Papers by Chunyang Jiang
BLM-AgrF: A New French Benchmark to Investigate Generalization of Agreement in Neural Networks (2023.eacl-main)
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| Challenge: | Existing benchmarks for deep learning are based on massive amounts of data, which are effective in hiding some of the shallowness of the learned models. |
| Approach: | They propose to use a French dataset to learn the underlying rules of subject-verb agreement in sentences, inspired by visual IQ tests known as Raven’s Progressive Matrices. |
| Outcome: | The proposed method is based on Raven’s Progressive Matrices, a visual IQ test, and a dataset built using the BLM framework. |
Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction (2026.eacl-long)
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| Challenge: | Recent systems that use analogical reasoning require extensive knowledge engineering and even transformer-based models show inconsistent results across complexity levels. |
| Approach: | They propose to implement analogical structure, contrastive learning, and minimal contextual cue principles into large language models that train on English verb alternations. |
| Outcome: | The proposed models learn the alternation rules with high F1 on English verb alternations. |
Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)
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Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo
| Challenge: | Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model. |
| Approach: | They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering. |
| Outcome: | The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies. |
Benchmarking Fine-Grained Error Detection in Multimodal Reasoning (2026.acl-long)
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| Challenge: | Multimodal Process Reward Models (MPRMs) have emerged as a pivotal framework for enhancing the reasoning capabilities of Multimodal Large Language Models. |
| Approach: | They propose a benchmark specifically designed to evaluate MPRMs’ proficiency in detecting erroneous reasoning steps across diverse error categories. |
| Outcome: | The proposed model achieves up to 4.8% performance improvement through test-time scaling. |
Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2023.eacl-main)
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| Challenge: | Multi-hop reasoning is a common approach for query answering, but can be biased to spurious paths which coincidentally lead to the correct answer with poor explanation. |
| Approach: | They propose a method that quantitatively estimates to what extent a path is spurious by a metric called Path Spuriousness (PS) they propose KG reasoning, which infers new facts along existing paths in KGs. |
| Outcome: | The proposed model significantly improves the agent’s ability to prevent spurious paths while keeping comparable to state-of-the-art performance. |
BLM-s/lE: A structured dataset of English spray-load verb alternations for testing generalization in LLMs (2023.findings-emnlp)
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| Challenge: | Current NLP models are achieving performance comparable to human capabilities on well-established benchmarks. |
| Approach: | They propose a BLM task to identify a missing element in a linguistic pattern from a list of candidate options based on a given matrix. |
| Outcome: | The proposed framework is based on the spray-load verb alternations in English as a case study. |
Graceful Forgetting in Generative Language Models (2025.emnlp-main)
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| Challenge: | Recent studies show that pre-trained models do not provide all knowledge needed for fine-tuning tasks. |
| Approach: | They propose a framework to achieve graceful forgetting in generative language models by pre-training a model on large-scale correlating datasets. |
| Outcome: | The proposed framework improves the learning plasticity of the target task by selectively discarding irrelevant knowledge. |