Papers with multimodal LLM
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR). |
| Approach: | They propose a multimodal LLM to receive source speech as extra input and reformat it as a cloze test with logits calibration to remove input information redundancy and simplify GER with clear instructions. |
| Outcome: | The proposed model improves on 9 popular ASR datasets and is faster than vanilla GER. |
STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding (2026.findings-acl)
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Hongwang Xiao, Wenjun Lin, Xi Chen, Hui Wang, Kai Chen, Jiashan Li, Yuancheng Sun, Sicheng Dai, Boya Wu, Qiwei Ye
| Challenge: | a multimodal protein language model (LLM) integrates sequence, structure, and function into functional annotation. |
| Approach: | They propose a multimodal protein language model that synergistically aligns bimodal representations with the textual modality to advance protein functional annotation. |
| Outcome: | The proposed model synergizes bimodal representations with the textual modality to advance protein functional annotation. |
MAviS: A Multimodal Conversational Assistant For Avian Species (2025.emnlp-main)
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Yevheniia Kryklyvets, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly, Jinxing Zhou, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal
| Challenge: | Existing multimodal large language models face challenges when it comes to specialized topics like avian species. |
| Approach: | They propose a large-scale multimodal avian species dataset that integrates image, audio, and text modalities for over 1,000 bird species. |
| Outcome: | The proposed model outperforms the baseline MiniCPM-o-2.6 by a large margin. |