Papers by Zhiyuan Liao
GROLE: Instance-Level Group Relative Optimization for LoRA Experts in Incremental Learning (2026.findings-acl)
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Yongyi Liao, Wencan Lai, Jun Fang, Jinjin Guo, Xiaohui Zhang, Zhiyuan Liu, Chao Liu, Pengzhang Liu, Qixia Jiang
| Challenge: | Large language models demonstrate remarkable zero-shot generalization, but adapting to downstream tasks requires continual fine-tuning. |
| Approach: | They propose a method that incrementally constructs a pool of frozen, task-specific LoRA experts. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in task-free and blurred-boundary settings. |
RA2FD: Distilling Faithfulness into Efficient Dialogue Systems (2024.emnlp-main)
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| Challenge: | Retrieval Augmented Generation (RAG) is effective but inference inefficient, while Retrieral Free Generations (RFG) are more efficient but sacrifice faithfulness. |
| Approach: | They propose a retrieval-free model training scheme that uses a teacher-student framework to distill the faithfulness capacity of a student's knowledge-infused responses. |
| Outcome: | The proposed model surpasses the previous SOTA RFG model on knowledge-grounded dialogue datasets by an average of 33% while improving inference efficiency. |
MiLiC-Eval: Benchmarking Multilingual LLMs for China’s Minority Languages (2025.findings-acl)
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| Challenge: | Large language models excel in high-resource languages but struggle with low-resourced languages . minority languages such as Tibetan, Uyghur, Kazakh, and Mongolian are marginalized in NLP research due to limited digital representation and the scarcity of training data. |
| Approach: | They propose a benchmark for minority languages in China that tracks the progress of large language models on low-resource languages. |
| Outcome: | The proposed benchmark focuses on underrepresented writing systems and syntax-intensive tasks. |
EvolveBench: A Comprehensive Benchmark for Assessing Temporal Awareness in LLMs on Evolving Knowledge (2025.acl-long)
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| Challenge: | Existing studies have explored how LLMs perceive time, but they often overlook the critical aspect of knowledge utilization. |
| Approach: | They propose a benchmark that evaluates temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness and reasoning. |
| Outcome: | EvolveBench measures temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness, Understanding and reasoning. |
Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion (2026.acl-long)
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| Challenge: | Proxy Tuning offers a logit-level strategy for introducing scaling effects, but it often fails in LRL settings because the large model’s weak LRL competence might overwhelm the knowledge of specialized smaller models. |
| Approach: | They propose a logit-based framework that balances LRL competence from a continually pretrained small model, task competence from high-resource language instruction tuning, and the scaling benefits of large models. |
| Outcome: | Experiments across four model families and eight LRLs show that TriMix outperforms single-model baselines and Proxy Tuning. |
HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks (2026.findings-acl)
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| Challenge: | Medical large vision-language models suffer from factual inaccuracies and unreliable outputs. |
| Approach: | They propose a framework that enhances Med-LVLMs through heterogeneous knowledge sources. |
| Outcome: | The proposed framework improves Med-LVLMs through heterogeneous knowledge sources. |
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon (2025.acl-short)
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| Challenge: | Existing studies evaluate whether large language models handle global cultural diversity . however, mechanisms behind cultural knowledge acquisition remain unexplored . |
| Approach: | They propose an interpretable framework to study cultural knowledge transfer in large language models . they observe bidirectional cultural transfer between English and other high-resource languages . |
| Outcome: | The proposed framework ensures training data transparency and controls transfer effects. |