Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper revisits the common practice of computing training data influence, especially methods for computing the influence only from the last layer and investigates improving the estimation of examples' influence.
The model size used in the NLP field is huge because we often incorporate a pre-trained language model as a base model.
We usually compute influence functions only from the final layers.
This paper also reveals that last layer representations in text classification models may suffer from the cancellation effect when computing the influence function on training samples.
This paper proposes a technique called TracIn-WE to mitigate the cancellation effect.
TracIn-WE is a modification of the existing method, TracIn, to compute the influence of each sample on the word embedding layer instead of the last layer.
This paper conducts experiments on three text classification tasks and shows that TracIn-WE significantly outperforms other data influence methods applied on the last layer.

strengths_and_weaknesses: Strengths:
* This paper reveals the issues of existing influence computation methods by calculated from the last layer.
* The empirical evaluation shows the better performance on the proposed method.
* This paper tackles the important research theme involved in XAI.


Weaknesses:
* The method is actually a straight forward extension, so that the technical novelty is marginal.

questions: * Have you ever tried to visualize the reducing cancellation effect for each layer?
limitations: This paper has limitation and potential negative societal impact sections in Appendix.
There are no additional concerns.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The paper proposes TracIn-WE that will produce data influence scores based on the level of the overall training input and at the level of words within the training input. The method mitigates an effect called “cancellation effect” resulting from the data influence extraction methods based the last layer weights. The word-embedding layer based proposed method (TracIn-WE) is built on the existing method called TracIn which is constructed on the last layer. 
strengths_and_weaknesses: Strengths: 

The method is based on the observation of existing data influence method’s cancellation across the data influence attributions (i.e., distributed test data loss) to training examples, i.e., the sign of attributions across training examples disagree and cancels each other out.  This kind of observation might be useful to develop improved methodologies for explainable NLP.

Weakness

1.	Example 3.1 is not clear to me as the example is based on a sparse representation whereas the word embedding inputs are dense representations. 

2.	The word embeddings provide the input representation. As a result, the similarity of inputs (train and test) would better capture by the embedding space. However, the data’s influence on a prediction is related to the output layers (last layer) which is closely linked to loss function. Using embedding space for data influence extraction has some limitations in terms of deep neural network training (e.g., vanishing gradient problem). It is not clear how the proposed method solves those fundamental problem (e.g., learning and loss relevance) when calculating the data influence based on the embedding layer.  

3.	Bias plays different rules than other weight. Removing Bias from the TracIn Calculation to Reduce Cancellation Effect needs further justification. What is the interpretation of cancellation ratio?  

questions: What is the interpretation of cancellation ratio? 


limitations: They have discussed limitations.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper thoroughly examines the effectiveness of different parameters serving as data influence measurement. It provides two important insights on the informativeness of parameters in lower layers and cancellation effect of parameters used by more training examples. Motivated by these insights, it develops a novel data influence method by replacing parameters of the last layer used in TracIn with embedding layer. Empirical results verify the insights and the effectiveness of the proposed method.
strengths_and_weaknesses: **Strengths**
* This paper provides two valuable insights: 
  * First, the gradients of the embedding weights capture high-level information due to gradient chain.
  * Second, parameters used by more training examples suffer more from 'cancellation effect' (i.e. gradients of multiple instances have large magnitude, but different signs).
* The proposed method takes both low- and high-level similarity into consideration. 
* Good writing and clear presentation.

**Weakness**
* The terms of 'share logic' and 'unique logic' are vague. Could you provide explanations and evidence?
* There should be an ablation study for using special tokens only on full dataset to demonstrate the effectiveness of word overlap.
* Removing data points also change the training process (i.e. schedule of batches). How do you control related variables to be consistent for different methods? Does the random seed of data sampler influence the model performance significantly?
questions: * Is it essential to use *gradient-based* similarity as the similarity term $S$?
* Some typos:
  * line 36: parametersin -> parameters in
  * line 194: "share" logic -> "share logic"
  * line 215: multiple periods.
  * Appendix Figure 4: or -> on

limitations: * The proposed method only works well when word embedding is fixed.
review_title: null

Meta-review: metareview: This paper studies an important problem, of identifying influential training examples. It exposes a potential shortcoming in prior work, of focusing on the last layer, and proposes an alternative method. The approach cleverly assures looking at word overlap via overlapping word embeddings while still aggregating high level information from the back flowing gradients. The reviewers appreciated the topic, the insights and observations, and the empirical observations.  Overall this paper is making novel contributions to an important area. 

There was some worry of novelty but I agree that the findings are novel and meaningful. The reviewers also raised various questions about vagueness of terms, which the authors addressed in the their response. There were also comments on missing controls and ablations, which the authors partly addressed in their response. To this, during the discussion, a suggestion was made to "to add the variance of multiple runs or a significant test, since the robustness towards randomness is also very important to a reliable data influence measurement". I strongly agree. 

Some technical questions by Reviewer iZ6x were answered. Please make sure to include the answers to them in the next revision, as well as all the clarifications and ablations provided in the author responses. 

Finally, I would strongly suggest adding experiments with at least one stronger model besides BERT, such as RoBERTa or DeBERTa. This would help give confidence that the approach is relevant for newer models. 

AC  
confidence: Certain
Decision: Accept