From: ICLR.cc/2023/Conference/Paper3974/Authors
title: Submission Withdrawn by the Authors

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: Added Performance on TriviaQA
comment: We are glad to inform the reviewer that we have completed our experiments on TriviaQA and posted the results in the general response. Please refer to “General Response on TriviaQA Performance” (on top of the page) for details.

Thank you for your patience and understanding!

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: Added Performance on TriviaQA
comment: We are glad to inform the reviewer that we have completed our experiments on TriviaQA and posted the results in the general response. Please refer to “General Response on TriviaQA Performance” (on top of the page) for details.

Thank you for your patience and understanding!

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: General Response on TriviaQA Performance
comment: Following the comments from Reviewer JdUP and gouD, we are happy to present the performance of our approach on TriviaQA benchmark to show its generality. We reuse the preprocessed TriviaQA dataset from DPR [1] and trained the retrievers (i.e. DPR and DPR+PiCL) using in-batch negatives and BM25 negatives. Note that we include only the reported performance of ANCE on TriviaQA from Xiong et al. [2], and that we aren’t able to reproduce their results on our setting due to limited computation resources. The results are shown in the table below:

|  | || | |TriviaQA| | |
|-|-|-|:-:|:-:|:-:|:-:|:-:|
| **Retriever** | **PLM** | **#N** | **Top-1** | **Top-5** | **Top-20** | **Top-100** | **MAP@10** |
| | | | | | | | |
| DPR       | $\text{BERT}_{\text{base}}$ |127|38.72|62.56|74.71|82.29|31.50|
| DPR       | $\text{BERT}_{\text{base}}$ |127+128|54.26|71.53|79.66|85.34|43.29|
| DPR+PiCL  | $\text{BERT}_{\text{base}}$  |127|43.02|64.05|74.67|82.24|34.10|
| DPR+PiCL  | $\text{BERT}_{\text{base}}$  |127+128|**56.93**|**72.32**|**80.46**|**85.72**|**45.15**|
| | | | | | | | |
| ANCE      | $\text{BERT}_{\text{base}}$ | $\text{-}$ | $\text{-}$ | $\text{-}$ |80.3|85.3| $\text{-}$ | $\text{-}$ |

We observe that DPR+PiCL shows better performance than DPR under all settings. Particularly, we see higher MAP@10 as well as Top-1 and Top-5 retrieval accuracies from DPR+PiCL, indicating that DPR+PiCL tends to be more precise. We also find it surprising that DPR+PiCL trained with BM25 negatives shows competitive performance to ANCE [2], which relies on a compute-intensive pipeline for negative mining. We believe the above results are promising given that PiCL can be orthogonally applied with negative mining approaches for further improvement.

We once again thank all reviewers for their insightful comments on our work.

[1] Karpukhin et al. Dense Passage Retrieval for Open-Domain Question Answering, EMNLP 2020

[2] Xiong et al. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval, ICLR 2021

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: General Response
comment: We thank all reviewers for their thoughtful feedback on our work. We have updated our manuscript and included additional experiment results to clarify some of the points raised by the reviewers. We summarize the major changes in our revised paper as follows:

1. We include a more detailed analysis on the answer-awareness of dense retrievers in Section B in the appendix.
2. We add the performance of Fusion-in-Decoder models [1] paired with DPR and PiCL retrievers in Table 3.
3. We include a new table on the performance of DPR and DPR+PiCL implemented with pretrained ERNIE [2] in Section C in the appendix.

In addition, we are working on additional experiments to examine the effectiveness of our PiCL method on another ODQA benchmark, TriviaQA. While we could not include the results in the updated paper, we will make sure that we present them to the reviewers as soon as the experiments are completed. Finally, we would like to note that we have updated the retrieval performance on the NQ benchmark due to configuration errors in Table 2. We have adjusted the values such that they are not only free of errors but also comparable to retrieval baselines tested under the standard DPR setting [3], which we could not deliver in our first submission. 

We thank the reviewers once again for their time and efforts. We would like them to know that we’re strongly encouraged by their positive comments on our ideas, and that their feedback has helped us a lot to further improve our work.

