QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation

Summary

This paper proposes a novel PEFT method that inspired by the Quantum Circuit. Their method is theoretically supported by the universality theorem and the rank representation theorem to achieve efficient high-rank adaptations on various downstream tasks. The QuanTA method surpasses LoRA, DoRA and even fine-tuning in some cases.

Strengths

1. The paper is well-motivated and presents sufficient theoretical prove and justifications. 2. The experiments are abundant and the model performs very well compared with other baselines with much less activated parameters 3. A well-written paper

Weaknesses

1. I'm curious about the performance of QuanTA on textual understanding (classification) tasks with BERT or RoBERTa. Afterall, all these PEFT models were initially applied to text classification tasks (LoRA, Adapter...) 2. Do you have experiments that combine other PEFT methods with your QuanTA? Since the number of activated parameters of your method is much less than baselines. Can we further boost the model performance by adding your method to others?

Questions

Please refer to the weaknesses section.

Rating

7

Confidence

3

Soundness

4

Presentation

3

Contribution

3

Limitations

Yes

Summary

The paper proposes an efficient fine tuning method inspired from quantum circuits for large scale pretrained models. This is an alternative lorank approximation to weight updates (LoRA). The paper considers decomposing the dimension d of the square weight matrix as d=d1xd2x...xdN) .The hidden vector in d dimensions can then be considered as a vector in the corresponding tensor product space of qudits. The action of the weight update is then considered as a sequence of quantum gates applied to these qudits. The weighted layer is then defined as y = W_\theta x = W_0 x + T_\theta x - Sx where W_0 is the initial weight matrix, T_\theta is trainable quantum circuit and S is fixed to be initial circuit for T_\theta. Through experiments on multiple datasets they are able to beat other fine tuning methods considering versions of Llama as the base model. The paper also discusses the theoretical results to show the universality of tensor decomposition into tensors acting on atmost 2 axis. They have also derived lower and upper bounds on the rank of the overall tensor product operator in terms of ranks of individual tensors. The work resolves the issue with low rank approximation which may not always be sufficient as shown through experiments.

Strengths

1. The idea of using tensor decomposition for PEFT is original , novel and would find applications in the area of LLM fine tuning. 2. The experimental results show promising results on multiple datasets including DROP, CommonSense Reasoning and Arithmetic Reasoning. 3. The paper is backed by theoretical justification for universality of tensor product decomposition. 4. The experiments also justify the need for higher rank approximations.

Weaknesses

1. The paper does not discuss the ordering over the pairs of qubits chosen. Also in general the universality theorem for tensor product might involve picking a set of pairs of qubits where a given pair might participate multiple times in the circuit. Is picking a pair of qubits only once always sufficient? 2. A minor concern could also be that the dimension of of hidden state needs to be factorizable as product of small dimensions. This would typically not be a concern in practice since these are powers of 2, so would work perfectly in those scenarios.

Questions

1. 1. The paper does not discuss the ordering over the pairs of qubits chosen. Also in general the universality theorem for tensor product might involve picking a set of pairs of qubits where a given pair might participate multiple times in the circuit. Is picking a pair of qubits only once always sufficient?

Rating

8

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors have addressed their limitations and societal impact well.

Summary

The authors address the issue that low-rank adaptation methods fail when applied to more complex tasks. They clearly present this motivation through experiments on two datasets of varying complexity. Drawing inspiration from quantum information science, the authors propose using the formalism from quantum circuits, which describe operations on quantum systems whose dimensions increase exponentially with the number of qubits.

Strengths

1. The paper is clearly motivated and well-written, with the only challenging aspect being the tensor formalism. However, the authors acknowledge this difficulty and frequently provide specific examples to aid understanding. 2. Integrating quantum concepts in fine-tuning appears to be a novel approach and demonstrates promising results.

Weaknesses

1. Please clarify the start and end limits in the summations. 2. The authors justify their results by stating that adaptive methods based on low-rank decomposition perform poorly on more complex tasks. Why is LoRA the only baseline method considered? Why did the authors not compare their method with high-rank methods, such as MoRA or KronA? 3. I appreciate that the authors devoted some attention to the complexity of the proposed methods, but it would be very beneficial if they also provided some evaluation of training time and GPU memory usage in comparison to other baselines.

Questions

1. From a practical point of view, is there any preference regarding the decomposition of the output dimension? For example, should we favour smaller-dimensional axes? There are many possibilities for such a decomposition, so any tips that would help in exploring this hyperparameter subspace would be valuable. 2. Why is the method limited to only two-axis gates? Multi-axis gates should be more expressive, correct? In quantum computing, we typically restrict ourselves to two-qubit gates due to the difficulty of implementing multi-qubit gates on real devices. However, this constraint does not apply here. 3. I understand the motivation of this work, where the authors show the limitations of low-rank adaptation methods in the context of more complex reasoning tasks. Therefore, will the proposed method provide any benefits in terms of fine-tuning in the domain of computer vision? 4. How large is the $\alpha$ parameter? My understanding is that $\alpha$ dictates the number of gates used in the circuit, and subsequently, the expressiveness of the final circuit is influenced by this value and the arrangement of these gates. Is this correct? If so, what is the relationship between the performance of the proposed method and the value of $\alpha$?

Rating

8

Confidence

3

Soundness

4

Presentation

3

Contribution

4

Limitations

yes

Summary

This work proposed a method, QuanTA, that leverages quantum-inspired techniques for efficient fine-tuning of large pre-trained language models. The authors show that it outperforms Low-Rank Adaptation (LoRA) in complex tasks and has significant improvements in common-sense and arithmetic reasoning.

Strengths

A novel idea to represent the weight updating by a quantum circuit inspired tensor adaptation.

Weaknesses

the comparison of related tensor-based approaches is missing, it is unclear the outperformance of the proposed method over related previous works.

Questions

1. As quantum circuit can also be written in the form of a tensor network, what are the main advantages of the proposed method compared to other previous tensor-based PEFT？ such as FACT(https://arxiv.org/pdf/2212.03145) or KronA(https://arxiv.org/pdf/2212.10650) 2. As the proposed method employs a quantum circuit to replace the low-rank adaptation, there are various circuit architectures, which kind of circuit ansatz (architecture) is used here, arbitrary or specific? and what is the motivation for choosing such one?

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

2

Limitations

no negative societal impact of their work