Venue: AKBC 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper analyzes the emergence of commonsense knowledge in few-shot knowledge models. In particular, it attempts to answer whether and when is this knowledge learned: during pretraining or from fine-tuning on KG examples. To investigate this, various commonsense knowledge models are trained in few-shot settings. The results and analysis in the paper shows that commonsense knowledge models can adapt from limited examples. Moreover, similar to a recent line of work, this work confirms that KG fine-tuning learns an interface to the commonsense knowledge learned during pretraining. Finally, the paper also presents an analysis of absolute, angular, and distributional parameter changes during few-shot fine-tuning. However, the insights here are less convincing to me.

Pros:
1. The paper is timely. It is presented well and answers some interesting question on the emergence of commonsense knowledge in these models in a very simple and intuitive way.
2. The analysis (while not always convincing) mostly makes sense and all the conclusions seem reasonable.

Cons:
1. The related work discussion seems too sparse to me.
2. It would help if all the conclusions from the experiments in the paper were presented in one place e.g. in the intro.

Questions and suggestions:
1. Why do we need to consider an encoder decoder model for this analysis? Why cant we directly analyse an encoder only or decoder only model?
2. It might help to provide more motivation and intuition for the various parameter change metrics e.g. angular parameter change in the paper. This would help the readers understand the conclusions much more easily. This part is less clear in my opinion.
3. This paper analyses commonsense knowledge. What would change in this paper if we were analysing declarative knowledge such as in other KGs? Is emergence of commonsense knowledge any different from other kinds of knoweldge?
review_title: Good paper - simple, well executed and well presented

final_score: rating: 7: Good paper, accept
review_content: This paper analyzes fine-tuning of pre-trained generative language models on a commonsense knowledge task, investigating how much the strong performance of the models is due to pre-training or to task-specific learning (via fine-tuning or prompting/in-context learning). The task is to predict natural text for the tail of (head, relation, tail) relationships from the Atomic_20^20 knowledge graph (KG), e.g. ("PersonX goes to the mall", xIntent) -> "to buy clothes". The paper shows that a T5-based model obtains a substantial fraction of the performance of fine-tuning on the full Atomic dataset, using few-shot learning with only a small number of examples (3) per relation. Few-shot learning also obtains a substantial improvement over zero-shot performance for this model. The paper also presents several measures of parameter differences between pre-trained and fine-tuned models and performs an analysis with them, showing that more parameters are updated in the models' decoders than in their encoders. Taken together with past work, the evaluation results and analysis support the paper's claim that most of the knowledge useful for the KG task was already present in the pre-trained models.

*Pros*:
- I found the paper well-motivated -- it's a natural and sound way to investigate how much performance on the KG tasks is due to pre-training versus task-specific fine-tuning, and an indirect way of investigating how much knowledge (of the type useful for this KG task) is encoded in the pre-trained language models. I expect this will be of interest to the community.
- The experimental results were overall very thorough (although see below for one possible gap), performing human evaluations, multiple trials, and evaluating a range of models.
- The parameter analysis was particularly strong. This method is simple (which I think is a good thing!) and well-motivated, and the results were well-explained, convincing to me, and tied in well to past work. 

*Cons*:
- The only model that is compared across all three conditions (zero-shot, few-shot, and fully-supervised) in the experimental results is GPT-2 XL, but for this model few-shot learning recaptures barely any of the performance of the fully-supervised condition (36.6% acceptability in zero-shot, at most 40.1% acceptability in few-shot, and 72.5% acceptability in fully-supervised). However, I suspect that this may be due to limitations of the T5 and GPT-3 models. Please see questions below.
- A few minor things about the presentation could be improved, see suggestions below. But this isn't at all a crucial weakness and should be easily fixable.

