- The paper introduces a contrastive learning framework that adjusts sequence likelihoods to control text outputs without modifying the model architecture.
- It employs a novel likelihood ranking strategy to construct contrastive samples, mitigating undesirable attributes such as toxicity, sentiment misalignment, and repetition.
- Experimental results show superior performance over baselines in detoxification, sentiment steering, and repetition reduction, highlighting its scalability and robustness.
An Overview of "Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning"
The paper "Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning" introduces a methodology for improving controllable text generation without requiring modifications to the model architecture. This approach is novel in its application of contrastive learning directly to sequence likelihood to aid Natural Language Generation (NLG) systems in avoiding undesirable text attributes such as toxic language and unnatural repetition. The paper demonstrates its methodology across three tasks: language detoxification, sentiment steering, and repetition reduction.
Click leverages a contrastive loss that is applied to sequence likelihood, providing a mechanism that decreases the generation probability of texts that exhibit undesirable characteristics, referred to as negative samples. A likelihood ranking strategy is incorporated for constructing these contrastive samples. This approach enables the model to differentiate effectively between positive and negative generations, thus optimizing text generation for preferred content attributes.
Methodology
- Task Formulation: The paper outlines controllable text generation as the process of producing text continuations that are fluent and contextually coherent, given a prompt, while also maintaining specific desirable features.
- Contrastive Learning: Click employs a max-margin contrastive loss, which is integrated with the standard language modeling loss. This dual-objective approach ensures that negative samples are deprioritized in the generation process. The model is trained on both language modeling and contrastive learning sets derived from initial model generations.
- Sample Construction: A novel likelihood ranking-based strategy guides the construction of contrastive samples. Generations are sampled, scored, and paired based on likelihood rankings, contrasting negative samples with closely ranked positive ones to mitigate potentially biased learning toward fluency over undesirable attributes.
Experimental Validation
The approach is validated over three tasks:
- Language Detoxification: Click significantly reduces toxic outputs compared to existing baselines like GeDi and Director, as demonstrated on the Bot-Adversarial Dialogue dataset.
- Sentiment Steering: Tested on sentiment polarity conversion tasks, Click distinctly outperforms baselines, providing higher proportions of target sentiment text.
- Repetition Reduction: Click effectively minimizes repetition, achieving superior diversity metrics while maintaining coherence and fluency, verified against the WikiText-103 dataset.
Implications and Future Work
The Click framework, with its sequence likelihood contrastive learning, demonstrates substantial improvements over existing methods in controlled text generation tasks without altering the architecture of the underlying LLMs. This approach suggests a scalable and flexible solution for diverse NLG applications requiring robust control over text outputs.
Potential avenues for future work include extending Click's framework to leverage advanced reward functions, enhancing label function reliability, and application across different languages and text domains. As AI text generation continues to evolve, methodologies like Click will play a crucial role in aligning model outputs with societal content expectations and ethical guidelines.