From: auai.org/UAI/2022/Conference/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: Meta Review: This paper studies the token uniformity problem prevalent in transformer-based models (namely, all tokens ending up with similar information in the upper layers of the transformer). It characterizes this phenomenon in terms of the singular value distribution of transformer layer outputs and proposes ways to alleviate the issue by decreasing the skew of singular values.

This is a fairly novel idea that is developed and empirically tested using multiple models and datasets. One reviewer raised important points about additional experiments, which the authors have (to a reasonable extent) addressed in the rebuttal. I would strongly ask the authors to include such additional experiments in their final version.

From: auai.org/UAI/2022/Conference/Paper670/Reviewer_3cqN
Q1 Summary and contributions: This paper have empirically shown that the degree of skewness of singular value distributions correlates with the degree of token uniformity. It also proposed a singular value transformation function by alleviating the skewness of the singular values. They also evaluated proposed transformation function on both unsupervised and supervised tasks. Experimental results show that their methods can more effectively address token uniformity compared to existing approaches.


Q2 Assessment of the paper: More detailed information regarding each of these aspects is given below:
Q2(1) Originality/Novelty: 2: Fair: The paper contributes some new ideas.
Q2(2) Significance/Impact: 2: Fair: The paper is likely to have moderate impact within a subfield of AI.
Q2(3) Correctness/Technical quality: 2: Fair: The paper has minor, easily fixable, technical flaws that do not impact the validity of the main results.
Q2(4) Quality of experiments (Optional): 2: Fair: The experimental evaluation is weak: important baselines are missing, or the results do not adequately support the main claims.
Q2(5) Reproducibility: 2: Fair: Key resources (e.g., proofs, code, data) are unavailable but key details (e.g., proof sketches, experimental setup) are sufficiently well-described for an expert to confidently reproduce the main results.
Q2(6) Clarity of writing: 2: Fair: The paper is somewhat clear, but some important details are missing or unclear.
Q3 Main strengths: 1. This paper proposed to use SVD to analyze the token uniformity across layers. Based on this observation, they propose to use  singular value transformation function by alleviating the skewness of the singular values.
2. This paper also evaluated the proposed method in some similarity tasks and some GLUE benchmarks. They show better performance compared to BERT.
Q4 Main weakness: 1. Lack of comparison with other methods such as adding exponential decay term in objective [1].

2. Lack of experiments with other pre-trained language model such as RoBERTa. For sentence eval task, lack of comparison with other methods such as SimCSE [2]. For example, if we combine this methods with simCSE, can we still improve over simCSE.

3. Lack of full results on supervised tasks. The author only report 3 task from GLUE benchmark.

4. The current results is only limited to encoder-style transformer. 

[1] Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, and Quanquan Gu. Improving neural language generation with spectrum control. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, 2020.
[2] Gao, Tianyu, Xingcheng Yao, and Danqi Chen. "Simcse: Simple contrastive learning of sentence embeddings." arXiv preprint arXiv:2104.08821 (2021).
Q5 Detailed comments to the authors: 1. Add missing comparison with other methods.
2. Add experiments on other pre-trained language model such as RoBERTa or T5.
3. report full results on GLUE.
4. extend analysis to transformer cross-attention.

Minor suggestions:
1. add missing citation in first page. There are some question mark in the papers.

Q6 Overall score: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., lack of novelty, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
Q7 Justification for your score: I think the current experimental section is a bit weaker. It will be good if authors can add those missing experiments.
Q8 Confidence in your score: 4: Quite confident. I tried to check the important points carefully. It is unlikely, though conceivable, that I missed some aspects that could otherwise have impacted my evaluation.
Q9 Complying with reviewing instructions: 1: Yes.

