Unified Multi-Task Learning & Model Fusion for Efficient Language Model Guardrailing (2504.19333v2)

Abstract: The trend towards LLMs for guardrailing against undesired behaviors is increasing and has shown promise for censoring user inputs. However, increased latency, memory consumption, hosting expenses and non-structured outputs can make their use prohibitive. In this work, we show that task-specific data generation can lead to fine-tuned classifiers that significantly outperform current state of the art (SoTA) while being orders of magnitude smaller. Secondly, we show that using a single model, \texttt{MultiTaskGuard}, that is pretrained on a large synthetically generated dataset with unique task instructions further improves generalization. Thirdly, our most performant models, \texttt{UniGuard}, are found using our proposed search-based model merging approach that finds an optimal set of parameters to combine single-policy models and multi-policy guardrail models. % On 7 public datasets and 4 guardrail benchmarks we created, our efficient guardrail classifiers improve over the best performing SoTA publicly available LLMs and 3$^{\text{rd}}$ party guardrail APIs in detecting unsafe and safe behaviors by an average F1 score improvement of \textbf{29.92} points over Aegis-LlamaGuard and \textbf{21.62} over \texttt{gpt-4o}, respectively. Lastly, our guardrail synthetic data generation process that uses custom task-specific guardrail poli

Unified Multi-Task Learning and Model Fusion for LLM Guardrailing

The paper "Unified Multi-Task Learning and Model Fusion for Efficient LLM Guardrailing" addresses the pressing issue of implementing efficient guardrails in LLMs to mitigate undesired behaviors. While LLMs demonstrated their potential in curbing unwanted user inputs, challenges associated with latency, memory consumption, and cost, alongside the nonformation nature of generative outputs, often deter their practical application.

Key Contributions

The authors introduce several methodologies that are particularly noteworthy:

1. Task-Specific Data Generation: By generating task-specific synthetic data, they fine-tune classifiers that outperform state-of-the-art (SoTA) methods while requiring significantly less computational resources. The fine-tuned classifiers are shown to be orders of magnitude smaller than mainstream LLMs used in similar tasks.
2. Multi-Policy Model, MultiTaskGuard: The proposal of MultiTaskGuard, a model pre-trained on large synthetic datasets, further enhances the generalization of these guardrail mechanisms. Pretraining is conducted with task-specific instructions, ensuring the model is adept across various benchmarks.
3. Model Merging Approach, UniGuard: The paper introduces UniGuard, developed through a search-based model merging strategy. This approach finds optimal parameters for merging single-policy and multi-policy models, optimizing their combined efficacy.

Across a suite of 7 public datasets and 4 benchmark tests, these guardrails improve upon the best-performing publicly available LLMs and several third-party APIs, with significant F1 score improvements of 29.92 points over Aegis-LlamaGuard and 21.62 over gpt-4o.

Implications and Significance

Theoretical Implications: The research demonstrates the potency of multi-task learning and pre-trained synthetic data-driven models. It highlights a pathway to develop robust classifiers that retain efficiency while maintaining high performance across complex guardrail tasks. This paper suggests that model merging, when paired with a well-structured search mechanism, can effectively harness the strengths of individual models.

Practical Implications: The introduction of smaller, fine-tuned models can drive substantial cost reductions within industries using LLMs, making high-performance guard railing feasible in more constrained computational environments. Additionally, by clarifying task specifications and dataset structure, this work lays a roadmap for the development of more focused and application-specific LLMs, especially for safety-critical tasks.

Future Directions: The promising results put forth by UniGuard and MultiTaskGuard point to several future research avenues. Exploring more sophisticated model merging strategies that consider context and parameter types, integrating more rigorous search algorithms, or expanding multi-task learning paradigms within LLMs could further enhance model robustness and efficiency.

In summary, this paper presents an in-depth exploration of methodologies for LLM guardrailing that combine simplicity, efficiency, and effectiveness. By innovating in data generation, multi-task learning, and model merging approaches, this work significantly advances the state of the art in creating streamlined, high-performance guardrail models for LLM applications. Such advancements could pave the way for more robust and scalable AI deployments across diverse domains.