Papers with selection method
Active Learning for Multilingual Semantic Parser (2023.findings-eacl)
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| Challenge: | Existing multilingual semantic parsing datasets are limited in translation effort due to data imbalance. |
| Approach: | They propose a first active learning procedure for multilingual semantic parsing (AL-MSP) it selects only a subset from existing datasets to be translated, they propose . |
| Outcome: | The proposed method significantly reduces translation costs with ideal selection methods. |
Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matching (2025.findings-acl)
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| Challenge: | In-Context Learning (ICL) empowers Large Language Models for rapid task adaptation without fine-tuning. |
| Approach: | They propose a method that aligns fine-tuning gradients between entire training set and selected examples to enable in-context learning and fine-uning. |
| Outcome: | The proposed method outperforms random selection on large LLMs from 4-shot to 128-shot scenarios across 9 datasets. |
Corpora of Typical Sentences (L18-1)
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| Challenge: | Typical sentences of characteristic syntactic structures can be used for language understanding tasks like finding typical slotfiller for verbs. |
| Approach: | They propose a method to select "typical sentences" from 5% of the original corpus . they use entropy measuring the distribution of words in a given position to identify larger sets of near-duplicate sentences . |
| Outcome: | The proposed method works language independently. |
BinarySelect to Improve Accessibility of Black-Box Attack Research (2025.coling-main)
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| Challenge: | Adversarial text attacks require a high level of knowledge of the models they target. |
| Approach: | They propose a more efficient selection method which combines binary search and attack selection methods to greatly reduce the number of queries needed to find a token. |
| Outcome: | The proposed method reduces the number of queries needed to find the first token compared to n queries on the Yelp dataset with a drop in attack effectiveness of only 5 points. |
Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model (2023.emnlp-main)
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| Challenge: | Large and sparse feed-forward layers (S-FFN) have proven effective in scaling up the model size for pretraining large language models. |
| Approach: | They compare S-FFN architectures for language modeling and compare their performance and efficiency . they found a simpler selection method that selects blocks through their mean aggregated hidden states . |
| Outcome: | The proposed model size and selection method achieve lower perplexity in language model pretraining compared to existing MoE architectures. |
Overcoming Black-box Attack Inefficiency with Hybrid and Dynamic Select Algorithms (2025.findings-emnlp)
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| Challenge: | Existing black-box attack methods require large number of queries, making them inefficient and impractical for researchers. |
| Approach: | proposed new attack selection strategies combine strengths of previous selection algorithms . Hybrid Select merges generalized BinarySelect techniques with GreedySelection . Dynamic Select learns which lengths of texts each selection method should be applied to . |
| Outcome: | proposed methods combine the strengths of previous selection algorithms to improve performance. |
Select High-quality Synthetic QA Pairs to Augment Training Data in MRC under the Reward Guidance of Generative Language Models (2024.lrec-main)
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| Challenge: | Existing approaches focus on downstream metrics to select QA pairs, which lack generalization across different datasets. |
| Approach: | They propose a general selection method that uses a large pre-trained language model as a reward model in a Reinforcement Learning framework for the training of the selection agent. |
| Outcome: | The proposed method improves performance on generative and extractive datasets. |