Understanding Retrieval Augmentation for Long-Form Question Answering
Authors: Hung-Ting Chen
Fangyuan Xu
Shane Arora
Eunsol Choi
Venue: COLM (2024)
Keywords: retrieval augmentation, retrieval-augmented generation, long-form question answering, question answering, attribution
TL;DR: We investigate how LMs use in-context documents to answer long-form questions.

Abstract: How retrieved documents are used in language models (LMs) for long-form generation task is understudied. We present two controlled studies on retrieval-augmented LM for long-form question answering (LFQA): one fixing the LM and varying evidence documents and the other fixing evidence documents and varying the LMs.
We study various attributes of generated answers (e.g., fluency, length, variance), with an emphasis on the attribution of generated answers to in-context evidence documents. We collect a dataset (SALAD) containing human annotations of sentence-level answer attribution in LFQA and evaluate existing methods for automatically judging attribution.
We find that while LMs can leverage relevant in-context documents, the generated answer is only partially attributable towards the documents, especially for LMs trained without retrieval augmentation.
Together, our analysis reveals how retrieval augmentation impacts long knowledge-rich text generation and provide directions for future work.

Published as a conference paper at COLM 2024
Understanding Retrieval Augmentation for Long-Form Ques- tion Answering
Hung-Ting Chen, Fangyuan Xu", Shane Arora", Eunsol Choi
Department of Computer Science
University of Texas at Austin (hungtingchen, fangyuan, whane.arora, arora, sunooaeeussslltttsssss..ooo
Abstract
How retrieved documents are used in language models (LMs) for long- form generation task is understudied. We present two controlled studies on retrieval-uugmented LM for long-form question answering (LFQA): one fix- ing the LM and varying evidence documents and the other fixing evidence documents and varying the LMs. We study various attributes of generated answers (e.g., fluency, length, variance), with emphasis the attribu- tion of generated answers to in-context evidence documents. We collect dataset (SALAD) containing human annotations of sentence-level answer attribution in LFQA and evaluate existing methods for automatically judg- ing attribution. We find that while LMs can leverage relevant in-context documents, the generated answer is only partially attributable towards the documents, especially for LMs trained without retrieval augmentation. Together, our analysis reveals how retrieval augmentation impacts long knowledge-rich text generation and provide directions for future work.
Introduction
Recent works (Nkkano et al. 2021 Malaviya et al., 2023 Gao et al., 2023b) proposed retrieval augmentation as powerful tool to provide up-to-date, relevant information to LMs for long-form answer generation. Yet, retrieval augmentation does not always affect LMs the way we anticipate. Liu et al. (2023a) discovered that information placed in the middle of contexts not used by LMs. Parametric knowledge continues to affect generation even when only relevant documents are provided in-context for factoid QA task (Longpre et al., 2021: Chen et al. 2022). These findings, however, are based on analyses on factoid QA with short answer spans, which is easier to evaluate. Our understanding of how retrieval augmentation impacts long-form generation in LMs is limited.
We study how retrieval impacts answer generation for LFQA, complex long-text generation task. We study two settings (illustrated in Figure Th: (1) fixing the LM and varying the degree of relevance of evidence documents and (2) fixing evidence documents and varying the L.s. As evaluating the quality of long-form answers is notoriously difficult (Krishna) et al. 2021), we start our analysis by measuring surface features (e.g. length, perplexity) that correlate with specific answer qualities such as coherence (Xu et al. 2023).
Our analysis reveals that retrieval augmentation changes LM's generation substantially, Some effects, e.g., change in the length of answers, are pronounced even when provided documents are irrelevant. Relevant in-context evidence documents lead to more substantial changes, leading LMs to generate more unexpected sentences (measured by higher per- plexity), while irrelevant documents do not have the same effects. Surprisingly, the impact of retrieval augmentation, even with the same set of evidence documents, can result in opposite effects for different base LMs (e.g. change in answer lengths and lexical diversity).
