Paper: Bayesian Low-rank Adaptation for Large Language Models (ICLR-2024)

Reviewer comment (Evaluation):
It looks like all the tasks using in this paper are still classification tasks. How it is different from standard Bayesian deep learning evaluation? I want to understand where the benefit is coming from on those eval metrics through a principled way. I feel it will be good to comment some potential on generation tasks that can reflect more of the autoregressive nature of transformers.

Author response:
> It looks like all the tasks using in this paper are still classification tasks. How it is different from standard Bayesian deep learning evaluation? 

We use classification tasks precisely so we can use standard, well-understood methods for assessing the quality of uncertainty estimation.  

> I want to understand where the benefit is coming from on those eval metrics through a principled way.

The improvement on calibration metrics is arising because we use approximate Bayesian inference to estimate our uncertainty in fine-tuning weights given a very small number of fine-tuning examples.

**Autoregressive generation**

Given that this is the first investigation of Laplace for LoRA fine-tuning, we chose to focus on multiple-choice QA because that allowed us to use robust, well-understood calibrations metrics like ECE and NLL.  

As a next step, we are indeed excited by the possibility of investigating free-text generation: Laplace-LoRA certainly could be applied in this setting.  However, the development of robust, well-understood calibration metrics for free-text generation remains a challenging and open research question. Given the complexity of evaluating calibration in the free-text setting, this extension is out-of-scope here, and we leave it for future work.

Reviewer comment (Novelty):
Majority of the benefits of Laplace-LoRA including ''post-hoc'' and ''scalable'' are from the existing method, which limits the contribution of this work. This one together with Weakness #1 above are the major concerns from my point of view.

Author response:
Bayesian deep learning is rarely if ever used by LLM practitioners.  That's because there are currently no effective and efficient methods for Bayesian finetuning of modern LLMs.  Here, we show comprehensively that highly efficient Bayesian methods can dramatically improve calibration in modern LLMs.  That's the key novelty in this paper, and it opens the way to practical applications of Bayesian methods in modern LLMs, along with a far more thorough exploration of Bayesian methods for LLMs in future work.

Regarding the novelty of our Laplace-LoRA approach, we believe its significance lies in the innovative integration of well-studied methods to create a novel framework for Bayesian fine-tuning on LLMs. This integration is not trivial and addresses a critical gap in current methodologies. In addition, we opensourced code that works with any publicly available LLMs on Huggingface.

Reviewer comment (Experiments):
Laplace Redux has discussion on relative wall-clock time cost and memory consumption comparison with other methods, is there similar result for Laplace-LoRA?

Author response:
We have added the memory and time cost comparison between LoRA fine-tuning and LoRA with post-hoc Laplace-LoRA in Section 5.4 and Table 5 in the main text.  The upshot is that the dominant additional cost arises from accumulating the low-rank Kronecker factors; that takes about 10\% of the time, and only slightly more memory memory (actually about 1-5\% more memory) than the original finetuning run.

Reviewer comment (Experiments):
More ablation study is expected. For example, LA on different types of layers (attention, dense, etc.), LA on different layers and different number of layers (first k layers or random select layers), or some fusion of the two mentioned above.

Author response:
In response to your request, we expanded our analysis in Appendix G.3, by applying LA to different LoRA layers. Specifically, we experimented with applying LA on the first 8, 16, 24, and 32 (all) layers. While we observed only small differences, there does seem to be a small improvement in the NLL when LA is applied beyond the top 8 layers.

Reviewer comment (Novelty):
It has some novelty, but not dramatic, because both Laplace Approximation and LoRA method are well-studied.

Author response:
Bayesian deep learning is rarely if ever used by LLM practitioners.  That's because there are currently no effective and efficient methods for Bayesian finetuning of modern LLMs.  Here, we show comprehensively that highly efficient Bayesian methods can dramatically improve calibration in modern LLMs.  That's the key novelty in this paper, and it opens the way to practical applications of Bayesian methods in modern LLMs, along with a far more thorough exploration of Bayesian methods for LLMs in future work.

Regarding the novelty of our Laplace-LoRA approach, we believe its significance lies in the innovative integration of well-studied methods to create a novel framework for Bayesian fine-tuning on LLMs. This integration is not trivial and addresses a critical gap in current methodologies. In addition, we opensourced code that works with any publicly available LLMs on Huggingface.

Reviewer comment (Presentation):
It is quite weird that the Section 3 Background followed by Section 4 Results directly, without a Method section in between. Maybe it needs a better section name.

Author response:
We have added a Methods section describing the nontrivial methods required for the low-rank K-FAC approach that we designed for Laplace-LoRA, with detailed derivations in Appendix E.

Reviewer comment (Experiments):
Does free-text generation tasks (e.g., free-from QA) still work under this framework? Why that kind of reasoning tasks are not considered in your experiments?

Author response:
Given that this is the first investigation of Laplace for LoRA fine-tuning, we chose to focus on multiple-choice QA because that allowed us to use robust, well-understood calibrations metrics like ECE and NLL.  

As a next step, we are indeed excited by the possibility of investigating free-text generation: Laplace-LoRA certainly could be applied in this setting.  However, the development of robust, well-understood calibration metrics for free-text generation remains a challenging and open research question. Given the complexity of evaluating calibration in the free-text setting, this extension is out-of-scope here, and we leave it for future work.

Reviewer comment (Evaluation):
**Unclear uncertainty estimation**: While the proposed Laplace-LoRA naturally estimates the weight posterior, it is unclear how to apply the proposed method to compute model uncertainties. Also, it remains unclear if the proposed method can handle the structured uncertainty estimation for next-token predictions (e.g., *Uncertainty estimation in autoregressive structured prediction, ICLR'21*). It would also be interesting to compare the proposed method with semantic uncertainty [Kuhn et al., ICLR'23].

Author response:
**Model uncertainties**

In common with with the standard Laplace approach, we compute a Gaussian approximation to the distribution over logits implied by the uncertainty in weights.
We do this by linearising the model (Eq. 12, 13 and 15).

**Autoregressive generation**

Given that this is the first investigation of Laplace for LoRA fine-tuning, we chose to focus on multiple-choice QA because that allowed us to use robust, well-understood calibrations metrics like ECE and NLL.  

As a next step, we are indeed excited by the possibility of investigating free-text generation: Laplace-LoRA certainly could be applied in this setting.  However, the development of robust, well-understood calibration metrics for free-text generation remains a challenging and open research question. Given the complexity of evaluating calibration in the free-text setting, this extension is out-of-scope here, and we leave it for future work.