[1] Izacard et al. Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering, EACL 2021

[2] Zhang et al. ERNIE: Enhanced Language Representation with Informative Entities, ACL 2019

[3] Karpukhin et al. Dense Passage Retrieval for Open-Domain Question Answering, EMNLP 2020

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: Response to Reviewer gdTg (Part 2)
comment: (Continuing our discussion on the effect of question types)

Among all question types, we see that DPR shows particularly low AARs on “where”, “what”, and “who” questions, which tend to include answers that refer to named entities, i.e. names of people, locations, and objects. Our hypothesis is that DPR fails to identify the presence of named entities that serve as the clue to answer passages. To further illustrate this problem, we include an example from the retrieval results of a DPR on NQ test set: 

|||**Question: Who died in the plane crash greys anatomy**|
|-|:-:|:-:|
|**Passage Type**|**Title**|**Text**|
|**Gold passage**|Flight (Grey's Anatomy)|Flight" is the twenty - fourth and final episode of the eighth season of the American television medical drama Grey 's Anatomy … six doctors from Seattle Grace Mercy West Hospital who are victims of an aviation accident fight to stay alive, but **Dr. Lexie Grey** ( Chyler Leigh ) ultimately dies. …|
|Top-1 DPR|Paul-Louis Halley|Socata TBM 700 aircraft crash on 6 December 2003, during an approach to Oxford Airport. The plane went into an uncontrolled roll, killing Halley, his wife, and the pilot. …|
|**Top-9 DPR**|Flight (Grey's Anatomy)|(Patrick Dempsey), and Dr. Mark Sloan (Eric Dane) desperately fight to stay alive. Meredith is relatively unscathed, while the rest have serious injuries: the pilot, Jerry (James LeGros), has a major spine injury, ... Shepherd is sucked out the side of the plane and awakens alone in the wood; his mangled hand having been pushed through the door of the plane. However, none are in as bad shape as **Lexie**, who is crushed under|

As shown in the table, while DPR is capable of retrieving passages with relevant semantics (e.g. aircraft crash, plane, and killing,) it fails to identify key named entities in the question such as Greys Anatomy. Contrary to DPR, DPR+PiCL learns to differentiate answer passages from their counterfactual counterparts in which key entities are not present. In some sense, our approach is in line with previous methods based on salient span masking [1] such as MSS [2], where the retriever is trained by predicting masked salient spans like named entities with the help of a reader.

> There are many better ODQA systems, like Fusion-in-Decoder and RocketQA (the authors mentioned the latter too). I feel it is not quite enough to just demonstrate only on DPR with BERT-base.

We agree with the reviewer that better retriever-reader baselines should be considered in Table 3. To address this point, we include additional experiments where we use T5-base implementations of Fusion-in-Decoder models [3] as readers. We paired FiD readers fine-tuned on NQ with DPR and DPR+PiCL and compared Exact Match (EM) scores of the resulting retriever-reader pipelines. Below is a summary of the results:

|Retriever|\# N| |Exact Match Score||
|-|-|:-:|:-:|:-:|
|||**Top-5 passages**|**Top-20 passages**|**Top-100 passages**|
|**DPR**|127|31.99|39.11|43.82|
|**DPR+PiCL**|127|34.27|41.47|44.85|
|**DPR**|127+128|38.31|43.13|45.37|
|**DPR+PiCL**|127+128|40.22|44.32|47.65|

Note that advanced ODQA systems (e.g. T5-large implementations of FiD paired with RocketQA) usually require large computational resources and are not viable under our settings. For such practical reasons, we leave the integration of PiCL into these model-centric approaches as future work.

> I feel it is not quite necessary to consider the counterfactual passage awareness for ODQA systems. In general, the passages are part of the ODQA system - it would be hard for an attacker to alter passages (instead of questions) of an ODQA system. 
> It is good that the overall performance is improved, but I feel the AAR score improvement lacks a real use case.

We agree with the reviewer that our counterfactual sampling strategy has practical limits as adversarial attacks to ODQA systems. However, we would like to clarify that we do not assume our counterfactual simulation as a plausible attack scenario on ODQA systems. The purpose of AAR experiments is to test if dense retrievers are able to differentiate answerable passages from unanswerable ones. By training an answer-aware retriever, we aim at the practical issue of false negatives in common ODQA scenarios, where retrieved passages provide somewhat relevant contexts but lack exact answers to the questions. We believe that the notion of an answer-aware retriever promises 1) efficiency in the subsequent reader and 2) robustness against noisy contexts from web-scale real-world corpora, i.e. spams. [4]