One desirable property of retrieval-augmented LFQA system is to have the answer atfributed to the evidence documents. To evaluate this, we collect SALAD, a dataset with Sentence-
* Eqqul contribution

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Question: pain relief, how know whether to choose between aspirin, Tylonol, Advil,
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A X
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A X
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Surface features (Section
Diversity 141 0.58
0.70
0.74
0.70
0.82
0.87
0.57
Ball BLEU)
Perplexity 13.8
10.0
13.1
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7.55
6.71
5.29
Figure We study (A) how differing LMs use the same in-context evidence documents to generate answer and (B) how in-context documents various degree of relevance affect the answer generation. We analyze generated answers on surface patterns (self-bleu, perplexity, etc) and their attribution to evidence documents. Attribution judgements are made per sentence, either by annotators (Section automatically from NLI model (Section 77. O's, A's and X's denote supported, partially supported and unsupported sentences respectively. Colored texts are generated texts not supported by in-context evidence documents.
level Attribution of Long-form Answers to evidence Documents, 12k human attribution judgements over 4k answer sentences. We provide an in-depth analysis of attribution with SALAD, which can serve as a benchmark for evaluating automatic attribution. We observe NLI models that performed well in detecting attribution in factoid QA (Bohnet et al. 2022 perform competitively in LFQA setting well. They significantly outpertorm chance baseline yet fall behind human agreement by 15% in accuracy. Our study reveals that attribution quality varies greatly across base LMs, even when they are provided with the same set of documents.
Our analysis reveals new insights on attribution patterns for long-form generation. The last generated sentence is substantially less attributable than earlier ones, and the generated text tends to follow the order of the in-context evidence documents, even when the in- context document contains multiple concatenated documents. Taken together, our study improves the understanding of how LMs use in-context evidence documents for LFQÅ and suggests concrete directions for future work. Our data and code is available at https //github. com/ wtt.:cotoottiieec 1tcchee2111/FFFg--eerrri
2 Background and Related Work
LFQA LFQA (Fan et al. 2019 Stelmakh et al. 2022) requires models to generate paragraph- length answers to complex, open-ended questions. To address this, WebGPT (Nakano et al., introduces web agent that searches the web and integrates relevant information to We evaluate this model closely in this study.
2021
LMs.
Retrieval Augmentation Retrieval-augmenned generation has received attention as a way to provide up-to-date, relevant documents to LMs at inference time (Ram et al. 2023) show- ing performance gains across multiple tasks (Shi et al. 2023). A line of work investigates how LMs incorporate in-context documents (Mallen et al.) 2023 Liu et al.) 2023a) with their parametric knowledge on simpler tasks such as factoid QA. Wang et al. (2023) studies the impact of retrieval in open-ended text generation with KNN-LM (Khandelwal et al. 2019).
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We focus on LFQA, which requires factual, attributable output over long sequences. Prior work (Krishna et al. 2021) also analyzed attribution in LFQA but studied smaller LLMs fine-tuned with in-domain data, whose attribution pattern varies significantly from the models we study.
Evaluating Attribution We focus our analysis on the attribution of long-form answers with respect to the prepended evidence document set. We follow the AIS framework Rashkin et al. (202)), an evaluation framework for whether system-generated text can be derived by given knowledge source. Bohnet et al. (2022) and Yue et al. (2023) study automatically evaluating attribution; the former uses off the-sheellff entailment models, while the latter prompts and fine-tune LLMs. Gao et al. (2023b) builds QA models that generate text along with citations and evaluates the citation quality of the generations automatically. [Bohnet et al. (2022) presents a controlled study of attribution (e.g., varying evidence documents and how they impact attribution) on factoid QA with Wikipedia as retrieval corpus.
Recent work [Liu al., 2023b) annotates attribution quality in long-form answers generated from commercial generative search engines. While they provide a comprehensive study on attribution quality with manual annotations, their study on black box models is limited, as they do not have knowledge of how the cited documents were integrated into the LMs. For instance, documents could have been retrieved post hoc (Gao et al., 2023a) or prepended in- context. We instead present a controlled study involving open-source models, and analyze their data in Section
3 Study Setting
We conduct controlled study on how retrieval augmentation impacts long-form answer generation for LMs, observing surface features and attribution while varying evidence document sets and LMs. In this section, we describe our experimental setting.
Dataset We source questions from EL15 dataset (Fan et al. 2019), which contains questions from the Reddit forum "Explain Like I'm Five". We use the entire test set released by WebGPT (Nakano et al., 2021 (271 questions) for automatic evaluation (Sec. and Sec. 7.2
and randomly sample 100 questions to collect manual attribution annotations (Sec.
Knowledge Source: Evidence Documents For each question, we compile four sets of evidence documents to examine how models use documents of varying degrees of relevance. Each document set contains 3-4 document snippets, each containing roughly 100 words. The statistics on each set can be found in Appendix A.1 We detail each document set below:
Human Demonstration Annotators from prior study (Nakano et al. 2021 used a com-
mercial search engine (Bing) to gather documents to answer questions. We include these as "gold" documents that are considered relevant for answering questions by humans. WebGPT Retriever We consider documents retrieved by the WebGPT (175B) (Nakano) et al., 2021) model. Their study found using these documents results in high-quality answer generation.
