Papers by Kaikai An
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation (2025.emnlp-main)
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Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Baobao Chang
| Challenge: | Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions. |
| Approach: | They propose a data organization paradigm where large language models transform documents into more structured and loosely interconnected LUs. |
| Outcome: | Experiments in open-domain and industrial settings show that the proposed paradigm outperforms existing paradigms and shows high adaptability across diverse document formats. |
LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation? (2024.naacl-long)
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| Challenge: | Existing models for text-to-image generation have been underperforming in image-totext generation tasks. |
| Approach: | They propose a framework that uses a split BERT to create a dedicated latent space for captions and integrates a regularization module to manage varying text lengths. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the MS COCO dataset with 38.2 BLEU@4 and 126.2 CIDEr . |
Coarse-to-Fine Dual Encoders are Better Frame Identification Learners (2023.findings-emnlp)
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| Challenge: | Recent efforts to model frame definitions lack sufficient representation learning of definitions or lack efficient frame modeling. |
| Approach: | They propose a frame-target-encoder architecture that uses coarse-to-fine learning to model alignment between frames and targets. |
| Outcome: | The proposed framework outperforms existing models by 0.93 overall scores and 1.53 R@1 without lf. |
Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints (2025.acl-short)
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| Challenge: | Semantic Parsing improves performance of smaller models, but it is unclear whether it extends similarly to large language models. |
| Approach: | They propose a prompting approach that embeds semantic hints within the prompt to improve LLM performance. |
| Outcome: | The proposed approach improves LLMs’ performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities. |
UltraIF: Advancing Instruction Following from the Wild (2025.emnlp-main)
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| Challenge: | a lack of transparency has resulted in a gap between research community and leading companies . large language models have demonstrated remarkable capabilities in following complex instructions . |
| Approach: | They propose a method to build large language models that can follow complex instructions with open-source data. |
| Outcome: | The proposed approach can synergize complex instructions and filter responses with evaluation questions. |
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025.acl-long)
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Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun
| Challenge: | Existing studies show that training LLMs on data containing unfamiliar knowledge during instruction tuning can encourage hallucinations. |
| Approach: | They propose a framework that measures how familiar the LLM is with instruction data and introduce an expert-aligned reward model to ensure the quality of selected samples. |
| Outcome: | The proposed framework reduces hallucinations while maintaining a competitive ability to follow instructions. |
Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation (2024.naacl-long)
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| Challenge: | Large-scale multilingual pretrained language models (mPLMs) yield impressive performance on cross-language tasks, yet significant performance disparities exist across different languages within the same mPLm. |
| Approach: | They propose to leverage the learned knowledge from well-performing languages to guide under-performing ones within the same mPLM. |
| Outcome: | The proposed model shows that it can guide under-performing languages while minimizing language-level performance disparities across different mPLMs. |
Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning (2024.findings-acl)
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| Challenge: | Named Entity Recognition (NER) methods require a substantial quantity of high-quality annotation for training models. |
| Approach: | They propose a method to reduce the number of incorrect pseudo labels in self-training . they propose 'uncertainty-aware teacher learning' and 'student-student collaboration' |
| Outcome: | The proposed method is superior to state-of-the-art DS-NER denoising methods. |
GATEAU: Selecting Influential Samples for Long Context Alignment (2025.emnlp-main)
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Shuzheng Si, Haozhe Zhao, Gang Chen, Yunshui Li, Kangyang Luo, Chuancheng Lv, Kaikai An, Fanchao Qi, Baobao Chang, Maosong Sun
| Challenge: | Existing studies have attempted to scale up the available data volume by synthesizing long instruction-following samples, but a lack of a well-defined strategy for ensuring data quality may introduce low-quality samples and restrict the model’s performance. |
| Approach: | They propose a framework to identify influential samples enriched with long-range dependency relations that can be used to align large language models to handle instructions with extremely long contexts. |
| Outcome: | The proposed framework identifies samples with long-range dependency relations and shows that the model trained on these samples exhibits better instruction-following and long-context understanding capabilities. |