FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Unlike other modalities, speech has unique temporal dependencies, making efficient inference methods unexplored. |
| Approach: | They propose a weighted token merging framework specifically designed for speech-related tasks to improve the trade-off between efficiency and performance. |
| Outcome: | The proposed method achieves state-of-the-art efficiency-performance trade-off on speech-related tasks. |
Similar Papers
MERaLiON-AudioLLM: Advancing Speech and Language Understanding for Singapore (2025.acl-demo)
Copied to clipboard
Yingxu He, Zhuohan Liu, Geyu Lin, Shuo Sun, Bin Wang, Wenyu Zhang, Xunlong Zou, Nancy F. Chen, AiTi Aw
| Challenge: | MERaLiON-AudioLLM is the first general-purpose audio-based large language model for multitask learning. |
| Approach: | They introduce MERaLiON-AudioLLM, a general-purpose audio-based large language model for multitask learning with a focus on Singlish understanding. |
| Outcome: | The proposed model exhibits strong generalization across a diverse set of tasks . it is a leading solution for region-specific AI applications. |
Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects (2026.findings-acl)
Copied to clipboard
Jun Zhang, Yicheng Ji, Feiyang Ren, Yihang Li, Bowen Zeng, Zonghao Chen, Ke Chen, Lidan Shou, Gang Chen, Huan Li
| Challenge: | Large Vision-Language Models are hindered by a systemic efficiency barrier known as visual token dominance. |
| Approach: | They propose a systematic taxonomy of efficiency techniques structured around the inference lifecycle . they examine visual encoding, prefilling, and decoding to understand bottlenecks . |
| Outcome: | The proposed techniques reveal how upstream decisions dictate downstream bottlenecks . the proposed techniques include hybrid compression and modality-aware decoding . |
Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem? (2025.findings-acl)
Copied to clipboard
| Challenge: | Multimodal large language models have shown remarkable performance for cross-modal understanding and generation, yet suffer from severe inference costs. |
| Approach: | They propose to prune redundant tokens in MLLMs to reduce computation and storage costs. |
| Outcome: | The proposed method reduces the computational and storage costs of MLLMs by identifying redundant tokens and pruning them. |
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
Copied to clipboard
Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
LVPruning: An Effective yet Simple Language-Guided Vision Token Pruning Approach for Multi-modal Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Multi-modal Large Language Models (MLLMs) incur significant computational overhead due to the large number of vision tokens processed, limiting their practicality in resource-constrained environments. |
| Approach: | They propose a language-guided vision token pruning method that can be integrated into existing MLLMs with minimal architectural changes. |
| Outcome: | The proposed method reduces vision tokens by 90% and preserves model performance. |
AdaptMerge: Inference Time Adaptive Visual and Language-Guided Token Merging for Efficient Large Multimodal Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing token reduction methods ignore image complexity and vision-language interactions, ignoring image complexity. |
| Approach: | They propose a training-free, inference-time token merging strategy that adaptively reduces visual tokens by leveraging feature diversity and language-guided relevance. |
| Outcome: | The proposed approach outperforms state-of-the-art token reduction methods on Google’s Gemma 3 models while achieving reduced computational costs and improved performance. |
RedApt: An Adaptor for wav2vec 2 EncodingFaster and Smaller Speech Translation without Quality Compromise (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained speech Transformers in speech translation systems have facilitated state-of-the-art (SotA) results, but their computational cost is high. |
| Approach: | They propose a Reducer Adaptor block that could be seamlessly integrated within any Transformer-based speech encoding architecture. |
| Outcome: | The proposed Reducer Adaptor block outperforms the existing SotA architecture by an average of 0.68 BLEU score on 8 language pairs from Must-C. |
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for accelerating Large Vision-Language Models lack comprehensive evaluation across diverse backbones, benchmarks, and metrics. |
| Approach: | They propose EffiVLM-BENCH framework for evaluating absolute performance and generalization and loyalty. |
| Outcome: | The proposed framework offers insights into optimal strategies for accelerating LVLMs. |
Massive End-to-end Speech Recognition Models with Time Reduction (2024.naacl-long)
Copied to clipboard
Weiran Wang, Rohit Prabhavalkar, Haozhe Shan, Zhong Meng, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Chengjian Zheng, Yanzhang He, Tara Sainath, Pedro Moreno Mengibar
| Challenge: | Using the neural architecture of Google’s universal speech model, we reduce the frame rate and speed up training and inference. |
| Approach: | They propose to use the neural architecture of Google’s universal speech model with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. |
| Outcome: | The proposed methods work with both connectionist temporal classification (CTC) and RNN-Transducer (RNN-T) and over two domains. |
LoPT: Lossless Parallel Tokenization Acceleration for Long Context Inference of Large Language Model (2026.acl-long)
Copied to clipboard
| Challenge: | Existing parallel tokenization methods suffer from inconsistent results due to boundary artifacts that occur after merging. |
| Approach: | They propose a Lossless Parallel Tokenization framework that ensures output identical to standard sequential tokenization. |
| Outcome: | The proposed method achieves significant speedup while guaranteeing lossless tokenization. |