Bing Search We retrieve relevant documents using Bing Search API with the question as the query, and obtain the top 10 pages returned by the APT, and retrieve four 100-word segments from aggregate search results. Post-processing details are in Appendix A.2 Random To simulate a set of irrelevant documents, we randomly sample another question in the test and take the corresponding documents retrieved by WebGPT.
We evaluate the relevance of the first three sets of documents manually by sampling 20 questions and examining the document sets. We find that WebGPT, human demonstration and Bing documents contain sufficient information to answer the question for 85%, 50% and 45% of the examples, respectively. Details on the manual study are in Appendix B.6
Base LMs & Answer Generation We consider three LMs: WebGPT((155) NNakano et al. 2021), GPT-3.5 (text-davinci-003) (Brown et al.) 2020) and Alpaca (Taori et al. 2023). The
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Model (+ evidence)
# Sentences
# Words
RankGen (1)
Self-BLEU (1)
Perplexity (L)
WebGPT WebGPT docs)
6.7
160_/33
11.35 11.95 -1,98
0.58
0.56-/0.07
13.81 /4.8
GPT-3.5
9.3
/19
12.77
0.710.04/0.06
6.130.02/1.77
+Human docs
6.60.9/18
17218/40
11.890.60/1.6
0.620.04/0.07
10.940.05/3.94
+WebGIT docs
6.80.9/188
18520/41
11.970.60/.77
0.620.04/0.07
11.630.13/4.1
BBing docs
1.0/9
17919/38
12.13u.68/199
0.640.04/0.00
9.03g.12/3.4
Random docs
7.61,1/2.1
18319/39
12.40p.67/2.13
0.68p.04/0.07
6.76g.05/1.66
Alpaca-7b
5.018/881
11333/73
12.17
0.51
11.950.02/7.18
Human docs
1.7366
13844/79
11.820.88/2.32
0.55p.09/0.14
12.990.20/5.73
WebGPT docs
6.2233/7.
14545/80
11.910.75/2.07
0.55g.08/0.14
13.270.13/5.68
+ Bing docs
7.62.8/5.0
187 66/107
12.04g.78/2.05
0.59
10.810.13/5.34
RRndomm docs
5.21.655.3
32/65
12.250.71/1.9
0,53
11.920.23/5.35
Human Human docs)
5.1-/27
119-/39 -39
9.29 /4.37
0.49 -4.17
17.63-/725
Table 1: Generated answer statistics. We present means and two standard deviations in its subscript: one computed over three answers generated for the same example, one over answers for different examples. Numbers in red and blue indicate decrease and increase from the base model respectively. We boldface rows where we collect human annotations for attribution (Section
WebGPT model is trained to interact with a commercial search engine (Bing) and compose long-form answers based on the information gathered from Bing for questions from the EL15
dataset We experimented with range of open-sourced LMs (GPT-1 (Wang & Komatsuzaki 2021) (6 Flan-T5 [Chung et al. 2022) (11B), Llama (Touvron et al. 2023) (7B, 13B, 30B) and Alpaca 7B (Taori et al.) al.] (023)) and found Alpaca to be the best perttrreg upon manual examination The prediction examples for all other LMs can be found in Appendix B.5 We prepend the concatenated evidence document set to the question and provide it as a prompt to LMs with brief instruction. We sample three answers for each setting to study answer variability. The decoding hyperparameters and prompts can be found in Appendix A.3
4 How In-Context Documents Impact Surface Answer Statistics
Metrics Unlike evaluating short, mostly entity-based answers (Rajpurkar et al. 2016 Fisch
et al.] 2019), evaluating the overall quality of long-form answers (Krishna al. 2022 Xu
et al. 2023) is notoriously difficult for both humans and models. In this section, we look at metrics that have been shown to correlate with specific aspects (e.g., fluency, coherence) XXu et al. 2023) of answers, to quantify differences between answers.
We report the number of sentences and number of words in the answer paragraph. The length is shown as sigmificant confounding factor in human evaluation for various
Length:
tasks, with humans often preferring longer answers (Sun et al. 2019: Liu et al. 2022 Xu
et al.
20. 2023
Self-BLEU Zhu et al. 2018 measures the lexical diversity of generated text. An answer
repetition it has higher Self-BLEU score. Prior work Xu et al. (2023) also found that lower Self-BLEU correlates to better coherence.
is less diverse and contains
RankGen (Krishna et al. 2022) a T5-XXL encoder trained with large-scale contrastive learning, ranking generation given prefix. A higher RankGen score signifies more likely continuation of the prefix. We measure RankGen score with question as the prefix.
