Papers with latency requirements
Prompt-Tuned Muti-Task Taxonomic Transformer (PTMTTaxoFormer) (2024.emnlp-industry)
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| Challenge: | Existing methods for Hierarchical Text Classification (HTC) are expensive and require explicit injection of the hierarchy, verbalizers, and/or prompt engineering. |
| Approach: | They propose a hierarchical text classification system that uses a single classifier to predict one or more topics using differentiable prompts and labels that are learnt through backpropagation. |
| Outcome: | The proposed model outperforms existing models on several benchmarks that span a range of topics consistently. |
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning (2025.findings-acl)
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Shivam Shandilya, Menglin Xia, Supriyo Ghosh, Huiqiang Jiang, Jue Zhang, Qianhui Wu, Victor Rühle, Saravan Rajmohan
| Challenge: | Existing prompt compression techniques rely on sub-optimal metrics such as information entropy or model it as a task-agnostic token classification problem that fails to capture task-specific information. |
| Approach: | They propose a task-aware prompt compression method that leverages existing Transformer encoders and a lightweight REINFORCE algorithm to ensure low latency requirements. |
| Outcome: | The proposed method improves task performance by 8% - 189% on three diverse and challenging tasks over state-of-the-art techniques while satisfying the same compression rate and latency requirements. |