Papers by Ruilin Yang
CriticBench: Benchmarking LLMs for Critique-Correct Reasoning (2024.findings-acl)
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| Challenge: | CriticBench is a benchmark designed to assess LLMs’ abilities to critique and refine their reasoning across a variety of tasks. |
| Approach: | They propose a benchmark to assess LLMs' ability to critique and correct reasoning across a variety of tasks. |
| Outcome: | The proposed benchmark examines the performance of 17 large language models in generation, critique, and correction reasoning. |
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are powerful tools for Text-to-SQL tasks . SQL solutions have a relatively fixed pattern, allowing for categorical thinking . |
| Approach: | They propose that query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type, thus enhancing their reasoning abilities across diverse difficulty levels and problem categories. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Spider and BIRD datasets. |
The Price of Thought: A Multilingual Analysis of Reasoning, Performance, and Cost of Negotiation in Large Language Models (2026.findings-eacl)
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Sherzod Hakimov, Roland Bernard, Tim Leiber, Karl Osswald, Kristina Richert, Ruilin Yang, Raffaella Bernardi, David Schlangen
| Challenge: | Negotiation is a fundamental challenge for AI agents as it requires an ability to reason strategically, model opponents, and balance cooperation with competition. |
| Approach: | They propose to use a self-play setup to compare commercial and open-weight large language models to their vanilla counterparts in three different languages to examine trade-offs between performance and cost. |
| Outcome: | The proposed model improves GPT-5's performance by 31.4 % while increasing its cost by nearly 400 %. |
Optimizing Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models (2026.acl-srw)
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| Challenge: | Documents are typically concatenated to chunks of maximum sequence length (MSL) and shuffled in chunks (atom-size chunks). |
| Approach: | They propose to pack and shuff documents in chunks of tokens to prevent overfitting . they also propose to use padding to only include one document per chunk . |
| Outcome: | The proposed method reduces the risk of overfitting and improves generalizability. |
From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs (2025.findings-acl)
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| Challenge: | Existing approaches to unlearning often treat nonsensical responses or template-based refusals as the unlearning target, making the process even more vulnerable to attacks and jailbreaks. |
| Approach: | They propose a method that uses inverted facts to remove the need for auxiliary models or retaining data while avoiding leakage. |
| Outcome: | Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility. |
Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)
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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu
| Challenge: | Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes. |
| Approach: | They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence. |
| Outcome: | The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show. |
E-ViC: Reasoning Beyond Text via Embodied Visual Chain for Spatial Intelligence (2026.acl-long)
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Junbo Qi, Yi Zhang, Hanchu Ni, Che Liu, Zhimin Yao, Ruilin Yang, Xiancong Ren, Liangjian Wen, Wei Ge, Yuya Ieiri, Osamu Yoshie, Yong Dai, Xiaozhu Ju
| Challenge: | Existing Vision-Language Models (VLMs) lack spatial reasoning, despite text-based CoTs . e-ViC reframes spatial intelligence as a verifiable, tool-using capability, argues a new study. |
| Approach: | They propose a framework that moves reasoning beyond text into the visual domain . they ground reasoning in pixel-level interactions to enable human-like "look-and-confirm" strategies . |
| Outcome: | The proposed framework outperforms existing Vision-Language Models with an average gain of 10.1%. |
Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing knowledge graphs lack robustness and incompleteness to provide link prediction. |
| Approach: | They propose to capture prior schema-level interactions related to relations by leveraging entity type information and introduce schema-guided negatives to bolster the efficiency of normal contrastive representation learning. |
| Outcome: | The proposed method achieves state-of-the-art performance on multiple established metrics across multiple datasets for link prediction. |
VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)
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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou
| Challenge: | Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges. |
| Approach: | They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. |
| Outcome: | The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs. |
Fair Text-Attributed Graph Representation Learning (2025.findings-emnlp)
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| Challenge: | Text-Attributed Graphs (TAGs) inherit issues from Graph Neural Networks such as fairness. |
| Approach: | They propose to evolve LM-as-encoder to LM as-fair-encoding process to explore fairness in TAGRL. |
| Outcome: | The proposed process can be integrated with fairness-enhancing strategies on the GNNs decoder side. |