Paper: Open-Domain Text Evaluation via Contrastive Distribution Methods (ICLR-2024)

Reviewer comment (Experiments):
For the generative CDM, there's no direct comparison with the expert/amateur model employed solely for constructing negative samples.

Author response:
1. (Soundness: Overlooked baselines) Thank you. We've conducted extensive study and reported the results in the updated draft's appendix. Please refer to Appendix A.4, Table 7.

Reviewer comment (Experiments):
These baselines are essential for establishing the paper's originality (applying the contrastive decoding idea into text generation evaluation) and shouldn't be omitted.

Author response:
1. (Soundness: Overlooked baselines) Thank you. We've conducted extensive study and reported the results in the updated draft's appendix. Please refer to Appendix A.4, Table 7.

Reviewer comment (Experiments):
In fine-grained evaluation, where users arbitrarily set the evaluation perspective, it's unclear if the negative sample creation strategy can consistently control construction towards a specific aspect.

Author response:
2. (Limitations: Fine-grained Evaluation) We admit that this is indeed the case for Generative CDM. For Discriminative CDM, fine-grained evaluation can be achieved by re-aligning the score via training the learnable pooler on a small set of samples that illustratively defines the fine-grained aspect.

Reviewer comment (Evaluation):
In holistic evaluation, which involves comparing generated outputs to a given input and assigning ranks, the paper notes that state-of-the-art Language Models like LLAMA-1/2 7b are too strong to act as amateur models. This suggests the approach is best suited for weaker models and raises the question of whether it can effectively evaluate outputs from more advanced models, potentially limiting its applicability.

Author response:
3. (Limitations: Holistic Evaluation) This is an insightful comment. We thank the reviewer for pointing this out to us. One of our future works is exactly on this direction, to study the multi-model (>2) case of CDM, to train a CDM system that consists of a series of rather small models (15M to 1.5B) and whether it can extrapolate the decision surface to correctly ranking stronger models (3B, 6.9B, 7B etc) accordingly to their performance. For this paper, though, we tend to keep only the core demostration and verification of effectiveness in a comparitively simplified setup. We respectfully beg for understanding from the reviewer.

Reviewer comment (Experiments):
The experiments were only conducted on T5 models, which raise the question of how well the decoder-only models perform with CDM.

Author response:
1. (More Experimented Models) Thank you. We've conducted extensive study and the results can be found in Appendix A.4. Decoder-only model generally suffers from the problem that it can only make use the pre-text to manipulate/predict the post-text, limiting its application in both Generative CDM and Discriminative CDM. But we show that this can be slightly remedied by finetuning autoregressive decoder-only language models to perform infilling, following the practice described in https://arxiv.org/abs/2207.14255 . Yet this remedy is not sufficient to essentially close the gap to those models that are pretrained to perform infilling, like T5.

Reviewer comment (Experiments):
The proposed method relies heavily on the divergence between two distributions for quality prediction and it seems (1) picking the right amateur model may require extra effort, and (2) whether the method generalizes to larger model scales (e.g., >7b) is unknown. As noted by the authors, “Note that the smallest configurations… such as LLaMa-1/2(Touvron et al., 2023a;b), can still be too strong to serve as the amateur model in CDM.”

Author response:
2. (Regarding selection of Amateur Models) We find that in general using a smaller amateur is always helpful, and in the extensive study shown in the appendix, we find that when the expert model is as large as 3b or 11b, the performace of CDM on the dialogue evaluation problem is already coming to a saturation. We also conduct experiments using LLaMa 2 7b against 13b, and find that a 7b model is indeed too strong as an amateur model.

Reviewer comment (Experiments):
Have you tried switching both amateur and expert models to larger scales? Will contrasting 7B and 70B output distribution yield good correlation with humans?

Author response:
1. (Larger Scales of Models) We've included some results contrasting T5-small against T5-11b, and also results from LLaMa 2 7b against 13b. Unfortunately we can't afford to adapt LLaMa2 70b for our purpose. We beg the reviewer's pardon on this.

Reviewer comment (Evaluation):
Performance seems especially bound by the expert model. This is not always the case for reference-free evaluation. Perhaps it is worth analyzing the cases where the metric fails.

Author response:
(Performance bound by the expert model) Yes, this is our finding too. In the extensive study we further extend the size of the largest studied T5 model to as large as 11b and we observe that the performance seems to be saturating.

