Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning
Recent advancements in large language models (LLMs) have been propelled by the innovative approach of multi-task instruct-tuning. This method allows LLMs to learn from a variety of tasks simultaneously, improving their adaptability and performance. However, a significant challenge has emerged from this paradigm: cross-task interference. This interference arises when conflicting gradients occur over shared parameters, complicating the learning process and hindering model efficiency.
Previous attempts to address cross-task interference have incorporated techniques such as task-specific neuron selection and mixture-of-experts models. While these methods have shown some promise, they have not fully mitigated the issue due to the inherent sharing of many parameters across different tasks. In their recent paper, researchers have empirically demonstrated that cross-task interference persists even with existing solutions, highlighting the need for a more robust approach.
To tackle this challenge, the authors propose a novel solution called Basic Abilities Decomposition for multi-task Instruct-Tuning (BADIT). Their research reveals that certain parameters within LLMs are consistently co-activated across tasks, indicating a structured organization into base groups. This observation leads to a compelling analogy: LLMs encode several orthogonal basic abilities, where each task can be represented as a linear combination of these abilities.
The BADIT approach involves decomposing LLM parameters into orthogonal high-singular-value LoRA (Low-Rank Adaptation) experts that represent these basic abilities. A key feature of BADIT is its dynamic enforcement of orthogonality during training, achieved through spherical clustering of rank-1 components. This innovative strategy not only preserves the integrity of the basic abilities but also reduces the degree of cross-task interference significantly.
Experimental Validation
The authors conducted extensive experiments using the SuperNI benchmark, evaluating the performance of their proposed BADIT method across six different large language models. The results from these experiments are promising, demonstrating that BADIT not only outperforms state-of-the-art (SOTA) methods but also effectively mitigates the cross-task interference that has plagued previous multi-task instruct-tuning efforts.
Key Findings
- Cross-Task Interference: The study confirms that cross-task interference remains a critical issue in multi-task instruct-tuning.
- Parameter Co-activation: Certain parameters are consistently co-activated across tasks, revealing an underlying structure in LLMs.
- Basic Abilities Concept: LLMs can be viewed as encoding a set of orthogonal basic abilities, allowing for more effective task representation.
- BADIT’s Effectiveness: The BADIT method demonstrates superior performance over traditional approaches, significantly reducing interference.
These findings have significant implications for the future of multi-task learning in natural language processing. By adopting a decomposition approach that focuses on basic abilities, researchers can enhance the performance of LLMs while addressing the challenges posed by cross-task interference. The ongoing evolution of LLMs continues to reshape the landscape of artificial intelligence, paving the way for more sophisticated and versatile models capable of handling diverse tasks with greater efficiency.
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