Papers by Dong Shu
The Impact of Reasoning Step Length on Large Language Models (2024.findings-acl)
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| Challenge: | Long reasoning steps in LLMs improve reasoning abilities, but the correlation between their effectiveness and the length of reasoning steps remains largely unknown. |
| Approach: | They conducted experiments that expand and compress the rationale reasoning steps within CoT demonstrations while keeping all other factors constant. |
| Outcome: | The results show that lengthening the reasoning steps in prompts significantly enhances LLMs’ reasoning abilities across multiple datasets. |
FinCall-Surprise: A Large Scale Multi-modal Benchmark for Earning Surprise Prediction (2026.acl-long)
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| Challenge: | Existing models for earnings surprise prediction rely on expensive, proprietary data. |
| Approach: | They propose to use textual transcripts and audio recordings to build a dataset for earnings surprise prediction. |
| Outcome: | The proposed dataset includes 2,688 unique conference calls from 2019 to 2021. |
A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models (2025.findings-emnlp)
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| Challenge: | Sparse Autoencoders (SAEs) can disentangle complex features into more interpretable components. |
| Approach: | They propose to use Sparse Autoencoders to disentangle LLM features into more interpretable components. |
| Outcome: | The proposed method disentangles complex features into more interpretable components. |
Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability (2025.findings-emnlp)
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| Challenge: | Large Vision-Language Models have demonstrated remarkable capabilities in processing both visual and textual information. |
| Approach: | They examine the challenge of alignment and misalignment in LVLMs through an explainability lens. |
| Outcome: | The findings highlight the need for standardized evaluation protocols and in-depth explainability studies. |
MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)
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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
| Challenge: | telemedicine is a medical practice that provides patient care remotely using video conferencing tools. |
| Approach: | They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance . |
| Outcome: | The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues. |
Logic-Consistency Text Generation from Semantic Parses (2021.findings-acl)
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| Challenge: | Text generation from semantic parses is challenging due to the complexity of the inner logic and the lack of automatic evaluation metrics for logic consistency. |
| Approach: | They propose a framework for logic consistent text generation from semantic parses that employs iterative training procedures and quality control. |
| Outcome: | The proposed framework enhances logic consistency and human evaluation on two benchmark datasets. |
When Safety Alignment Fails to Generalize: Probing with Language Game Jailbreaks (2026.findings-acl)
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| Challenge: | Existing safety alignment methods rely on fixed or narrow transformation schemes to generalize . existing methods based on fixed and narrow transformations are often inadequate . |
| Approach: | They propose a framework for discovering and refining language game-based jailbreaks to probe alignment generalization. |
| Outcome: | The proposed framework allows controlled exploration of alignment behavior across closely related linguistic variants. |
Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are generalist agents capable of operating within complex environments. |
| Approach: | They propose a class of tools that can serve as a middleware layer shielding LLMs from environmental complexity. |
| Outcome: | The proposed tool can shield the LLM from environmental complexity in two representative complex environments. |
Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders (2025.emnlp-main)
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| Challenge: | Sparse Autoencoders (SAEs) have recently emerged as powerful tools for interpreting and steering the internal representations of large language models (LLMs). |
| Approach: | They propose a method that identifies the most influential latents by incorporating output-side gradient information. |
| Outcome: | The proposed method identifies the most influential latents by incorporating output-side gradient information. |
FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models (2026.acl-long)
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| Challenge: | FinChart-Bench is the first benchmark specifically focused on real-world financial charts. |
| Approach: | They propose a benchmark specifically focused on real-world financial charts. |
| Outcome: | The proposed benchmark evaluates 26 state-of-the-art LVLMs on FinChart-Bench. |
Improving LLM Reasoning through Interpretable Role-Playing Steering (2025.findings-emnlp)
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| Challenge: | Existing methods for role-playing rely on prompt engineering, which lacks stability and interpretability. |
| Approach: | They propose a framework that extracts latent representations from role-play prompts and constructs a steering vector that can be injected into the model's residual stream with controllable intensity. |
| Outcome: | The proposed framework extracts latent representations from role-play prompts, selects the most relevant features based on activation patterns, and constructs a steering vector that can be injected into the model’s residual stream with controllable intensity. |