Reviewer comment (Evaluation):
Although main results on multiple tasks are promising, the paper lacks analysis (qualitative or quantitative) to convince the reader CDM is capturing desirable properties of the outputs.

Author response:
(Lacking Analysis and/or Illustrative Examples) Thank you for this suggestion. Due to the time constraint and paper length capacity, we're not able to incorporate more illustrations and detailed qualitative analysis into the draft. We're continuously working on this and more results will be present in the camera ready version.

Reviewer comment (Evaluation):
Negatives in generative CDM are created at the segment level. My impression is this would lead to grammatically similar but semantically different negatives. Stronger negatives would likely differ in style and structural complexity. Even basic ordering differences, such as swapping "A is a B" with "B is a A" are not likely to be handled.

Author response:
(Negative Sampling Generation) We respectfully disagree with the reviewer that we're only doing manipulation in the segment level. We've conducted ablation study to compare the performance of doing negative sampling in segment, utterance and guided positions using AMR, and do the manipulation for 1 steps or multiple steps. Please refer to Section 4, Table 3 and Appendix, Table 7.

Reviewer comment (Evaluation):
The metric is evaluated solely against human preferences. It would be helpful to see if the metric can properly order a set of baseline models by their relative strength. Although it's possible I may have misunderstood if this is already being done or not.

Author response:
(Metric Alignment and Partial Order of Models) We thank the reviewer for this suggestion. This is definitely interesting and we're doing it as one of our follow-up works of this paper. As described in the future work section, this follow-up work will focus on the multi-model version of CDM, and we're testing its capabilities of ranking models accordingly to their respective capabilites.

Reviewer comment (Experiments):
Is it really a safe assumption that small is worse than large? Aren't larger models also harder to train? I understand in practice your choice of T5 probably does obey this pattern, but naively scaling a model up or down and keeping most of the hyperparams fixed may not follow this pattern.

Author response:
(Whether the partial order assumption generally holds) We definitely agree that this should be the case. We stay agnostic of whether this assumption generally holds for any aspect of text quality, but we tend to believe this assumption generally holds for a considerably large enough range of aspects. Our method is trying to build towards cases where the assumption safely holds.

Reviewer comment (Experiments):
Can we do paraphrasing instead of segment replacement?

Author response:
(Paraphrasing against Segment Replacement) This is insightful and actually links to some previous results in edit-based text style transfer. To summarize, these results indeed show that when the target is to accurately reflect the opposite information of the current context, doing sentiment-reversed paraphrasing is indeed more effective. However, our case here is a little bit different - we don't actually care too much about preserving the semantics of the manipulated region. Instead, we only care what could be properly substituting the deleted part that best "confuses" a potentially weaker discriminator while still making the full text a negative sample. We do think it's possible, but the formulation and training will be more complex and we doubt the performance gain in this negative sample synthesis problem.

Reviewer comment (Experiments):
Did you consider using multiple seeds of amateur/expert? Do you expect it to influence results?

Author response:
(Random Seeds Issue) Yes, we run the main results using different random seeds for 10 times and make sure the conclusion holds.

Reviewer comment (Experiments):
Have you considered using multiple contrastive evaluations combined? For instance, could train particularly biased amateur. For inspiration, see "He He et al. Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual"

Author response:
(Intentionally Manipulated Amateur Model) This is possible, but it could slightly deviate from the paper's main focus on showing that "quantification of LM scaling law can be used for sequence-level evaluation purposes".

Reviewer comment (Experiments):
I am not certain if the partial order assumption holds for different aspects of the text quality. For example, the authors evaluate CDM for the overall quality and coherence for dialog and factuality for summarization. It would be interesting to see if the partial order assumption holds for factuality, for examples, in the first place. The authors should report performance of expert and amateur models on different aspects.

Author response:
(Whether the partial order assumption generally holds) We definitely agree that this should be the case. We stay agnostic of whether this assumption generally holds for any aspect of text quality, but we tend to believe this assumption generally holds for a considerably large enough range of aspects. Our method is trying to build towards cases where the assumption safely holds.

Reviewer comment (Experiments):
The authors have tried various sizes of T5 models. I think it might be of interest to include other model families in their comparisons.

Author response:
(Other model families) Thank you, we've conducted further experiment on a larger range of models, including larger sizes of T5 and decoder-only autoregressive models (Pythia, LLaMa 2). Due to the constrained time, we've only been able to present the extensive results on the dialogue evaluation data, but we plan to continuously work out all results on summarization too before the camera ready.

