Papers
Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief Propagation
Nakyung Lee, Yeongoon Kim, Minhae Oh et al.
Mitigating Biases in Language Models via Bias Unlearning
Dianqing Liu, Yi Liu, Guoqing Jin et al.
Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning
Wei Huang, Anda Cheng, Yinggui Wang
Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs
Kangda Wei, Hasnat Md Abdullah, Ruihong Huang
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
Shengyuan Wang, Jie Feng, Tianhui Liu et al.
Mitigating Hallucination in Large Vision-Language Models through Aligning Attention Distribution to Information Flow
Jianfei Zhao, Feng Zhang, Xin Sun et al.
Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations
Yifan Lu, Ziqi Zhang, Chunfeng Yuan et al.
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization
Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang et al.
Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets
Chenlin Liu, Minghui Fang, Patrick Zhang et al.
Mitigating Hallucinations in Vision-Language Models through Image-Guided Head Suppression
Sreetama Sarkar, Yue Che, Alex Gavin et al.
Mitigating Interviewer Bias in Multimodal Depression Detection: An Approach with Adversarial Learning and Contextual Positional Encoding
Enshi Zhang, Christian Poellabauer
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations
Shuo Li, Jiajun Sun, Guodong Zheng et al.
Mitigating Sequential Dependencies: A Survey of Algorithms and Systems for Generation-Refinement Frameworks in Autoregressive Models
Yunhai Hu, Zining Liu, Zhenyuan Dong et al.
Mitigating Spurious Correlations via Counterfactual Contrastive Learning
Fengxiang Cheng, Chuan Zhou, Xiang Li et al.
Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data
Shenglai Zeng, Jiankun Zhang, Pengfei He et al.
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent
Junda Wu, Yuxin Xiong, Xintong Li et al.
Mixed Signals: Decoding VLMs’ Reasoning and Underlying Bias in Vision-Language Conflict
Pouya Pezeshkpour, Moin Aminnaseri, Estevam Hruschka
Mixing Inference-time Experts for Enhancing LLM Reasoning
Soumya Sanyal, Tianyi Xiao, Xiang Ren
MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora
Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang et al.
Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning
Sugyeong Eo, Jung Jun Lee, Chanjun Park et al.
Mixture of Languages: Improved Multilingual Encoders Through Language Grouping
João Maria Janeiro, Belen Alastruey, Francisco Massa et al.
Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning
Enjun Du, Siyi Liu, Yongqi Zhang
Mixture of LoRA Experts for Continual Information Extraction with LLMs
Zitao Wang, Xinyi Wang, Wei Hu
Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization
Guanghui Song, Dongping Liao, Yiren Zhao et al.
MKT: A Multi-Stage Knowledge Transfer Framework to Mitigate Catastrophic Forgetting in Multi-Domain Chinese Spelling Correction
Peng Xing, Yinghui Li, Shirong Ma et al.