Papers by Kashun Shum
TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing autoregressive models suffer from the exposure bias problem due to mismatches between training and generation stages. |
| Approach: | They propose a Transformerbased Implicit Latent GAN which combines a transformer autoencoder and GAN in the latent space with a novel design and distribution matching based on the Kullback-Leibler divergence. |
| Outcome: | The proposed model improves local and global coherence and quality-diversity trade-off on three benchmark datasets. |
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Chain-of-thought (CoT) prompting is a new approach to prompt large language models (LLMs) but most studies rely on human-annotated rational chains to prompt LLMs . |
| Approach: | They propose a method that augments rational chains from a small labeled dataset and pruning low-quality chains to construct a pool of machine generated rationale chains based on the labels. |
| Outcome: | The proposed method can bypass human engineering of CoT by automatically augmenting rational chains from a small labeled dataset, and pruning low-quality chains to construct a candidate pool of machine generated rationale chains based on the labels. |