Paper: Understanding Retrieval Augmentation for Long-Form Question Answering (ICLR-2024)

Reviewer comment (Evaluation):
I do not think that the superficial level statistics provide much meaningful information about the RAG pattern in general or even these specific versions of it. It is self-evident that when provided with information to contextual the answer then generative language models have different linguistic properties. This has been studied before.

Author response:
Re: take-aways from surface statistics   
Regarding major takeaways from measuring the surface statistics, please refer to our response to all the reviewers. Our analysis presents higher-level patterns that hold across models (including GPT-4). We also compare prepended documents of various degrees of relevance, and no prior work has been done to study how relevance of the prepended documents could affect the generated answers.

Reviewer comment (Experiments):
If I understand it correctly, then the dataset that was collected is specific to the algorithms used in this analysis. Since the answers are labeled this means that to apply this dataset to a new algorithm (or even a new generation/inference run) will require some machinery to transfer those labels. This can be challenging but I did not see a discussion of this process in the paper. So it is limited to only "answer attribution" methods.

Author response:
Re: Specificity of our analysis   
As the reviewer comments, the attribution annotations are collected for the outputs of the selected models / algorithms. We do acknowledge that human judgments are the most accurate for evaluating attribution, and this is why we collect human annotations in the first place; however, as human evaluation is expensive and difficult to scale, we explore automatic attribution prediction, a cheaper proxy substitute. Researchers can evaluate the attribution of their answers using the highest performing NLI model, which we identify in Section 7.2. While it would be nontrivial to transfer our collected labels to the outputs of newer models, our annotated data can provide a benchmark for developing high-performing NLI models which can provide reliable estimates for newer models.

Reviewer comment (Experiments):
Many approaches to this problem couple the answer and citation/attribution generation. Besides that, the dataset is rather on the small side for datasets on this topic.

Author response:
Re: size of dataset   
We cover a relatively small number of questions (a total of 100 questions), but we collect *sentence-level* annotations on six different generation settings. We ended up with around 4,000 (answer sentence, attribution label) pairs in total. We do have a significance test showing that the linear correlation between location of answer sentences and their supporting sentences in the documents (Figure 3(a)) is statistically significant, and will include it in the paper.

Reviewer comment (Evaluation):
I find it somewhat surprising that the authors did not analyze the superficial statistics in light of the dataset (by filtering on attribution accuracy etc.).

Author response:
[Q2] : *Wouldn't conditioning the SLS analysis on correct vs. incorrect results (possibly filtering with your dataset) provide more actionable information on the RAG for LFQA setup?*   
Thank you for your suggestion! We can compare surface answer statistics between answers that are mostly “supported” by the documents and answers that are not. We will include this analysis in the revision.

Reviewer comment (Writing):
While the paper demonstrates good motivation and understanding of the problem so-called long-form question answering, I have a different interpretation of the term “long-form”. I thought the problem is referring to “long length/width form” or “long structured/semi-structured tables”, which pose a greater challenge for current LLM-based retrieval systems. Therefore, I question whether “long-form” is an appropriate term to accurately define this problem.

Author response:
Thanks for letting us know about the confusion surrounding the terminology. The task long-form question answering (LFQA) was first proposed by Fan et al. (2019) [1], which defines LFQA as “generating paragraph-length explanations in response to complex, diverse questions”. The term has been used consistently in a range of followup work. We will make this definition clear in the revision.

Reviewer comment (Experiments):
Since the tested dataset consists of a relatively small number of questions (271), it raises the question of why the entire dataset was not utilized for the experiments.

Author response:
In the “Dataset” paragraph in Section 3, we wrote “We use the entire test set released by WebGPT (Nakano et al., 2021) (271 questions) for automatic evaluation (Section 4, 7.2), and randomly sample 100 questions to collect manual attribution annotations (Section 5).” As stated, we only use a subset of the test set to collect human annotations. The reason for taking a subset is the limited budget: the total cost of the experiments is already 5886.60 USD (which we will include in the paper), and annotating the whole test set is prohibitively expensive. Thus we propose to approximate human annotation with NLI models in Section 7, and we report answer attribution predicted by the T5 model in Figure 4(b), which is evaluated on all examples in the test set.

Reviewer comment (Presentation):
The main findings of this work would deserve being stated more clearly. While the annotation of supporting sentences across multiple documents is of interest to the field, this is not the paper's main listed contribution. The paper makes a number of observations on how various retrieval-augmented LFQA systems behave, but without connecting them clearly to a consistent set of conclusions, or giving actionable guidance for researchers designing retrieval-augmented LFQA solutions.

Author response:
Thank you for your suggestions on presenting our findings better!  We will make the writing clear to state the hypothesis for each of our analysis. For answer surface statistics computation, our hypotheses were that 
(1) Prepending random documents should impact the surface statistics less than prepending relevant documents. (2) Retrieval augmentation will make the generated answer more specific (e.g., mentioning specific named entities) and diverse, leading to higher perplexity and less repetition (lower self-bleu). 

For Figure 3 (a)(b) showing the location of supporting sentences, our hypothesis was that the order of information presented in the documents would align with the order of information presented in the generated answers. For Figure 2 which shows the similarity of answers between different generation settings, our hypothesis was answers generated with more relevant documents would be more different from the answers generated without any document. 


