Papers by Botian Jiang
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model (2025.findings-naacl)
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Zhaowei Li, Wei Wang, YiQing Cai, Qi Xu, Pengyu Wang, Dong Zhang, Hang Song, Botian Jiang, Zhida Huang, Tao Wang
| Challenge: | Representative models like LLaVA and MiniGPT-4 have great capabilities in various tasks. |
| Approach: | They propose a unified model to represent various multi-modal tasks using a single representation. |
| Outcome: | The proposed model outperforms existing models in a variety of tasks while maintaining generality and scalability. |
SeqXGPT: Sentence-Level AI-Generated Text Detection (2023.emnlp-main)
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| Challenge: | Existing methods for sentence-level AIGT detection are weak . large language models (LLMs) can generate human-like content . |
| Approach: | They propose a sentence-level AIGT detection challenge using LLMs as log probability lists . they propose 'check' GPT' method that uses log probability list features to detect AIGT . |
| Outcome: | The proposed method surpasses baseline methods in sentence- and document-level detection challenges. |
WESR: A Benchmark and Strong Baseline for Word-level Event-Speech Recognition (2026.findings-acl)
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Chenchen Yang, Kexin Huang, Liwei Fan, Qian Tu, Botian Jiang, Dong Zhang, Linqi Yin, Shimin Li, Zhaoye Fei, Qinyuan Cheng, Xipeng Qiu
| Challenge: | aaron carroll: the precise localization of non-verbal vocal events remains a critical yet under-explored challenge. carroll says current methods suffer from insufficient task definitions with limited category coverage. carrol: knowing exactly where an event occurred is not enough; knowing exactly what it happened is. |
| Approach: | They propose a taxonomy of 21 vocal events with a new categorization into discrete versus continuous types. |
| Outcome: | The proposed model disentangles ASR errors from event detection while maintaining ASR quality. |
Watermarking LLMs with Weight Quantization (2023.findings-emnlp)
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| Challenge: | Large language models are being deployed at an astonishing speed, exposing users to high risks. |
| Approach: | They propose a method that plants watermarks in quantization process of large language models without pre-defined triggers during inference. |
| Outcome: | The proposed method protects model weights without pre-defined triggers . it works when the model is used in the fp32 mode and remains hidden when the models are quantized to int8 . |
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance (2024.emnlp-main)
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Pengyu Wang, Dong Zhang, Linyang Li, Chenkun Tan, Xinghao Wang, Mozhi Zhang, Ke Ren, Botian Jiang, Xipeng Qiu
| Challenge: | Existing methods for enhancing harmlessness and helpfulness of large language models (LLMs) involve complex and resource-intensive training processes. |
| Approach: | They propose a method that decouples harmlessness from helpfulness during inference phase. |
| Outcome: | The proposed method significantly reduces the attack success rate (ASR) of harmful instructions and jailbreak instructions while maintaining almost unchanged performance in downstream tasks. |
Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models (2025.emnlp-main)
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Wei Wang, Zhaowei Li, Qi Xu, Linfeng Li, YiQing Cai, Botian Jiang, Hang Song, Xingcan Hu, Pengyu Wang, Li Xiao
| Challenge: | Recent advances in multi-modal large language models have demonstrated remarkable capabilities in multimodal understanding, reasoning, and interaction. |
| Approach: | They propose a method that effectively aligns and integrates multi-scale knowledge of objects . they use a pipeline that provides over 300K essential training data to enhance alignment . |
| Outcome: | The proposed method effectively aligns and integrates multi-scale knowledge of objects, including texts, coordinates, and images. |
Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLMs (2025.findings-emnlp)
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Chenkun Tan, Pengyu Wang, Shaojun Zhou, Botian Jiang, Zhaowei Li, Dong Zhang, Xinghao Wang, Yaqian Zhou, Xipeng Qiu
| Challenge: | Recent advances in multimodal large language models focus on improving performance . however, language prior conflict leads to suboptimal vision-language alignment . |
| Approach: | They propose a method to decouple the alignment process from language prior interference . they use a proxy LLM to detach from language interference during pretraining . |
| Outcome: | The proposed method improves training performance and generalizes training data. |
UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025.emnlp-main)
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Pengyu Wang, Shaojun Zhou, Chenkun Tan, Xinghao Wang, Wei Huang, Zhen Ye, Zhaowei Li, Botian Jiang, Dong Zhang, Xipeng Qiu
| Challenge: | Existing datasets address understanding and generation in isolation, limiting the performance of unified vision large language models. |
| Approach: | They propose a dataset that facilitates mutual enhancement between multimodal understanding and generation. |
| Outcome: | The proposed framework integrates diverse visual and textual inputs and outputs, enabling comprehensive cross-modal reasoning and precise text-to-image alignment. |