Papers by Yifan Jin
A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025.acl-long)
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Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang
| Challenge: | achieving data-efficient post-training of Large Language Models is a key research question. |
| Approach: | They propose a taxonomy of data-efficient LLM post-training methods from a data-centric perspective. |
| Outcome: | The proposed methods cover data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems. |
MEEL: Multi-Modal Event Evolution Learning (2024.findings-acl)
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| Challenge: | Existing models fail to grasp the principles governing event evolution in various scenarios. |
| Approach: | They propose a multi-modal event evolution learning approach to grasp event evolution . they propose an instruction encapsulation process that transforms evolving graphs into instruction-tuning data . |
| Outcome: | The proposed model grasps the event evolution mechanism yielding advanced MMER ability. |
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)
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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen
| Challenge: | Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness. |
| Approach: | They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio. |
| Outcome: | The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model . |
Finite State Automata Inside Transformers with Chain-of-Thought: A Mechanistic Study on State Tracking (2025.acl-long)
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| Challenge: | Existing studies show that Chain-of-thought (CoT) can enhance the performance of large language models (LLMs) however, there is limited understanding of the algorithms that Transformer+CoT can learn. |
| Approach: | They propose two metrics to evaluate Transformer+CoT's state tracking capabilities and identify the circuit responsible for tracking the world state. |
| Outcome: | The proposed model achieves 100% accuracy for each state, highlighting an implicit finite state automaton (FSA) embedded within the model. |
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning (2022.emnlp-main)
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| Challenge: | Prefix-tuning is an essential paradigm of parameter-efficient transfer learning . fine-tuned models require separate copies of model parameters for each task . |
| Approach: | They propose to understand and further develop prefix-tuning through the kernel lens . they propose a new variant of prefix tuning that shares the exact mechanism as prefix tun . |
| Outcome: | The proposed method improves prefix-tuning performance by training only a small portion of parameters. |
ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)
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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu
| Challenge: | Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem . |
| Approach: | They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths . |
| Outcome: | The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines . |
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention (2022.findings-naacl)
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| Challenge: | Existing methods to fine-tune pre-trained language models are parameter efficient . fine- tuning the models requires multiple copies of the parameters, which is inefficient. |
| Approach: | They propose to use kernel-based adapters to tune only a few parameters while freezing the rest of the parameters. |
| Outcome: | The proposed methods achieve or improve strong performance over a diverse set of natural language generation and understanding tasks. |
Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences (2022.naacl-main)
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| Challenge: | Existing models for long sequences are not efficient due to the quadratic space and time complexity of the self-attention modules. |
| Approach: | They propose to reduce the quadratic complexity to linear (modulo logarithmic factors) by low-dimensional projection and row selection. |
| Outcome: | The proposed methods outperform transformer-based models with smaller time/space footprint on the Long Range Arena benchmark. |
Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)
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Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang
| Challenge: | Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality . |
| Approach: | They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting. |
| Outcome: | The proposed model outperforms existing models on comprehensive metrics. |
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks (2026.findings-acl)
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Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng
| Challenge: | Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. |
| Approach: | They propose a framework that retrieves context from a memory bank to enhance alignment . they propose EMI-based approach to align vision and language models . |
| Outcome: | The proposed framework reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead. |
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest (2022.coling-1)
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| Challenge: | Medical relation extraction (MRE) tasks aims to extract relations between entities in medical literature. |
| Approach: | They propose to combine semantic and syntactic information from medical texts by using causal explanation theory. |
| Outcome: | Empirically, the proposed model outperforms existing methods on benchmark medical datasets. |