Papers with processing speed
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
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| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
In-Context Former: Lightning-fast Compressing Context for Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing methods to reduce inference costs of transformer-based large language models entail quadratic complexity . et al., 2017): transformer-derived large language model performance is a major challenge. |
| Approach: | They propose a method that compresses long contexts into short soft prompts . they use the self-attention mechanism of the large model to extract and condense information . |
| Outcome: | The proposed method reduces compression costs by 68 to 112 times while achieving 90% of baseline performance. |
GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment (2024.findings-acl)
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| Challenge: | Existing methods of acceleration require fine-tuning of considerably large models, such as Llama-7B, posing a challenge for average users. |
| Approach: | They propose a Guidance-based Knowledge Transfer framework that leverages a larger LLM as a 'teacher' and a smaller 'student' model to finalize responses. |
| Outcome: | The proposed framework achieves a maximum accuracy improvement of 14.18%, along with a 10.72 times speed-up on GSM8K and an accuracy improvement 14.00% along with 7.73 times speed up in CSQA. |
Visual Detection with Context for Document Layout Analysis (D19-1)
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| Challenge: | a challenge in scientific literature mining is the difficulty of extracting high-quality text from formatted PDFs. |
| Approach: | They propose a method to visually segment key regions of scientific articles using object detection augmented with contextual features. |
| Outcome: | The proposed method improves the accuracy of the proposed method and the speed of the dataset. |
Flashback: Memory Mechanism for Enhancing Memory Efficiency and Speed in Deep Sequential Models (2025.coling-main)
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| Challenge: | Existing deep sequential processing models have problems with memory degradation and inaccurate gradient backpropagation. |
| Approach: | They propose a Flashback property that preserves memory as an identity mapping until it is overwritten by a hidden state at a different time step. |
| Outcome: | The proposed model can be implemented in Transformers and Mamba, and it performs well. |
A Survey for Efficient Open Domain Question Answering (2023.acl-long)
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| Challenge: | Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP). |
| Approach: | They propose to use open domain question answering to answer factual questions from a large knowledge corpus without explicit evidence. |
| Outcome: | The proposed models can answer factoid questions from a large knowledge corpus without explicit evidence. |