Can Small Language Models Learn, Unlearn and Retain Noise Patterns?
Authors: Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani


Reviewer paper summaries: The paper investigates the ability of pre-trained small language models (<7B parameters) to learn and unlearn patterns from the training data. To do so, the authors continue fine-tuning a pre-trained model on different sequences of noisy/clean instructions (questions and answers) or use in-context learning to test the resilience of models to these perturbations.

They define 3 types of noise: flipping words in the answer, flipping tokens (referred to as characters in the text) in the answers, and using irrelevant answers.

For the experiments, they create a synthetic test dataset with these types of noise and check whether the model (instruction-tuned or via in-context learning) can generate answers with a specific type of noise. Thus, they create test datasets with and without such noise to test for this.

Then, the experiments comprise different sequences of training datasets to assess the learning and unlearning capabilities of the models. For example, (i) train on clean data, then (ii) irrelevant answers and (iii) finally test.

They found discrepancies in the learning and unlearning capabilities of the specific models tested.
Summary of strengths: The paper sets out to obtain a very interesting goal: “gain insights into the robustness and adaptability of SLMs (small language models) in handling noisy data, contributing to a deeper understanding of their learning mechanisms and potential limitations”.
Summary of weaknesses: (W1) Despite being an interesting objective, the analysis does not seek to explore the causes of a model’s behaviour. The paper offers a mere report of the results that the models obtained in the different setups and does not provide any analysis that points to a “deeper understanding” (as mentioned in the paper) of the models’ behaviours.

(W2) The exposition is very difficult to follow (suggestions in the next text box):

1. The description of the experimental design and results is spread across the paper and not super clear

2. Most of the paper is used to discuss results which could have easily been summarised in a table/plot, and the residual space could have been used for an analysis of the causes of models’ behaviour
Comments, suggestions and typos: Suggestions to address W2:

1. A summary table for sec. 3.3 would help a lot; 3.3.2 and 3.4 should be in the same subsection

2. Follow the ACL template and remove new line after a paragraph (e.g., 524, 544, etc)

3. The writing, in some parts, has incorrect sentences (e.g., 251-253) which should be double-checked

4. I would remove the mention of the “Stochastic Parrot Hypothesis” in the paper as it is very weak. The paper does not try to disentangle “pattern matching” from “reasoning” in language models. It rather just checks whether a model can learn/forget a specific pattern without providing an intuition of why some models do well while others fail
Overall assessment scores: 2.5