Papers with K-means clustering
On Training Instance Selection for Few-Shot Neural Text Generation (2021.acl-short)
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| Challenge: | Pretraining large neural networks with a language modeling objective has led to dramatic improvements in text generation. |
| Approach: | They propose a selection strategy to select few-shot training instances based on unlabeled data to identify the most worthwhile data points that should be annotated under some budget of labeling cost. |
| Outcome: | The proposed strategy outperforms random sampling on three text generation tasks. |
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models (2022.acl-long)
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| Challenge: | Existing feature-based model selection methods are vulnerable to fine-tuning and are not reliable indicators for the PTM’s transferability. |
| Approach: | They propose feature-based model selection methods which assess PTMs’ transferability to a specific task in a fast way without fine-tuning. |
| Outcome: | The proposed methods can make FMS mistakenly judge transferability of models and can be combined with the backdoor attack to misguide the FMS to select poisoned models. |
Improving Clustering with Positive Pairs Generated from LLM-Driven Labels (2025.emnlp-main)
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| Challenge: | Existing unsupervised clustering methods lack label knowledge, resulting in suboptimal performance. |
| Approach: | They propose to use LLM-driven labels to generate positive pairs from embedded data and an embedder to obviate the need for negative pairs. |
| Outcome: | The proposed framework surpasses state-of-the-art benchmarks on a range of datasets and generates interpretable labels for improved understanding of clustering results. |