[1] Guu et al. Retrieval Augmented Language Model Pre-Training, ICML 2020

[2] Sachan et al. End-to-End Training of Neural Retrievers for Open-Domain Question Answering, ACL 2021  

[3] Izacard et al. Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering, EACL 2021

[4] Piktus et al. The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus, arXiV 2021

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: Response to Reviewer gdTg (Part 1)
comment: Thank you for your constructive feedback on our work! We address the concerns raised by the reviewer as follows:

> The paper lacks a detailed analysis of the answer awareness finding. The only example is in Figure 1 and we don't know the scores change there;. 
> also NQ has many question types (what, when, where, how, how many,…) and I would like to see a breakdown of which types are the most/least answer aware.

We thank the reviewer for raising this point. We acknowledge that certain aspects of our answer-awareness findings, e.g. score changes between original and counterfactual passages, should be discussed in more detail. We also agree that the question type is a key linguistic feature that may affect the answer-awareness of dense retrievers, as different question types in training samples may introduce different biases. Hence, we will now present a more extensive analysis on our answer-awareness findings below.

To provide an overview of how relevance scores differ between original and counterfactual passages, we expand upon the answer-awareness analysis in Section 5.3 (see Figure 5) and measure the average difference in relevance scores $sim(q,p^+)$ and $sim(q,p^*)$. (Note that $q$, $p^+$, and $p^*$ denote a question, a positive passage, and a counterfactual passage, respectively). We are specifically interested in whether DPR+PiCL tends to put larger differences between original and counterfactual passages compared to DPR. For that, we reuse the gold $(q,p^+,p^*)$ triplets from our AAR experiments and compute the mean score difference $sim(q,p^+) - sim(q,p^*)$ measured from both DPR and DPR+PiCL. For our analysis, we consider two subsets of samples, one that satisfies $sim(q, p^+)>sim(q, p^∗)$ (i.e. Answer-aware) and another that doesn’t (i.e. Answer-unaware).We summarize the results in the table below:

||DPR|DPR+PiCL|
|:---------:|:---------:|:---------:|
|Answer-aware|1.99|3.38|
|Answer-unaware|-0.98|-1.12|
|ALL|1.18|2.57|

We observe that DPR+PiCL generally shows larger score differences than a vanilla DPR, particularly for samples where the retrievers are answer-aware, i.e. $sim(q,p^+) > sim(q,p^*)$. While the differences may seem small, we would like to point out that even a slight decrease in the relevance score leads to a significant drop in the rank of a passage. That said, the results in the above table suggest that DPR+PiCL is better at distinguishing between answerable and counterfactual passages. We also compare the distributions of score differences from DPR and DPR+PiCL using a histogram and find that DPR+PiCL tends to assign higher relevance scores to answerable passages than counterfactual passages. We kindly suggest the reviewer to refer to Section B of the appendix in the updated paper.

To better understand if the answer-awareness of a retriever depends on certain linguistic features, we further investigate the effect of question types on the answer-awareness of DPR. Specifically, we reuse the gold $(q,p^+,p^*)$ triplets from AAR experiments and classify them into six subsets based on question types. For now, we identify the type of a question simply by looking for exact matches of the question words: how, what, when, where, which, and who. For each of the six question sets, we measure AARs of retriever models, i.e. DPR and DPR+PiCL. We present a summary of the results below:

|Question Type|\# of Questions|AAR_DPR (%)|AAR_DPR+PiCL(%)|
|:---------:|:---------:|:---------:|:---------:|
|How|114|71.93|85.09|
|What|228|67.54|77.19|
|When|295|80.68|87.46|
|Where|146|65.07|81.51|
|Which|41|73.17|85.37|
|Who|547|70.02|80.07|

We observe that AARs of dense retrievers do vary significantly across different question types. Particularly, DPR shows large gaps in AARs between different question types, ranging from 65.07% to 80.68%. On the other hand, DPR+PiCL shows 1) significant improvements in AAR over a vanilla DPR for all question types and 2) smaller variance in AARs across different question types.

(We continue our discussion in the follow-up response.)

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: Response to Reviewer JdUP
comment: We are grateful for your constructive feedback on our work. We agree with the reviewer that additional experiments on other ODQA benchmarks are required to show that our method is generalizable. Hence, we are working to evaluate the performance of DPR+PiCL on another popular ODQA benchmark TriviaQA. While we could not report the results on the updated manuscript since it takes a lot of time to conduct such experiments on limited hardware, we plan to deliver the results as soon as we have them before the end of the rebuttal period.