Reviewer comment (Evaluation):
For summarization, why are the authors focusing on factuality only? What about other dimensions: for example, Coherence, Factuality, Fluency, Informativeness? It would have been nice to see how CDM does on various aspects. Also the comparison is limited to very few other metrics. Please see https://openreview.net/forum?id=OIe3kpwl40D for a better experimental setup.

Author response:
(Experimental Setup regarding summarization) We focus on factuality evaluation because we find this could be a challenging aspect for abstractive summarization models, following a few previous works like QAGS, FactCC. We agree that it is definitely valuable and interesting to extend the metric learning to more aspects, but we beg the reviewer's pardon as we struggle to fit into the paper capacity limit.

Reviewer comment (Presentation):
The captions in Table 1 and Table 4 are not very clear. Is the data in the first block used to train T5 or do we use off-the-shelf T5 checkpoint? What is the “/” in 1200/3600 and 17567/2078?

Author response:
(Data Table Issues) Yes, as is indicated by the information in the parentheses, we are training amateur and expert models on the CNN/DailyMail data and testing the composed CDMs on QAGS and SummEval datasets. "/" indicates different sub-sets of the data (TopicalChat: 17567 conversations; PersonalChat: 2078 conversations; 1200 summaries in SummEval with 3600 annotated factuality scores by CoCo https://aclanthology.org/2021.findings-emnlp.10.pdf ).

Reviewer comment (Writing):
“Previous works report their performance inconsistently in either Spearman/Pearson correlation or an accuracy score with 0/1 quantization of the annotations. We adopt 0/1-quantization and report the accuracy of each baseline/model.” → This is not at all clear. It would be good to clarify this.

Author response:
(Result Reformatting Issues) Some previous works report their performance by treating factuality evaluation as a balance-labelled binary classification problem and report the accuracy (FactCC, FalseSum, QAGS), whereas some report the Spearman and Pearson Correlation (CoCo). To provide a clearer comparison, we tried to reproduce each of the compared baselines and unify the results into the accuracy measure. We binarize CoCo's results into discrete True/False prediction using a threshold determined by top50% of the CoCo score, as is described in their paper.

Reviewer comment (Presentation):
Section 4.2.2: Table 6 -> Table 5

Author response:
(Minor Writing Issues) Thank you. We've fixed them accordingly.

Reviewer comment (Writing):
sum_log m -> sum_m in Section 3.4.2.

Author response:
(Minor Writing Issues) Thank you. We've fixed them accordingly.

Reviewer comment (Experiments):
The method design of negative sample generation seems not to make sense for me. It is commonly known that high-quality negative samples may promote the performance of discriminators. Even if low-quality samples are needed, large language models (LLMs) such as GPT-4 also have the ability to generate texts with controlled qualities via prompt design. Thus, I’m curious about the necessity to train a model on task-specific data to construct negative samples, which may degrade the generalization ability of the proposed method.

Author response:
We definitely agree that high-quality negative samples may promote the performance of discriminators. However, it is also not the case that ``negative samples'' almost as high-quality as the original golden sample would still promte the performance of the discriminator. In other words, the goal is that when the synthesized samples could still be considered negative, we want to maximize our chance of fooling weak discriminators (in other words, being close to the original distribution). One of the purpose of this paper is to focus how could we properly formulate such a goal (which essentially a min-max game) such that we can steadily expect a more efficient negative sampling process, following some pricipled practice. In this paper, our finding is to simply using CDM and enlarging the discrepancy of the amateur and expert model.

Reviewer comment (Experiments):
The baselines used in the experiment are somewhat outdated with the rapid development of LLM-based evaluation metrics. Since the authors have already cited two representative works including UNIEVAL [1] and G-EVAL [2] in the introduction and claim that they have limitations, these two methods should be surely added to the baselines for direct comparison.

Author response:
Thank you for your valuable feedback. We've included results from a few more recently published models in the updated draft. G-Eval is definitely the SOTA among all the studied metrics, yet we argue this could be a direct result that GPT-4 is explicitly trained to align with human opinions, making it less of a fair comparison. While T5 is rather an older model family, it is still very competitive (especially the larger configurations of it) in many import aspects. Detailed comparison and discussion can be found in the Appendix.