Papers by Yue Bai
Advancing Vision-Language Models with Adapter Ensemble Strategies (2024.findings-emnlp)
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| Challenge: | CLIP revolutes vision-language pretraining by using contrastive learning on paired web data. |
| Approach: | They propose to combine a "adapter ensemble" with traditional machine learning techniques to augment large-scale pretrained vision-language models. |
| Outcome: | The proposed model outperforms baselines and derives improvement when the number of ensemble parameters increases. |
MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026.acl-long)
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Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, Xiao-Ming Wu
| Challenge: | Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning. |
| Approach: | They propose a multimodal scientific dataset and benchmark curated from open-access publications. |
| Outcome: | MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers. |
Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction (2026.findings-acl)
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Yulin Chen, Haoran Li, Yuan Sui, Yue Liu, Yufei He, Xiaoling Bai, Chi Fei, Li Yabo, Haozhe Ma, Yangqiu Song, Bryan Hooi
| Challenge: | Prompt injection attacks manipulate large language models (LLMs) by misleading them to deviate from the original input instructions and execute maliciously injected instructions. |
| Approach: | They propose a prompt injection defense method that suppresses the model's instruction-following tendencies rather than suppressing them. |
| Outcome: | The proposed method outperforms prompt-engineering-based approaches and fine-tuning methods and reduces the ASR to nearly 0% in some scenarios. |
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)
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Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin
| Challenge: | Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding . |
| Approach: | They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning. |
| Outcome: | The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS. |
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale (2025.acl-long)
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Jiawei Guo, Tianyu Zheng, Yizhi Li, Yuelin Bai, Bo Li, Yubo Wang, King Zhu, Graham Neubig, Wenhu Chen, Xiang Yue
| Challenge: | Current instruction-tuning datasets focus on simplistic visual question answering tasks, and provide phrase-level answers without any intermediate rationales. |
| Approach: | They propose to use open-source multimodal large language models to train MLLMs on a dataset with 12M instruction-response pairs to elicit CoT reasoning. |
| Outcome: | The proposed model achieves state-of-the-art performance on benchmarks such as MathVerse, MMMU-Pro, and MuirBench, and gains improvements of up to 4% on non-reasoning-based benchmarks. |
Probing Audio-Visual Reasoning in Multimodal Language Models through the Lens of Audio (2026.acl-long)
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Kaixiong Gong, Kaituo Feng, Bohao Li, Yibing Wang, Mofan Cheng, Shijia Yang, Jiaming Han, Benyou Wang, Yutong Bai, Zhuoran Yang, Xiangyu Yue
| Challenge: | Recent multimodal large language models lack robust audio-visual integration ability and performance on DeafTest is highly correlated with AV-Odyssey accuracy. |
| Approach: | They propose a benchmarking tool that integrates audio-visual reasoning with audio-video cues to infer solutions. |
| Outcome: | The proposed model performs well on DeafTest, but lacks audio perception in simple audio tasks. |
Exploiting Abstract Meaning Representation for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Existing work attempts to address these challenges using Pretrained Language Models (PLMs) but the diversity of surface form expressions can hinder the model’s ability to capture accurate correlations, especially when the context is lengthy and complex. |
| Approach: | They propose a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs to assist the model in understanding complex semantic information. |
| Outcome: | The proposed method outperforms existing methods and significantly improves performance on both Natural Questions and TriviaQA. |
Cautious Next Token Prediction (2025.findings-acl)
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Yizhou Wang, Lingzhi Zhang, Yue Bai, Mang Tik Chiu, Zhengmian Hu, Mingyuan Zhang, Qihua Dong, Yu Yin, Sohrab Amirghodsi, Yun Fu
| Challenge: | Existing methods for decoding autoregressive models are temperature scaling and nucleus sampling to balance diversity and coherence. |
| Approach: | They propose a training-free decoding strategy that uses a model with a low perplexity score to select the trial with the lowest perplexities as the most probable and reliable path. |
| Outcome: | The proposed approach outperforms existing standard decoding strategies consistently by a clear margin. |
Cross-domain Generalization for AMR Parsing (2022.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. |
| Approach: | They evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain parsing. |
| Outcome: | The proposed method reduces the domain distribution divergence of text and AMR features on two out-of-domain sets. |
Semantic-based Pre-training for Dialogue Understanding (2022.coling-1)
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| Challenge: | Pre-trained language models are weak in understanding the main semantic meaning of a dialogue context. |
| Approach: | They propose a semantic-based framework that leverages explicit semantic knowledge to capture the core semantic information in dialogues during pre-training. |
| Outcome: | The proposed model is superior to existing models on chit-chats and task-oriented dialogues. |
FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)
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Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang
| Challenge: | FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Approach: | They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Outcome: | The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download. |
Online Back-Parsing for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) is a semantic graph representation that abstracts meaning away from a sentence. |
| Approach: | They propose a decoder that back predicts projected AMR graphs on target sentences . their results show superiority over previous state-of-the-art decoded graph Transformer . |
| Outcome: | The proposed model outperforms the state-of-the-art model on two AMR benchmarks. |
Improving Chinese Pop Song and Hokkien Gezi Opera Singing Voice Synthesis by Enhancing Local Modeling (2023.emnlp-main)
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| Challenge: | Singing Voice Synthesis (SVS) synthesizes pleasing vocals based on music scores and lyrics . current acoustic models ignore the significance of local modeling within the sequence and the hard-to-synthesize parts in the predicted mel-spectrogram . |
| Approach: | They propose a method to enhance local modeling in the acoustic model by focusing on phoneme tokens located before and after the phoneme. |
| Outcome: | The proposed method improves local modeling in the acoustic model by focusing on the hard-to-synthesize parts of the predicted mel-spectrogram. |
Explain the Synth: Interpretable Evaluation of LLM Data Synthesis (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly used to generate tabular data. |
| Approach: | They propose a framework that uses a rule-based model as a shared explanatory language to examine the explanation of real versus synthetic data. |
| Outcome: | The proposed framework compares the explanatory structure induced by real versus synthetic data. |
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
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| Challenge: | Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. |
| Approach: | They propose two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-tuning to improve structure awareness. |
| Outcome: | The proposed model is superior to pre-trained language models on AMR parsing and AMR-to-text generation tasks. |
Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation (2024.acl-long)
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| Challenge: | Abductive reasoning is the process of making educated guesses to provide explanations for observations. |
| Approach: | They propose a task of complex logical hypothesis generation to generate a complex logique hypothesis that can explain a set of observations. |
| Outcome: | The proposed model generates logical hypotheses closer to the reference hypothesis, but not better on unseen observations. |
Semantic Representation for Dialogue Modeling (2021.acl-long)
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| Challenge: | Existing models for dialogue modeling lack ability to represent core semantics, such as ignoring important entities. |
| Approach: | They develop an algorithm to construct dialogue-level AMR graphs from sentence-level data and explore two ways to incorporate AMRs into dialogue modeling. |
| Outcome: | The proposed model is superior to existing models on dialogue understanding and response generation tasks. |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)
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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang
| Challenge: | Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing. |
| Approach: | They propose a cross-lingual conversation summarization benchmark that explicitly considers source context. |
| Outcome: | The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations. |