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: Response to Reviewer PGpM 
comment: > The experiment in Table 3 can be improved by running different PLM for PiCL method. So far, the performance of some baselines such as RocketQA in Top-5 results are better than proposed method. It is not clear whether the worse performance is caused by the ERNIE language model or the retrieval method

Thank you for pointing this out. Previous studies on dense retrieval, including RocketQA [1], show that using better PLMs such as ERNIE for retriever training leads to better performance. To study the effect of the choice of PLMs on the performance of retrievers trained with PiCL, we have implemented DPR+PiCL models with pretrained ERNIE and measured their performance on the NaturalQuestions benchmark. The results are shown below:

|Retriever|PLM|\# N|Top-1|Top-5|Top-20|Top-100|
|---|---|---|---|---|---|---|
|**DPR+PiCL**|BERT|47+48|45.38|66.79|78.53|85.04|
|**DPR+PiCL**|ERNIE|47+48|47.29|68.92|79.53|85.98|
|**DPR+PiCL**|ERNIE|127+128|49.5|69.36|80.06|86.23|

We observe that the ERNIE implementation of DPR+PiCL outperforms its original BERT implementation, and that its performance increases with the number of negatives (i.e. in-batch and BM25 negative passages) used to train the model. While RocketQA does achieve a substantial performance gain over other baselines, it relies heavily on significantly large batches and a compute-intensive negative sampling pipeline, which makes it infeasible to reproduce the model due to tremendous computational costs.

[1] Qu et al. RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering, NAACL 2021

From: ICLR.cc/2023/Conference/Paper3974/Authors
title: Response to Reviewer gouD 
comment: We thank the reviewer for your motivating feedback. In response to your suggestion, we are testing our approach on another ODQA benchmark TriviaQA to validate the generality of our method. It will take a while before we have all the results since we are running extensive experiments with limited computational resources, but we aim to present the results of our experiments as soon as we have them before the end of the rebuttal period.

From: ICLR.cc/2023/Conference/Paper3974/Reviewer_gdTg
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: The paper studies counterfactual learning for the retriever in ODQA systems. It finds that after removing the sentence containing the correct answer, DPR still often gives the passage a high score, sometimes (25%-32%) higher than the original passage. It proposes to mitigate this problem by using the counterfactual passages in training, called the PiCL method. PiCL uses the counterfactual passages as hard negatives w.r.t the positive passages, and pseudo-positives w.r.t the negative passages. The final PiCL loss combines a modified DPR loss, a hard negative loss, and a pseudo positive loss. Experiments show that PiCL can improve the performance (retriever and final QA accuracy) of DPR, while also improving the answer-awareness scores.
strength_and_weaknesses: Strength: 

The paper has a somewhat surprising finding that removing the answer sentence does not affect the score too much. Normally I would expect the answer type can affect the relevance score. For example., for a question containing "how many" like in Figure 1, passages with numbers will rank higher than those without. This can be a valuable finding to the community. Also, the experiments show solid improvements.

Weakness: 
1. The paper lacks a detailed analysis of the answer awareness finding. The only example is in Figure 1 and we don't know the scores change there; also NQ has many question types (what, when, where, how, how many,…) and I would like to see a breakdown of which types are the most/least answer aware. This is critical to the paper.
2. The performance improvement is only tested on the original DPR with BERT-base and extractive reader. There are many better ODQA systems, like Fusion-in-Decoder and RocketQA (the authors mentioned the latter too). I feel it is not quite enough to just demonstrate only on DPR with BERT-base. I think it is at least necessary to try a seq2seq decoder (like T5) as the reader, which yields generally much higher results.
3. I feel it is not quite necessary to consider the counterfactual passage awareness for ODQA systems. In general, the passages are part of the ODQA system - it would be hard for an attacker to alter passages (instead of questions) of an ODQA system. This is different from other counterfactual works that attack the input directly. From another angle, if we change the counterfactual passages to just changing the answer word to something else, it can be natural that ODQA lacks counterfactual awareness in this case. It is good that the overall performance is improved, but I feel the AAR score improvement lacks a real use case.
clarity,_quality,_novelty_and_reproducibility: The paper is nicely written and easy to read.

