Papers with task-specific approaches
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to selective prediction are not consistently outperform the simplest baseline MaxProb in all three settings. |
| Approach: | They propose to use a large-scale setup of 17 datasets to study selective prediction in NLP tasks using in-domain, out-of-domain and adversarial settings. |
| Outcome: | The proposed approaches outperform the simplest baseline MaxProb in in-domain, out-of-domain and adversarial settings, but none consistently outperformed in all three settings. |
Chain of Thought Prompting Elicits Knowledge Augmentation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge augmentation methods require retrieving knowledge from external knowledge sources or developing a reasoner to leverage the logical rules within the external knowledge source. |
| Approach: | They propose a Chain-of-Thought-based method that augments knowledge for deep learning by removing the need for additional knowledge retrieval or knowledge reasoning models. |
| Outcome: | The proposed method outperforms both pure CoT-based methods and the non-augmented method across the majority of 11 publicly available benchmarks for various reasoning tasks. |
Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends (2024.acl-long)
Copied to clipboard
| Challenge: | Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. |
| Approach: | They propose a pipeline that trains a state-of-the-art Coreference Resolution system within the constraints of an academic budget and trains with up to 0.006x the memory resources. |
| Outcome: | The proposed framework outperforms encoder-based discriminative systems on the CoNLL-2012 benchmark, training with up to 0.006x the memory resources and obtaining 170x faster inference compared to previous state-of-the-art systems. |