Controlled Text Generation with DExperts
The paper presents a novel decoding-time strategy for controlling attributes in text generation, known as DExperts (Decoding-time Experts), which combines pretrained LLMs (LMs) with smaller "expert" and/or "anti-expert" LMs. These experts and anti-experts are finely tuned LMs which emphasize desirable and undesirable attributes in text, respectively. DExperts operate by exploiting a product-of-experts mechanism at decoding time, allowing it to align token probabilities in accordance to both the experts' and anti-experts' assessments, thereby steering generated text towards certain desired characteristics such as detoxification or sentiment polarity.
Methodology
DExperts effectively integrate pretrained LMs with additional smaller models that contribute additional biases towards (or away from) specified attributes. The key innovation here is the use of these tunable small LMs in combination with the output logits of a larger base LM to alter the probabilities of forthcoming words during generation. The formulation is straightforward; it amplifies the probabilistic predictions of tokens favored by the expert model while suppressing those preferred by the anti-expert, straight from the decoding-time logic to avoid fine-tuning the colossal base models.
The experimental procedures focus on two applications: (1) reducing toxicity and (2) achieving controlled sentiment generation. These tasks capitalize on initially fine-tuning smaller LMs on data exemplifying the target and non-target attributes (e.g., toxic versus non-toxic, positive versus negative sentiment).
Key Results
The DExperts model is shown to significantly outperform other established methods in both tasks, maximizing control over generation attributes without sacrificing diversity or fluency in output. In language detoxification, DExperts demonstrates its effectiveness across multiple model sizes with less reliance on large datasets for training its antitoxic experts. In the sentiment-control experiment, it adeptly manipulates sentiment even in adversarial setups, further proving the robustness of this technology.
In all scenarios tested, DExperts maintains higher fluency and produces less toxic outputs compared to standalone pretrained models or competitive adversarial methodologies such as GeDi or PPLM. Its utility is underscored especially in regard to its operational efficiency and practicality given the computational costs associated with retraining or finetuning expansive LMs.
Implications and Future Directions
The paper highlights DExperts' potential in making advanced LLMs safer and more applicable across ethical and social dimensions. The technique described provides an accessible framework for researchers and developers seeking to incorporate nuanced control over text generation with limited resources. By delegating attribute control to smaller LMs and operating at decoding time, DExperts stands robust against the quickly compounding computational restrictions that accompany the scaling of modern LMs.
Furthermore, the versatility of combining multiple experts and anti-experts in a single ensemble opens a promising avenue for multi-faceted text modification — a key aspect for fields like content personalization, automatic moderation, and beyond. Looking forward, exploration into integrating DExperts with diverse emerging initiatives such as reinforcement learning could compound the advantages of this approach. Additionally, it serves as a valuable case paper in balancing model transparency with control capabilities, emphasizing ethical adherence in automated language technologies.
In conclusion, DExperts epitomizes a pragmatic advance in the controlled application of LLMs, suggesting a scalable pathway forward not merely for their refinement but also for their disciplined deployment in sensitive and mission-critical domains.