VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder

arXiv:2307.00852 · cs.CL · Submitted 2023-07-03 · Read on arXiv

cs.CL

Submitted: 2023-07-03

Updated: 2026-09-20

Comments: 15 pages. Published in Findings of the Association for Computational Linguistics: NAACL 2024

Journal ref: Findings of the Association for Computational Linguistics: NAACL 2024, pages 364-378

DOI: 10.18653/v1/2024.findings-naacl.26

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 46× in this paper · explained in 2 episodes
• Latent Space — 14× in this paper · explained in 7 episodes
• Embeddings — 13× in this paper · explained in 2 episodes
• Transformer Models — 7× in this paper · explained in 2 episodes
• Natural Language Generation — 6× in this paper
• Variational Mutual Information Maximization — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Attention Mechanism — 3× in this paper · explained in 3 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Variational Bayes — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• Language Models are Few-Shot Learners
• PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
• CTRL: A Conditional Transformer Language Model for Controllable Generation
• CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models
• LLaMA: Open and Efficient Foundation Language Models
• AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
• OPT: Open Pre-trained Transformer Language Models

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering