Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models (2410.20008v1)

Abstract: Fine-tuning pre-trained LLMs on a diverse array of tasks has become a common approach for building models that can solve various NLP tasks. However, where and to what extent these models retain task-specific knowledge remains largely unexplored. This study investigates the task-specific information encoded in pre-trained LLMs and the effects of instruction tuning on their representations across a diverse set of over 60 NLP tasks. We use a set of matrix analysis tools to examine the differences between the way pre-trained and instruction-tuned LLMs store task-specific information. Our findings reveal that while some tasks are already encoded within the pre-trained LLMs, others greatly benefit from instruction tuning. Additionally, we pinpointed the layers in which the model transitions from high-level general representations to more task-oriented representations. This finding extends our understanding of the governing mechanisms of LLMs and facilitates future research in the fields of parameter-efficient transfer learning and multi-task learning.

• The paper identifies distinct layer groups in instruction-tuned LLMs, pinpointing where general representations shift to task-specific encoding.
• The paper employs matrix analysis techniques like MOSSA and CKA across over 60 NLP tasks to quantify how instruction tuning refines model layers.
• The paper demonstrates that recognizing key transitional layers can enhance applications such as parameter-efficient transfer learning and model compression.

Analysis of Multi-Task Learning Dynamics in Instruction-Tuned LLMs

The paper "Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned LLMs" investigates the intricacies of task-specific information retention in fine-tuned LLMs particularly focusing on instruction-tuned models over a vast array of NLP tasks. The analysis utilizes a comprehensive array of over 60 tasks, employing state-of-the-art models such as Llama 2.

Key Contributions and Methodology

The paper centers on understanding where within the layers of instruction-tuned LLMs multi-task learning contributes to encoding and specialization for various tasks. Implementing matrix analysis techniques such as Model-Oriented Sub-population and Spectral Analysis (MOSSA) and Center Kernel Alignment (CKA), the authors map out how instruction tuning influences model representations.

The paper's methodology involves training experimental models on a diverse set of tasks and comparing these models to control models trained individually on specific tasks. The main analytical tools, MOSSA and CKA, provide insights into task-specific knowledge encoding and the impact of instruction tuning. The focus shifts from simply measuring model performance to examining the latent representations within model layers.

Findings and Implications

A significant finding is the identification of functional layer groupings within instruction-tuned LLMs: shared layers (1-9), transition layers (10-15), and refinement layers (16-32). These distinctions highlight layers where general representations transition into task-specific representations. Importantly, these transitional layers exhibit the most pronounced shift in representational function, serving as pivotal points in multi-task adaptations.

The research underscores that instruction tuning significantly refines these task-specific representations, particularly in the middle layers of the LLMs. Contrary to pre-trained LLMs, which often inadequately encode specialized knowledge such as coreference resolution or structured data translation, instruction-tuned models show significant improvements, especially in encoding task-relevant details.

Broader Theoretical and Practical Implications

This division of layers into functional groups not only enhances our conceptual understanding of multi-task learning in LLMs but also opens avenues for practical applications like parameter-efficient transfer learning (PEFT) and model compression. In practical terms, knowing which layers contribute to general versus task-specific learning can inform more efficient tuning and resource allocation.

Additionally, the paper's insights can drive improvements in adaptive architectures, facilitating more robust generalization to unforeseen tasks by exploiting the shared representational capacity encoded in initial layers. This aspect aligns well with documented findings on generalization capabilities documented in work by Wei et al. and others on instruction tuning.

Future Directions

The analysis and findings significantly contribute to the broader discourse on multi-task learning and suggest areas for further research. Future investigations might explore broader architectural variations and scaling implications, as well as contrast instruction tuning effects across different LLM architectures. Understanding the extent to which these findings generalize to unseen domains or non-textual modalities such as code offers an intriguing trajectory for advancing the utility of LLMs.

In summary, this paper provides a robust and nuanced examination of instruction tuning in LLMs, offering valuable insights for advancing NLP technologies. It elucidates the layered encoding mechanisms that underpin the capabilities of modern LLMs, paving the way for innovative approaches to model fine-tuning and architecture design in multi-task learning contexts.