Perplexity: We report the perplexity of the answer measured with GPT-2-XL (Radford et al. 2019). Lower perplexity generally indicates more fluent generated text, though human-written texts (Holtzman et al. 2019) do not necessarily exhibit lower perplexity compared to model-generated text.
!hile their model not released, the model outputs were provided /ttttps://opeeaipublliccc
lob.core. windows. net. wwttt//wwpp-eeo Seabbptt-nnee answer-nwwweiii wwweerrvvvweeeeeeeeeeeee wiwwer/indexx.e...eee win//xidxxiiiee html
2This is likely because FI.15 one of the seed task used to generate training data for Alpaca.
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Results Table presents the statistics for answers. Overall, prepending relevant docu- ments yields bigger changes for both GPT-3.5 and Alpaca compared to prepending random documents. Prepending unrelated documents has little effect on the automatic metrics for Alpaca, but impacts the generation of GPT-3.5, especially in length and Self-BLEU. This might be related to instruction tuning enabling LMs (Alpaca in this case) to be more robust to irrelevant prompts (Webson & Pavlick 2022). ) We report the results on GPT-4 (which shows the same trend GPT-3.5) Appendix B.4. and seven other LMs in Appendix B.5
Using the same evidence documents brings different effects on different LMs. On GPT- 3.5, providing documents results in shorter outputs and less repetitions, while on Alpaca, results longer outputs and more repetitions. Yet, on both models, adding relevant documents causes bigger changes in length than adding random documents. Overall, GIT- 3.5 generate longer answers with less variability across examples. Alpaca answers exhibit higher variance across examples across all metrics.
For both models, RankGen scores decrease when the document set is more relevant. This can be as the model incorporates new information from retrieved documents, generation become less predictable from the question alone. Perplexity also shows similar trends, with relevant documents increasing perplexity. This might be because it copies rare tokens from documents, which will get assigned high perplexity when evaluating answers alone.
Models can differentiate random and relevant documents. In our experiments, answers generated with random documents are the most similar to answers generated without documents, see Appendix [.1 for detailed analysis.
Our finding diverges from Krishna et al. (2021), which showed that conditioning on random and relevant documents does not bring differences in smaller-scale, fine-tuned retrieval- augmented LMs. This suggests LMs fail to incorporate information from retrieved doc- uments into their answers. There can be multiple reasons for different conclusion from their study and ours. First, the retrieved documents during their fine-tuning process rarely contain relevant information, resulting in the LMs relying mostly on their parametric knowl- edge. Another reason is the significant train-test overlap in the ELI-5 dataset, leading LMs to memorize answers without using retrieved documents. In our setting, we evaluate LLMs without dataset-specific fine-tuning process.
Attribution Dataset (SALAD) Construction
While automatic metrics show that in-context documents influence generation substantially, we lack a deeper understanding on how the answers change. In this section, we focus on attribution (Rashkin et al. 2021), which measures how much of the generated answer can be entailed from the evidence documents. As automatically measuring attribution is nontrivial, we first collect human annotations. We compare our collected dataset SALAD with recent attribution datasets in Appendix C44 Unlike prior work which conducted annotations on full-fledged systems without altering evidence documents to the LM, our annotation presents multiple evidence document sets for the same base L.M.
Setup Given a question x, generated answer y, which consists of sentences ".. Yn and a set of reference documents D, we aim to label each answer sentence yi with one of the following: Supported, Paartially Supported, Not Supported by D. If the sentence is Supported or Partially Supported, the annotator also provides a minimal subset of sentences from that support the answer sentence. Lastly, the annotator highlights the unsupported span if the sentence is Partially Supported.
Data Collection We construct SALAD by collecting annotations for 100 questions randomly sampled from the ELI-5 test set on six model document set configurations, namely WebGPT +(WebGPT docs); GPT-3.5 +[No docs, WebGPT docs, Human docs) and Alpaca +NNo docs, WebGPT docs). For answers generated with in-context documents, we use the document set as the reference document D, and use WebGPT documents as D for answers generated
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without documents, We collect three annotations per example as the task is somewhat subjective and take the majority label, discarding 3.4% of examples without a majority vote. The inter-annotator agreement is reasonably high (Krippendorff' alpha = 0.71)
Insights from Attribution Annotation
Equipped with manual annota- tion, analyze how much of long-form answers can be at- tributed to evidence documents. Table summarizes the annota- tion results.
Supportedness Part.