Method-wise, the novelty is limited. The main novelty is in the answer awareness finding.

Code is included with the submission.

summary_of_the_review: Overall, I think the paper has a (somewhat) surprising finding but lacks the evidence to support it.
correctness: 2: Several of the paper’s claims are incorrect or not well-supported.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3974/Reviewer_JdUP
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper discusses the answer-unawareness of dense passage retrieval, which fails to distinguish unanswerable counterfactual passages. To address this issue, the authors propose PiCL, a novel representation learning method with counterfactual samples as pivots between positive and negative samples. The proposed method enhances the answer awareness of dense retrievers, which further improves the retrieval and ODQA performance. 
strength_and_weaknesses: Strength
1. A simulation with simple counterfactual examples effectively shows the answer-unawareness of DPR. 
2. The proposed approach that considers counterfactual samples as pseudo-positives and hard negatives is novel. 
3. Experiment results show the effectiveness of the proposed method, in terms of both retrieval and QA. 

Weakness
1. The analysis and experiments are mainly conducted on NQ. Additional experiments on other ODQA benchmarks further improve the generalizability of answer-unawareness and the proposed method. 

clarity,_quality,_novelty_and_reproducibility: The paper is well-written. The motivation based on the observation is also clear. The provided code further supports reproducibility. 
summary_of_the_review: This paper presents an interesting observation on the answer-unawareness of dense retrievers with counterfactual examples. The proposed approach to prevent answer-unawareness seems novel and effective, although experiments are limited in NQ. 
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3974/Reviewer_PGpM
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: In the paper, the authors empirically observe that recent dense retrieval models based on DPR often rank unanswerable counterfactual passages higher than their answerable original passages. Based on the observation, the authors propose a new representation learning approach for passage retrieval that leverages counterfactual samples as pivots between positive and negative samples in their learned embedding space. Last, they incorporate PiCL into the retriever training to show the effectiveness of PiCL on ODQA benchmarks and the robustness of the learned models.
strength_and_weaknesses: Strength:
1. The paper is well-written and easy to follow.
2. The motivation for retrieving the answerable passages is promising.
3. The experiment is well performed and show some interesting observations.

Weakness:
1. The experiment is well done but the explanations on the counterfactual loss and counterfactual sample are not clear.
2. The experiment in Table 3 can be improved by running different PLM for PiCL method. So far, the performance of some baselines such as RocketQA in Top-5 results are better than proposed method. It is not clear whether the worse peformance is caused by the ERNIE language model or the retrieval method.
clarity,_quality,_novelty_and_reproducibility: The paper is well-written with high quality. The overall idea is novel. And it's feasible to reproduce the results.
summary_of_the_review: The paper is well-written and easy to follow. The motivation for retrieving the answerable passages is interesting. The only concern is the experimental settings and the corresponding explanations.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3974/Reviewer_gouD
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper observes that dense retrievers based on DPR often fail to rank answerable passages higher than their counterfactual counterparts. To address such answer-unawareness in dense retrievers, the authors propose counterfactually-Pivoting Contrastive Learning (PiCL), a novel representation learning approach for passage retrieval that repurposes counterfactual samples as pivots between positives and negatives in their learned embedding space. The extensive experiments show that applying PiCL on DPR enhances the model performance on the ODQA benchmark and improves its robustness to unanswerable, counterfactual passages.
strength_and_weaknesses: Strengths:
1. The idea of leveraging the counterfactual passages as additional training hard negative samples is novel, and I did not see such in prior works.
2. From Tables 2 & 3, the improvement of the proposed PiCL seems significant.
3. The presentation of this work is excellent. The rich use of diagrams and tables makes this paper easy to understand.

Weaknesses:
To further prove the generality of the proposed PiCL, the authors are better able to do experiments on more benchmarks in addition to Natural Questions (kwi).
clarity,_quality,_novelty_and_reproducibility: The work is well-written and novel. I examined the code provided by the author and am sure that the reproducibility should be acceptable.
summary_of_the_review: In my opinion, the proposed PiCL method is novel, and the experimental results successfully demonstrated the effectiveness of the proposed method. I am favorable this paper will be accepted.
correctness: 4: All of the claims and statements are well-supported and correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 4: The contributions are significant, and do not exist in prior works.
flag_for_ethics_review: NO.
recommendation: 8: accept, good paper