Re: Why not link analysis to quality of LFQA systems?   
This is a great question! Ideally, we would like to analyze how various design choices of retrieval augmentation impacts the performance of LFQA systems. However, this is incredibly hard. Recent ACL paper [1] showed that the “end-to-end human-perceived quality” for LFQA  is elusive, as even domain experts disagree on ranking different answers. This is caused by the complexity of LFQA evaluation, where a wide range of factors, such as completeness and ease of understanding, are considered, and different people weigh factors differently. They suggest that both human and automatic evaluation should focus on a single aspect. This is why we focus on attribution of the answers, which is widely studied by previous and concurrent works, for its more straightforward evaluation (Bohnet et al. (2022) [2], Yue et al. (2023) [3], Gao et al. (2023) [4], Liu et al. (2023) [5]) 


Re: Actionable conclusions:   
Please refer to the response to all the reviewers.

Reviewer comment (Presentation):
Why is figure 4a a box plot? A common assumption for box plots is that it reflects independent, identically distributed samples. In this case, as each point reflects a different dataset, and the datasets are the same across models, this assumption does not seem to hold here.

Author response:
[Q1]  Box plots:   
We simply aim to visually show each model's performance across a wide range of datasets. If you have suggestions for alternative visualization, we would be happy to modify it! We also provide the full numbers in Table 7 in the appendix.

Reviewer comment (Evaluation):
What are the main actionable conclusions from your work that any researcher working on multi-document retrieval-augmented long-form question answering systems should know? For instance, what does Figure 3.(a) imply in terms of optimal ordering of documents presented to the LFQA system?

Author response:
[Q2.1] *what does Figure 3.(a) imply in terms of optimal ordering of documents presented to the LFQA system?*  
Figure (3) implies that the ordering of information of the answer would be inline with that of the documents. The optimal ordering would depend on the type of question and how you would like to organize the answer. One possible ordering is to follow the discourse structure proposed in Xu et al. (2022) [6]. We could potentially identify which role the information in each document could provide and arrange them in the desired order. Exploring the optimal ordering of information would be out of scope for this paper, and we leave that to future research.

Reviewer comment (Evaluation):
Does retrieving and using longer documents imply an improvement of end-to-end quality?

Author response:
[Q2.2] *does retrieving and using longer documents imply an improvement of end-to-end quality?*  
We reiterate the point that giving an “overall quality” score is difficult and even experts disagree. We mainly focus on “attribution” in this work and leave the evaluation of other facets to future work.

Reviewer comment (Experiments):
How do various models handle different degrees of noise (irrelevant documents) in their context?

Author response:
[Q2.3] *how do various models handle different degrees of noise (irrelevant documents) in their context?*   
We only compare GPT-3 and Alpaca when prepending irrelevant documents, as we do not have access to the WebGPT model. GPT-3 exhibits a more severe difference in the surface statistics, which we discuss in Section 4: “Prepending unrelated documents has little effect on the automatic metrics for Alpaca, but impacts the generation of GPT-3, especially in length and Self-BLEU. This might be related to instruction tuning that enables LMs (Alpaca in this case) to be more robust to irrelevant prompts”. Alpaca shows slightly more changes in the percentage of supported answers (Figure 4(b)), but changes are little for both models.

Reviewer comment (Evaluation):
While qualitative insights are valuable, an over-reliance on them without sufficient quantitative backing might be a weakness.

Author response:
Re:[W1]  qualitative vs. quantitative analysis:   
We argue most of our analyses are quantitative, providing metrics to support our conclusions. For instance, our surface pattern statistics analysis (Section 4) presents statistically significant patterns across multiple experimental settings. Similarly, our study on the percentage of supported sentences (Section 5) and their pattern is quantitative. From our understanding, qualitative analysis typically refers to analysis that is based on anecdotal evidence from a handful of examples, while our study includes quantitative analysis including more than 5 metrics on a wide range of examples (hundreds of examples across 10 settings).  If there are specific types of quantitative analysis that the reviewer thinks will be helpful for the analysis , we’d be happy to include them if feasible.

Reviewer comment (Experiments):
The off-the-shelf models that the authors compared are not comprehensive. I feel it's important to include GPT-4.

Author response:
Re: [W2] not including GPT-4  
We focus our analysis on three models (GPT-3, WebGPT, Alpaca), carefully chosen models that exhibit strong LFQA performances. We also include the surface statistics and example outputs of other models (GPT-J 6B, Flan-T5-XXL, Llama-(7B, 13B, 30B), Alpaca-7B, davinci-(001, 002)) in Appendix B4. At the time we conducted the experiments, GPT-4 was not released yet. Collecting document-level human annotations is non trivial and expensive (we spent a total of 5,886.60 USD for data collection), thus we did not add GPT4 results last minute. We agree with the reviewers that evaluating GPT-4 will be valuable to our analysis. Please refer to the response to all the reviewers for the results on surface statistics and answer attribution predicted by the T5 model.

Reviewer comment (Evaluation):
We observed different behaviors across models like WebGPT, GPT-3, and Alpaca when provided with the same set of documents. What do you hypothesize as the underlying reasons for these differences?

Author response:
Re: [Q1] Potential reasons for different behaviors between model  
The main difference between WebGPT and the other models is that WebGPT is further fine-tuned with a retrieved document prepended. We find this makes the WebGPT model more faithful to the prepended documents (Table 2).