Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units (2025.emnlp-main)
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| Challenge: | Existing methods to enhance reasoning capabilities of language models are expensive and often lack the ability to perform complex reasoning tasks. |
| Approach: | They propose a token-level multi-model collaboration strategy to enhance reasoning capabilities in language models by selecting the optimal tokens from the next token distributions. |
| Outcome: | The proposed method is superior to existing methods and will be released soon. |
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| Challenge: | Existing approaches to collaboration between multiple Large Language Models (LLMs) rely on highly capable models with strong self-reflection abilities or are limited to models sharing the same tokenizer. |
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Multimodal large language models for inclusive collaboration learning tasks (2022.naacl-srw)
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| Challenge: | This project leverages advances in multimodal large language models to build an inclusive collaboration feedback loop for participants developing general collaboration skills. |
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| Challenge: | Multimodal large language models have shown remarkable performance for cross-modal understanding and generation, yet suffer from severe inference costs. |
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| Challenge: | Using a latent variable model, multiple large language models can be trained to collaborate at the token level. |
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| Challenge: | MultiModal Large Language Models (MM-LLMs) have undergone significant advances in the past year . traditional MM models incur substantial computational costs, especially when trained from scratch . |
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Generation with Dynamic Vocabulary (2024.emnlp-main)
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| Challenge: | Using static vocabulary, vocabulary is ignored in advanced generation tasks. |
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| Challenge: | This tutorial presents foundations, agentic capabilities, and inclusive applications of multimodal large language models. |
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XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models (2023.emnlp-main)
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Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa
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| Challenge: | unified multimodal large language models (MLLMs) are emerging but lack a systematic framework to connect them and situate current trends within a broader landscape. |
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