TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

1 code implementation • Findings (ACL) 2021 • Shizhe Diao, Xinwei Shen, Kashun Shum, Yan Song, Tong Zhang

Text Generation

RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models

1 code implementation • 31 Dec 2023 • Yuanhao Wu, Juno Zhu, Siliang Xu, Kashun Shum, Cheng Niu, Randy Zhong, Juntong Song, Tong Zhang

Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs).

Hallucination Retrieval

Plum: Prompt Learning using Metaheuristic

1 code implementation • 14 Nov 2023 • Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, Kashun Shum, Renjie Pi, Jipeng Zhang, Tong Zhang

Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models.

Image Generation

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

1 code implementation • 13 Apr 2023 • Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang

Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples.

Ethics

Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data

2 code implementations • 24 Feb 2023 • Kashun Shum, Shizhe Diao, Tong Zhang

However, most CoT studies rely on carefully designed human-annotated rational chains to prompt LLMs, posing challenges for real-world applications where labeled data is available without rational chains.

Arithmetic Reasoning Language Modelling