Papers by Ting Hua
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage (2026.findings-acl)
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Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie
| Challenge: | Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. |
| Approach: | They propose an algorithm that detects threshold where information loss exceeds information gain during sparse decoding to reduce token consumption by up to 90% and a marginal accuracy degradation of less than 2%. |
| Outcome: | The proposed algorithm reduces token consumption by 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks. |
SMART: Evaluating LLMs’ Mathematical Reasoning via a Human Cognitive Process-Inspired Benchmark (2026.acl-long)
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| Challenge: | Existing evaluation methods focus on the final answer or on the intermediate reasoning steps, overlooking its inherently multi-stage and multi-dimensional nature. |
| Approach: | They propose a benchmark that decomposes mathematical problem-solving into four cognitive dimensions and introduces dimension-specific tasks to measure their cognitive processes. |
| Outcome: | The proposed model decomposes mathematical problem-solving into four cognitive dimensions and introduces dimension-specific tasks to measure their cognitive processes. |
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation (2020.acl-main)
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| Challenge: | Existing word-level policy models that learn dialog policy and language generation from dialog corpora often lead to degeneration issues where the utterances become ungrammatical or repetitive. |
| Approach: | They propose to represent prior dialog transitions as a graph and learn a CG grounded dialog policy that can foster a more coherent and controllable dialog. |
| Outcome: | The proposed framework is able to learn dialog policy in open-domain multi-turn conversation. |
Adaptive Rank Selections for Low-Rank Approximation of Language Models (2024.naacl-long)
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| Challenge: | Singular Value Decomposition (SVD) or its weighted variants has progressed in compressing language models. |
| Approach: | They propose a binary masking mechanism for optimizing the number of ranks in a differentiable framework. |
| Outcome: | The proposed algorithm achieves much better accuracy than previous SVD and its weighted variants. |
DuReadervis: A Chinese Dataset for Open-domain Document Visual Question Answering (2022.findings-acl)
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| Challenge: | Open-domain question answering is a task that requires answering questions based on a collection of document images. |
| Approach: | They propose to use document images to answer questions using layouts and visual features instead of text. |
| Outcome: | The proposed approach reduces human cost and improves scalability of QA systems by incorporating layouts and visual features. |
HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks (2026.findings-acl)
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Yiming Zeng, Jinghan Cao, Zexin Li, Wanhao Yu, Zhankai Ye, Dawei Xiang, Ting Hua, Xin Liu, Shangqian Gao, Tingting Yu
| Challenge: | Existing approaches treat instruction-based text editing as a generic text generation problem. Existing methods either over-edit or fail to apply modifications consistently. |
| Approach: | They propose a framework that processes each editing request to best align with it. |
| Outcome: | The proposed framework achieves 9% improvement over the state-of-the-art model. |
CrochetBench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain? (2026.acl-long)
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| Challenge: | Existing multimodal large language models cannot generate executable procedures . authors propose a new benchmark to assess procedural competence in multimodal models . |
| Approach: | They propose a new benchmark to assess procedural competence in multimodal large language models . they use a CrochetPARADE DSL representation to enable structural validation and functional evaluation . |
| Outcome: | The proposed model enables structural validation and functional evaluation via execution. |
Context Attribution with Multi-Armed Bandit Optimization (2026.findings-acl)
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| Challenge: | Existing approaches to augmenting attribution with retrieval-augmented generation (RAG) focus on training models to explicitly cite context segments during generation, but their reliability remains unverifiable. |
| Approach: | They propose a framework that formulates context attribution as a combinatorial multi-armed bandit problem by using Linear Thompson Sampling to efficiently identify the most influential context segments while minimizing the number of model queries. |
| Outcome: | The proposed method reduces model queries by 30% while matching or exceeding the attribution quality of existing approaches. |
Towards Conversational Recommendation over Multi-Type Dialogs (2020.acl-main)
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| Challenge: | In recent years, there has been a significant increase in the work of conversational recommendation due to the rise of voice-based bots. |
| Approach: | They use a Chinese dialog dataset DuRecDial to study conversational recommendation in the context of multi-type dialogs where bots can proactively lead a conversation from a non-recommendation dialog to a recommendation dialog. |
| Outcome: | The proposed dataset allows to investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how interact with users for recommendation. |
AgentDrug: Utilizing Large Language Models in an Agentic Workflow for Zero-Shot Molecular Optimization (2025.findings-emnlp)
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| Challenge: | Molecular optimization is a fundamental task in drug discovery. |
| Approach: | They propose an agentic workflow that leverages LLMs in a structured refinement process to achieve significantly higher accuracy. |
| Outcome: | The proposed workflow improves on single- and multi-property optimization tasks under loose and strict thresholds. |
Numerical Optimizations for Weighted Low-rank Estimation on Language Models (2022.emnlp-main)
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| Challenge: | Singular value decomposition (SVD) is one of the most popular methods for estimating a target matrix with smaller matrices. |
| Approach: | They propose a method that approximates a target matrix with smaller matrices by two smaller . they also propose metric to predict when the SVD may introduce a significant performance drop. |
| Outcome: | The proposed method can perform better than current SOTA methods in compressing Transformer-based language models. |
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding (2021.naacl-main)
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| Challenge: | Existing continual learning approaches suffer from low accuracy and performance fluctuation when the distributions of old and new data are significantly different. |
| Approach: | They propose a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. |
| Outcome: | The proposed model outperforms the best state-of-the-art method by 20% in average accuracy and each component contributes effectively to overall performance. |
Dynamic Low-rank Estimation for Transformer-based Language Models (2023.findings-emnlp)
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| Challenge: | RankDyna is a matrix decomposition method that can be used to compress Transformer-based language models. |
| Approach: | They propose a matrix decomposition method that enables dynamic rank resource allocation . they say it can outperform current SOTA methods under various parameter budget levels . |
| Outcome: | The proposed method outperforms current SOTA methods under various budget levels . the proposed method is more efficient with higher compression rates . |
Process-Supervised Reinforcement Learning for Code Generation (2025.emnlp-main)
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| Challenge: | Existing reinforcement learning strategies based on outcome supervision have shown effectiveness in code generation tasks, but their effectiveness in the field of code generation remains limited. |
| Approach: | They propose a method that uses a teacher model to mutate and refactor statements and a compiler to automatically label them. |
| Outcome: | The proposed method improves performance in complex code generation tasks. |