Papers by Xiaoyu Fan
LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for adapting LLMs to streaming rely on expensive re-encoding or limited scalability. |
| Approach: | They propose a group position encoding paradigm built on batch architectures to enhance consistency between streaming and batch modes. |
| Outcome: | The proposed method outperforms existing methods on cross-lingual and cross-modal tasks. |
VisiPruner: Decoding Discontinuous Cross-Modal Dynamics for Efficient Multimodal LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal Large Language Models (MLLMs) suffer from significant computational overhead due to the quadratic growth of attention computations with the number of multimodal tokens. |
| Approach: | They propose a training-free pruning framework that prunes multimodal tokens without a trained pruning method. |
| Outcome: | The proposed pruning framework outperforms existing token pruning methods and generalizes across diverse MLLMs. |
CondenseFlow: Scalable Latent Space Collaboration via Semantic Compression for Multi-Agent Systems (2026.findings-acl)
Copied to clipboard
| Challenge: | Full-state latent communication in LLMs suffers from memory overhead scaling linearly with collaboration rounds. |
| Approach: | They propose a lightweight module that uses learnable semantic probes to compress KV caches into fixed-size representations. |
| Outcome: | The proposed module reduces KV cache memory by over 99% and inference latency by approximately 20% on seven benchmarks spanning six models . it outperforms text-based methods by 1.7 percentage points on average across all configurations while outperforming existing methods by 1.7%. |
Does Chain-of-Thought Reasoning Really Reduce Harmfulness from Jailbreaking? (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing jailbreak attacks fail against reasoning models enhanced by Chain-of-Thought (CoT) reasoning. |
| Approach: | They propose a jailbreak method that uses Chain-of-Thought reasoning to reduce harmfulness from jailbreaking. |
| Outcome: | The proposed jailbreak method performs well against open AI models and deepseek-R1 reasoning models. |
Multimodal Language Models See Better When They Look Shallower (2025.emnlp-main)
Copied to clipboard
Haoran Chen, Junyan Lin, Xinghao Chen, Yue Fan, Jianfeng Dong, Xin Jin, Hui Su, Jinlan Fu, Xiaoyu Shen
| Challenge: | Existing studies show that multimodal large language models extract visual features from the final layers of a pretrained Vision Transformer. |
| Approach: | They propose a feature fusion method that strategically incorporates shallower layers . they propose MLLMs that extract visual features from the final layers of a pretrained Vision Transformer . |
| Outcome: | The proposed method outperforms deep layers on fine-grained visual tasks . it is the first comprehensive study of visual layer selection for MLLMs . |
Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Fine-tuning and in-context learning are two prevalent methods in imbuing large language models with task-specific knowledge. |
| Approach: | They propose to use a circuit shift theory to explain why in-context learning is superior to fine-tuning for tasks with implicit patterns. |
| Outcome: | The proposed method can grasp deep patterns and significantly improve accuracy on implicit patterns, compared with fine-tuning and in-context learning. |
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation (2022.naacl-main)
Copied to clipboard
David Adelani, Jesujoba Alabi, Angela Fan, Julia Kreutzer, Xiaoyu Shen, Machel Reid, Dana Ruiter, Dietrich Klakow, Peter Nabende, Ernie Chang, Tajuddeen Gwadabe, Freshia Sackey, Bonaventure F. P. Dossou, Chris Emezue, Colin Leong, Michael Beukman, Shamsuddeen Muhammad, Guyo Jarso, Oreen Yousuf, Andre Niyongabo Rubungo, Gilles Hacheme, Eric Peter Wairagala, Muhammad Umair Nasir, Benjamin Ajibade, Tunde Ajayi, Yvonne Gitau, Jade Abbott, Mohamed Ahmed, Millicent Ochieng, Anuoluwapo Aremu, Perez Ogayo, Jonathan Mukiibi, Fatoumata Ouoba Kabore, Godson Kalipe, Derguene Mbaye, Allahsera Auguste Tapo, Victoire Memdjokam Koagne, Edwin Munkoh-Buabeng, Valencia Wagner, Idris Abdulmumin, Ayodele Awokoya, Happy Buzaaba, Blessing Sibanda, Andiswa Bukula, Sam Manthalu
| Challenge: | Low-resource languages are left out of large-scale pretraining datasets . authors explore how to leverage existing pre-trained models to create low-resourced translation systems for 16 African languages. |
| Approach: | They investigate how large-scale pre-trained models can be used to create low-resource translation systems for 16 African languages. |
| Outcome: | The proposed models can translate between hundreds of languages even though there is little parallel data available for training. |