From: auai.org/UAI/2022/Conference/Paper670/Reviewer_x984
Q1 Summary and contributions: This paper focuses on the problem of token uniformity in transformer-based models and first presents a neat geometric interpretation of the problem. The paper shows that the singular values distribution of singular values of outputs of each transformer layer tends have a skewed distribution. It proposes an approach to repair the skewed distribution of singular values with a soft decay transformation while also shining light on the local neighborhood structure & experiments validate the method. 
Q2 Assessment of the paper: More detailed information regarding each of these aspects is given below:
Q2(1) Originality/Novelty: 3: Good: The paper makes non-trivial advances over the current state-of-the-art.
Q2(2) Significance/Impact: 3: Good: The paper is likely to have high impact within a subfield of AI OR moderate impact across more than one subfield of AI.
Q2(3) Correctness/Technical quality: 3: Good: The paper appears to be technically sound, but I have not carefully checked the details.
Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q2(5) Reproducibility: 3: Good: Key resources (e.g., proofs, code, data) are available and key details (e.g., proofs, experimental setup) are sufficiently well-described for competent researchers to confidently reproduce the main results.
Q2(6) Clarity of writing: 4: Excellent: The paper is well-organized and clearly written.
Q3 Main strengths: + The paper contributes to solid theoretical analyses and an important geometric interpretation. 
+ The approach is sound and repaid itself is simple and is easy to implement. 
+ Excellent experimental results confirms the validity of the approach
Q4 Main weakness: - I believe an error analysis of the experimental results would be interesting and would complete the paper. 
- This is not a strict weakness per se, but I was curious on the utility of the approach for generative tasks - such as machine translation. 
Q5 Detailed comments to the authors: - It would be very useful to have a section with an analyses of errors - I believe this would be more helpful than the t-SNEs 

Q6 Overall score: 8: Strong Accept: Technically strong paper with, with novel ideas, excellent impact on at least one area of AI or high to excellent impact on multiple areas of AI, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations. 
Q7 Justification for your score: + Neat paper, nicely written, solid theoretical foundations and that shows impressive results. 
+ The proposed method is simple to implement
Q8 Confidence in your score: 4: Quite confident. I tried to check the important points carefully. It is unlikely, though conceivable, that I missed some aspects that could otherwise have impacted my evaluation.
Q9 Complying with reviewing instructions: 1: Yes.

From: auai.org/UAI/2022/Conference/Paper670/Reviewer_mnp4
Q1 Summary and contributions: The paper proposes to characterize token uniformity using the
distribution of singular values. It shows empirically that a less skewed singular value distribution
can alleviate the token uniformity problem.  The paper 
proposes a novel transformation function for updating the singular
values.  The proposed singular value transformation function is
applied to a range of transformer-based language models. Empirical
evaluation is provided to validate the proposed technique.
Q2 Assessment of the paper: More detailed information regarding each of these aspects is given below:
Q2(1) Originality/Novelty: 3: Good: The paper makes non-trivial advances over the current state-of-the-art.
Q2(2) Significance/Impact: 3: Good: The paper is likely to have high impact within a subfield of AI OR moderate impact across more than one subfield of AI.
Q2(3) Correctness/Technical quality: 3: Good: The paper appears to be technically sound, but I have not carefully checked the details.
Q2(5) Reproducibility: 4: Excellent: Key resources (e.g., proofs, code, data) are available and key details (e.g., proof sketches, experimental setup) are comprehensively described for competent researchers to confidently and easily reproduce the main results.
Q2(6) Clarity of writing: 3: Good: The paper is well organized but the presentation could be improved.
Q3 Main strengths: An interesting idea for mitigating token uniformity in transformers.
The proposed technique is validated through experiments.
Q4 Main weakness: The paper doesn't seem to address the root cause of skewed distribution
of singular values.
Q5 Detailed comments to the authors: In the 1st paragraph in Section 3.1, it is unclear the first k token
embeddings correspond to the largest k singular values.
It'd be helpful to show similar plots as in Figure 3, after
SoftDecay transformation.

It's unclear how softdecay is applied, after each layer?
How exactly does softdecay mitigate token uniformity?

There seems to be a typo in Eq 1. There are inconsistent notations,
see the first paragraph under Section 3.1 and Algorithm 1.

The toy example doesn't seem to tell much.
Q6 Overall score: 7: Accept: Technically solid paper, with high impact on at least one sub-area of AI or moderate to high impact on more than one area of AI, with good to excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations. 
Q7 Justification for your score: It is a good paper, addressing an important problem. The main idea is interesting, and validated by experiments.

Q8 Confidence in your score: 3: Somewhat confident, but there's a chance I missed some aspects. I did not carefully check some of the details, e.g., novelty, proof of a theorem, experimental design, or statistical validity of conclusions.
Q9 Complying with reviewing instructions: 1: Yes.