DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
DExperts combines expert and anti-expert models to control text generation at decoding time, significantly reducing GPT-3 toxicity.
Key Findings
Methodology
DExperts combines pretrained language models with expert and anti-expert models to control text attributes during decoding. Expert models generate desirable attributes, while anti-expert models generate undesirable ones. By adjusting the output probability distribution, DExperts achieves effective attribute control.
Key Results
- In language detoxification, DExperts reduced GPT-2's average maximum toxicity from 0.527 to 0.302, significantly outperforming other methods.
- In sentiment-controlled generation, DExperts achieved a 94.46% positive sentiment rate on neutral prompts, surpassing baselines like GeDi.
- DExperts effectively reduces toxicity even with only anti-experts, demonstrating flexibility.
Significance
DExperts is significant for academia and industry, offering an efficient method to control text generation without retraining large models, addressing long-standing issues of harmful content generation.
Technical Contribution
DExperts introduces a novel method for text generation control by combining expert and anti-expert models during decoding. Unlike existing methods, it avoids fine-tuning the base model, reducing computational costs and excelling in multiple tasks.
Novelty
DExperts is the first to combine expert and anti-expert models for decoding-time text generation control. It differs from prior methods by not relying on base model fine-tuning, instead adjusting output probabilities.
Limitations
- DExperts may struggle with extremely complex text attributes, as it relies on the quality of expert and anti-expert models.
- The method may be limited when full access to the base model is required, such as with GPT-3 API access.
Future Work
Future research could explore DExperts' application in more text attribute control tasks and improve performance on more complex language models.
AI Executive Summary
Recent advances in natural language generation have been significant, yet controlling the attributes of generated text remains challenging. Existing methods often require fine-tuning large pretrained language models, which is computationally expensive and impractical. DExperts combines pretrained language models with expert and anti-expert models to control text generation attributes during decoding, avoiding base model fine-tuning.
DExperts excels in language detoxification and sentiment-controlled generation tasks. In detoxification, it significantly reduces GPT-2's toxicity while maintaining fluency and diversity. In sentiment control, DExperts achieves a 94.46% positive sentiment rate on neutral prompts, outperforming existing baselines.
The innovation of DExperts lies in its ability to control text generation without fine-tuning the base model, instead adjusting output probabilities. This makes it computationally efficient and applicable even when full access to the base model is unavailable, such as through GPT-3 APIs. Future research can further explore DExperts' application in more text attribute control tasks and improve performance on more complex language models.
Deep Analysis
Background
Recent advances in natural language generation, especially with large pretrained models like GPT-3, have been significant. However, these models often lack control over specific attributes, potentially generating harmful or inappropriate content. Existing methods typically rely on fine-tuning models, which is computationally expensive for large-scale models.
Core Problem
The core problem is achieving effective control over specific attributes of generated text without fine-tuning large pretrained language models. This problem is crucial as it directly impacts the safety and applicability of natural language generation technologies.
Innovation
The core innovation of DExperts is its decoding-time combination of expert and anti-expert models to control text generation attributes. Expert models generate text with desirable attributes, while anti-expert models generate text with undesirable attributes. This approach achieves effective control without fine-tuning the base model.
Methodology
- �� Use a pretrained language model as the base model
- �� Train expert models to generate text with desirable attributes
- �� Train anti-expert models to generate text with undesirable attributes
- �� Combine outputs of expert and anti-expert models during decoding to adjust the base model's output probability distribution
- �� Control the modification strength to the base model's output using parameter α
Experiments
Experiments used GPT-2 and GPT-3 as base models, evaluated on language detoxification and sentiment-controlled generation tasks. For detoxification, the Jigsaw Unintended Bias in Toxicity Classification dataset was used. For sentiment control, the Stanford Sentiment Treebank dataset was used.
Results
In detoxification, DExperts significantly reduced GPT-2's toxicity while maintaining fluency and diversity. In sentiment control, DExperts achieved a 94.46% positive sentiment rate on neutral prompts, outperforming existing baselines.
Applications
DExperts can be directly applied in scenarios requiring controlled text generation attributes, such as social media content moderation and automated customer service systems. Its ability to function without fine-tuning the base model makes it effective even with limited computational resources.
Limitations & Outlook
DExperts may struggle with extremely complex text attributes, as it relies on the quality of expert and anti-expert models. Additionally, the method may be limited when full access to the base model is required, such as with GPT-3 API access. Future research could explore improving performance on more complex language models.
Plain Language Accessible to non-experts
Imagine you're in a kitchen. A pretrained language model is like a versatile chef who can cook various dishes but sometimes makes dishes that aren't to your taste. DExperts acts as an assistant, helping the chef add the right spices (expert models) or remove unsuitable ones (anti-expert models) to ensure the final dish meets your expectations. This way, you get a delicious meal without changing the chef.
ELI14 Explained like you're 14
Imagine playing a game with a super powerful character (pretrained language model) that sometimes makes bad choices. DExperts is like a smart assistant, helping the character make better choices at crucial moments, like choosing friendlier dialogue or avoiding inappropriate actions. This way, you have a better gaming experience without retraining the character. Isn't that cool?
Glossary
DExperts
A method combining expert and anti-expert models to control text generation during decoding.
Used to control specific attributes like toxicity and sentiment in text generation.
Expert Model
A model that generates text with desirable attributes.
In DExperts, used to enhance positive attributes in text.
Anti-Expert Model
A model that generates text with undesirable attributes.
In DExperts, used to suppress negative attributes in text.
GPT-3
A large pretrained language model with powerful text generation capabilities.
Used as one of the base models in DExperts.
Sentiment Analysis
A technique for identifying and classifying sentiment tendencies in text.
Used in DExperts to evaluate sentiment attributes of generated text.
Open Questions Unanswered questions from this research
- 1 How can DExperts' performance be improved on more complex language models?
- 2 How does DExperts perform when handling extremely complex text attributes?
Applications
Immediate Applications
Social Media Content Moderation
DExperts can automatically detect and filter inappropriate content on social media, ensuring platform safety.
Long-term Vision
Intelligent Customer Service Systems
By controlling the sentiment and tone of generated text, DExperts can enhance user experience in automated customer service systems.
Abstract
Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language model with "expert" LMs and/or "anti-expert" LMs in a product of experts. Intuitively, under the ensemble, tokens only get high probability if they are considered likely by the experts, and unlikely by the anti-experts. We apply DExperts to language detoxification and sentiment-controlled generation, where we outperform existing controllable generation methods on both automatic and human evaluations. Moreover, because DExperts operates only on the output of the pretrained LM, it is effective with (anti-)experts of smaller size, including when operating on GPT-3. Our work highlights the promise of tuning small LMs on text with (un)desirable attributes for efficient decoding-time steering.