Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The paper talks about a very important problem in PLMs regarding bias in the data that it is trained on and highlights specifically for the case of indegenous community of NZ. The author highlights the techniques with relavant references as starting points for the debaising.
strengths_and_weaknesses: Strength: The problem is clearly explained along with relevant examples and techniques to bridge the bias gaps in PLMs\
Weakness: It is a good starting direction for debiasing but the effectiveness of the techniques along with the availability of additional data cannot be commented upon. 
suggested_changes: Some sample cases where the application of such techniques worked would make it clear if there is a possibility of transferability.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: Authors have provided some insights on how pre-trained large language models(PLM) such as GPT-2, can have bias related to under represented indigenous societies. Authors have used biasness of PLM for New Zealanders and its' indigenous population Maori and then suggested most suitable techniques(from available technqiues) for debiasing without re-training.
strengths_and_weaknesses: Strengths:
1. Clarity in the objective, easy to read and enough literature is provided by the author.
2. Authors have provided, an insight how currently all debiasing techniques are focused towards US racial biases however other under represented societies such as New Zealand indigenous population have been ignored.
3. They have provided which of the available debiasing techniques will be better for debiasing as data is very less for such indigenous populations.


suggested_changes: Paper is more of a qualitative analysis but it would be better if authors can report how does the suggested debiasing techniques perform for New Zealand indigenous group will be very useful. As it can help to address the same issues for other indigenous groups as well.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The authors present a new perspective on debiasing pre-trained language models with respect to racial attributes. The discussion frames the indigineous communities in New Zealand. The authors present the problematic of current debiasing approaches and how they should be adapted to correctly assess racial bias in NZ society. 
strengths_and_weaknesses: *Strengths*
- The paper takes an interesting and useful approach to assess social bias. 
- The paper is well written and meet the reviewing criteria: it is clear, there is appropiate discussion of relevant literature, and the claims made in the paper are justified by their findings.

*Weaknesses*
- The paper is reproducible. However, the lack of empirical excercises makes the title inadequate: since bias is not being measured, it is hard to tell the effectiveness of debiasing techniques without quantifying it. This is more a qualitative analysis, which is fine, but should be reflected in the title.

suggested_changes: - I would re-order the abstract to start presenting the problematic of what is currently being done, then continue with what is done in this paper. And then add a final sentence summarizing your findings. Also, in 'pre-defined bias attributes', do you mean 'racial attributes'?
- Footnote 2 show be next to the first appearance of the term (in the prompt 'Two white kiwi men'). 
- In section 2, the following claim should be rephrased as it seems to over-simplify the problem 'Analysing debiasing techniques for PLMs requires two aspects: mitigating against bias and measuring bias.' --> For instance, a good analysis would also require to measure model performance before and after debiasing, to ensure that the debiased model still performs correctly.
review_title: null

Decision: Invite to present