Papers by Chuang Gan
JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions (2023.findings-acl)
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| Challenge: | Existing benchmarks focus on a single reasoning type and ask human annotators to write candidate statements related to the particular type of commonsense. |
| Approach: | They propose a new commonsense reasoning dataset based on human’s Interactive Fiction (IF) gameplaywalkthroughs. |
| Outcome: | The proposed dataset is challenging to previous machine reading models and large language models with a significant 20%performance gap compared to human experts. |
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training (2025.findings-acl)
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Yihang Yao, Zhepeng Cen, Miao Li, William Han, Yuyou Zhang, Emerson Liu, Zuxin Liu, Chuang Gan, Ding Zhao
| Challenge: | Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. |
| Approach: | They propose a data-centric approach that enhances LLMs’ awareness of symmetry in query variations and propose syMmetry-ENhanceD (MEND) data augmentation. |
| Outcome: | Extensive experiments on logical and arithmetic reasoning tasks show that the proposed approach improves model robustness at the knowledge extraction stage through query augmentation. |
Sparse Universal Transformer (2023.emnlp-main)
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| Challenge: | Existing models that use VTs as their backbone model are based on UTs that share parameters across layers and have better compositional generalization. |
| Approach: | They propose to use Sparse Mixture of Experts to reduce UT's computation complexity while retaining its parameter efficiency and generalization ability. |
| Outcome: | The proposed model achieves strong generalization results on formal language tasks and impressive parameter and computation efficiency on standard natural language benchmarks. |
Tailored Primitive Initialization is the Secret Key to Reinforcement Learning (2026.acl-long)
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| Challenge: | Reinforcement learning (RL) has emerged as a powerful paradigm for improving the reasoning capabilities of large language models. |
| Approach: | They propose a pipeline that automatically discovers thinking token patterns with reasoning primitives and curates SFT datasets to prepare LLMs for RL. |
| Outcome: | The proposed pipeline outperforms baseline methods on mathematical and logical reasoning benchmarks on RL tasks. |
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. |
| Approach: | They propose to re-formulate IF game solving as Multi-Passage Reading Comprehension tasks using context-query attention mechanisms and structured prediction to efficiently generate and evaluate action outputs. |
| Outcome: | The proposed methods achieve high winning rates and low data requirements on the recent IF benchmark (Jericho) |
Aligning Large Multimodal Models with Factually Augmented RLHF (2024.findings-acl)
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Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liangyan Gui, Yu-Xiong Wang, Yiming Yang, Kurt Keutzer, Trevor Darrell
| Challenge: | Large Multimodal Models (LMMs) are built across modalities and the misalignment between two modality can result in "hallucination" . developing LMMs faces challenges such as a lack of data and a limited number of data sets. |
| Approach: | They propose a new algorithm that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options. |
| Outcome: | The proposed approach improves on the LLaVA-Bench dataset with the 96% performance level of the text-only GPT-4 and an improvement of 60% on MMHAL-BENCH over other baselines. |
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (2020.acl-main)
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| Challenge: | Extensive experiments on four machine translation tasks demonstrate that HAT can discover efficient models for different hardware (CPU, GPU, IoT device). |
| Approach: | They propose to construct a large design space with arbitrary encoder-decoder attention and heterogeneous layers and then train a SuperTransformer that efficiently produces many SubTransformers with weight sharing. |
| Outcome: | The proposed framework can find efficient models for different hardware (CPU, GPU, IoT device) it achieves 3 speedup, 3.7 smaller size over baseline Transformer; 2.7 speed up, 3.6 smaller sizes over Evolved Transformer with 12,041 less search cost and no performance loss. |
Revisiting the Roles of “Text” in Text Games (2022.findings-emnlp)
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| Challenge: | Recent work has shown that random text hashes could be complementary rather than contrasting in text games. |
| Approach: | They propose a scheme to extract contextual information into an approximate state hash as extra input for an RNN-based text agent. |
| Outcome: | The proposed scheme achieves competitive performance with state-of-the-art text agents using advanced NLU techniques such as knowledge graph and passage retrieval. |
Steering LLM Thinking with Budget Guidance (2026.findings-acl)
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| Challenge: | Existing budget control methods for large language models are inadequate for long reasoning . budget guidance can be used to control reasoning length without fine-tuning . |
| Approach: | They propose a budget guidance method that models a Gamma distribution over remaining thinking length during next-token generation and uses it to guide generation in a soft, token-level manner. |
| Outcome: | The proposed method achieves up to 26% accuracy gain on the MATH-500 benchmark compared to baseline methods while maintaining competitive accuracy with only 63% of the thinking tokens used by the full-thinking model. |