Papers with computational bottleneck
Understanding and Improving Hidden Representations for Neural Machine Translation (N19-1)
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| Challenge: | Existing studies have explored some methods for understanding hidden representations, but they have not sought to improve the translation quality rationally according to their understanding. |
| Approach: | They propose to construct a sequence of nested relative tasks and measure the feature generalization ability of the learned hidden representation over these tasks. |
| Outcome: | The proposed methods achieve consistent improvements (up to +1.3 BLEU) on two widely-used datasets. |
Learning with Noise-Contrastive Estimation: Easing training by learning to scale (C18-1)
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| Challenge: | Neural language models have recently shown great improvement, but they share a common issue: large output vocabulary, computational time, and high dimensional space. |
| Approach: | They propose to make scaling factor a trainable parameter and use noise distribution to initialize output bias. |
| Outcome: | The proposed training strategies yield stable and competitive performances in small and large scale language modelling tasks. |
CrisPrune: Combining Contextual Relevance and Intrinsic Saliency for Efficient Visual Token Pruning in MLLMs (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning compromise the integrity of visual understanding in pursuit of efficiency. |
| Approach: | They propose a model-agnostic method that integrates visual saliency and text relevance to reconcile efficiency with understanding by integrating visual salions and text relevant. |
| Outcome: | The proposed method outperforms state-of-the-art methods on LLaVA-NeXT . it achieves 13 decrease in FLOPs while maintaining 97% of original performance . |
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)
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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin
| Challenge: | Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization. |
| Approach: | They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach. |
| Outcome: | The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks. |