams (2024.emnlp-industry)
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Mahmoud Mahfouz, Ethan Callanan, Mathieu Sibue, Antony Papadimitriou, Zhiqiang Ma, Xiaomo Liu, Xiaodan Zhu
| Challenge: | Chartered Financial Analyst (CFA) program is widely recognized globally . study compares state-of-the-art large language models with open-source models . proprietary models pass levels I and II, but fail at level III due to essay questions . |
| Approach: | They benchmark five leading proprietary models and eight open-source models on mock CFA exams to provide an overview of their financial analysis capabilities. |
| Outcome: | The models on the mock CFA exams pass the highest scores, but fail at the lowest levels due to essay questions. |
RASPberry: Retrieval-Augmented Monte Carlo Tree Self-Play with Reasoning Consistency for Multi-Hop Question Answering (2025.findings-acl)
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| Challenge: | Existing methods for generating and analyzing multiple document knowledge are not effective for multi-hop question answering. |
| Approach: | They propose a Monte Carlo tree-based approach to inference-time scaling using RASPberry. |
| Outcome: | Experimental results show that the proposed method achieves better inference-time scaling on smaller LLMs. |
Detecting Non-Membership in LLM Training Data via Rank Correlations (2026.eacl-long)
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| Challenge: | Large language models (LLMs) are trained on increasingly vast and opaque text corpora. |
| Approach: | They propose a test that detects dataset-level non-membership using only grey-box access to model logits. |
| Outcome: | The proposed test detects dataset-level non-membership using only grey-box access to model logits. |