*Questions*
- Is it possible to evaluate either GPT-3 or T5 in all of the zero-shot, few-shot, and fully-supervised settings, as GPT-2 XL doesn't do well in the few-shot condition? I suspect GPT-3 may not be fine-tunable (for the fully-supervised condition), and T5 may not work well in the zero-shot since it's an encoder-decoder model, but it would help to clarify this.
- How is the "automatic formatting" of relations into prompts done (Section 3) -- was the prompt text for a given relation obtained from some source, or written by hand? Were prompts tuned on validation data in either the few-shot or zero-shot setting? This is relevant given Jiang et al. 2020 showing that performance is sensitive to subtle variations.
- Could more details be given on the setup in the "augmentation" experiments? Do these include the n=3 training examples as part of the prompt, but without fine-tuning the model? Was the formatting of the prompt the same across both GPT-2 XL and GPT-3, and does the formatting have any hyperparameters / design decisions that needed tuning?


*Suggestions*:
- In the experimental setup it would help to clarify the optimizer used.
- I was surprised when section 4 ended without varying n over a larger budget of training examples (which comes later in Section 5), given the focus on this in the intro. I'd suggest at least adding a forward reference to the text in Section 4.
- The paper used both "learning" in Table 3 and "fine-tuning" elsewhere in the text to mean the same thing (I think); it would help to use the same term for both.
- Could more motivation be given for using rows in angular parameter change (rather than e.g. columns, or flattening the matrices and treating them as a single vector)? Rows might be motivated by e.g. each row's correspondence to an individual element in the output vectors, but I don't know if this is true because I don't know whether inputs are row or column vectors. 
- I'm also not totally clear on the advantage of angular parameter change over l1 distance -- I found it a bit difficult to think about how l1 change could be low but angular change high (in section 5.3).
- 5.1: "large number of the learning"
- Super minor, but the paragraphs before equations (1-3) seem to have compressed line spacing. If these equations are wrapped in \small, this might be fixed by inserting \par\nobreak or \par\nobreak\vspace{-1em} between the paragraphs and the equations. (I once spent a while figuring out how to fix this.)

review_title: Well-motivated and insightful analysis that interpolates between zero-shot and fully-supervised

final_score: rating: 7: Good paper, accept
review_content: Summary:

The paper demonstrates that commonsense knowledge models can learn declarative commonsense relationships very quickly in a few shot setting.
This paper aims to address the question of whether commonsense knowledge is directly encoded in pretrained LMs or whether the encoding is learned during finetuning with a favorably initialized pretrained model. 
By varying the number of training examples in a few shot setting, the authors show that models seem to learn an interface to knowledge they already contain with only a few commonsense  examples.
Furthermore, the paper performs analysis wrt parameter change to support that the model seems to be learning an interface to knowledge it already knows, showing that 1) few shot training largely updates parameters in the decoder attention heads and not the large FF networks (that have been showed to embody memory storage during pretraining) and 2) that larger models exhibit parameter change across a concentrated set of parameters, showing that their larger size allows for less entangled knowledge retention. 


Strengths:

-Good evaluation metrics, extensive analysis, and interpretable methods to support claims in the paper

-Very interesting use of LMs as few shot learners to investigate commonsense knowledge abilities 

-Useful findings on prompt performance in few shot commonsense interface learning 

-Paper is very well written and structured


Weaknesses:

-The paper only considers ATOMIC as a KG, it could be useful to experiment w/ more KGs. Extending this to ConceptNet should not be very difficult given the overlap of relations in ATOMIC and ConceptNet. Would be helpful and interesting to see if the findings generalize to other types of commonsense knowledge or whether the findings are specific to the type of knowledge found in ATOMIC. 

review_title: Interesting Commonsense Analysis Using Few-Shot Knowledge Models

Meta-review: This paper analyzes the emergence of commonsense knowledge in language models pre-trained on knowledge graphs. Given the popularity of this line of research, this paper is very timely, and reviewers found it well-executed on the whole. The study has some limitations in terms of the models and KGs considered, but I think it is worth accepting to AKBC.


Decision: Accept