Papers by I-Fan Lin
SPILL: Domain-Adaptive Intent Clustering based on Selection and Pooling with Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for intent clustering rely on labeled examples or unsupervised fine-tuning to optimize results for each new dataset. |
| Approach: | They propose a method that uses an embedder to derive an embedding for each utterance and then pool them with the seed to improve the embeddable results. |
| Outcome: | The proposed method outperforms embedding methods and is comparable to state-of-the-art methods. |
LLMs Enable Bag-of-Texts Representations for Short-Text Clustering (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for short text clustering require labeling and no embeddings optimization. |
| Approach: | They propose a training-free method for unsupervised short text clustering that relies less on careful selection of embedders than other methods. |
| Outcome: | The proposed method achieves comparable or superior results to state-of-the-art methods, but without embeddings optimization or prior knowledge of clusters or labels. |
Generate then Refine: Data Augmentation for Zero-shot Intent Detection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing data augmentation methods rely on few labelled examples for each intent category, which can be expensive in settings with many possible intents. |
| Approach: | They propose a data augmentation method for intent detection in zero-resource domains by using an open-source large language model and a smaller sequence-to-sequence model. |
| Outcome: | The proposed method significantly improves the data utility and diversity over the zero-shot LLM baseline for unseen domains and over common baseline approaches. |