Setting
Ex.
Yes
No
WebGPT + WebGPT docs
649
95%
2%
3%
GPT-3.5 + WebGPT docs
659
85%
4%
11%
Alpaca WebGPT docs
545
61%
7%
32%
GPT--3.5 Human docs
661
73%
7%
20%
6.1 Comparing Attribution of Various LMs
GPT--3.5 without does
896
22%
8%
70%
Alpaca without docs
447
23%
6%
71 %
Total
3,857
59%
6%
35%
We first compare attribution per- formance of different models using the same evidence docu- ment set (the top section in Ta- ble 2) We observe that genera- tions from the WebGPT model are most faithful to the evidence documents. Even with the same set of evidence documents, answers generated by Alpaca has ten times more unsupported sentences than that of WebGPT.
Table 2: Attribution Annotation Results: The percent- age of each attribution label of answer sentences with respect to their corresponding evidence document sets. For answers generated without documents, the answers are evaluated with WebGPT documents.
LMs fine-tuned with retrieval augmentation achieve greater faithfulness. Unlike the other two models, WebGPT was fine-tuned for LFQA with evidence document prepended. This suggests that fine-tuning LMs under retrieval-augmmentd setting might be helpful for generating more faithful long-form answers. This echos findings from prior work in factoid QA (Bohnet et al. 2022) that retrieve-then-rea systems trained with a retrieval component achieve more taithful generation.
6.2 Comparing Attribution when Varying Documents
Unsurprisingly, answers generated without documents (last two rows) are largely irrelevant to reference document set (WebGPT docs). This does not necessarily mean the generated answers are not factual, as valid answers to the same question can be different (Krishna et al., 2021 Xu al. 2023) and thus could be attributed to different sets of documents. Nonetheless, over 20% of sentences were supported by reference documents, suggesting LLMs exhibit some parametric knowledge that matches information in the reference documents.
Comparing the same base model (GPT-3.5) provided with different evidence document sets (WebGPT docs vs. Human docs), we find that the model use WebGPT does more efficiently. This might be due to WebGPT documents being longer (about 10%) than hu- man demonstration documents, providing more comprehensive information to copy from. Nonetheless, even with WebGPT docs, 15% of the answer sentences are not supported, suggesting that GPT-3.5 generates information that are beyond what can be inferred from evidence documents.
6.3 Attribution Patterns
We analyze attribution pattern as the model autoregressively generate long-text.
Mooe details about crowdsourcing, including recruitment and disagreement patterns, can be found in Appendix

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0.0
0.0
Document Sentence Index Fraction
11.2
11.0
0.1
0.1
12.6
12.4
9.8
10.5
0.4
0.4
10.2
11.1
0.5
0.5
9.4
0.6
0.6
8.7
9.1
8.3
0.9
0.9
11.3
9.3
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Fraction
1.0
Avg
0.0 0.1 0.2 0.3 0.4 D.5 0.6 0.7 0.8 0.9 1.0 Fraction
Avg
(a) Location of supporting sentences on genera- (b) Location of supporting sentences on genera- tion settings without in-context documents.
tion settings with in-context documents.
0.22
3.00
8.11
2.07
0.00
5:34
0.08
2.08
0.12
6.70
0.00
3.20
5:04
3.07
0:02
5:21
6.11
0.07
8.34
5:74
(c) Location of unsupported sentences SALAD.
(d) On data from Liu et 2023b)
Figure 2: On the top, we show the distribution of location of supporting sentences in the document D for Nth answer sentence chunk. We normalize by the column visualize the distribution of supporting sentences in evidence documents for each answer sentence chunk. The "Avg" column shows the average across answer sentences, indicating how frequently each document chunk are supporting the answer. We report aggregate results on generation with documents in (a) and without documents (the bottom two generation settings in Table 2) in (b) as control study. On the bottom, show the percentage of unsupported sentences by the relative location in the answer.
Does the order of information presented in the evidence documents impact the order of information presented in the generated answer? If LM synthesizing information based on the content alone, there should be little correlation with the order of the documents, as they are simply concatenated. We plot the correspondence between the answer sen- tence location and their supporting sentences in the evidence document set in Fig. 2 (b),, by aggregating the supporting sentences sets annotated for each answer sentence. We
report supporting sentences locations on answers generated with documents (Fig 2 2()) and without documents (Fig. (b)).. On netrieval-augmentee generation (a), we identity linear correspondence pattern, with information mentioned earlier in the document tend to
appear earlier in the generated answer This suggests the order of evidence documents is reflected in the order of generated contents. Recent study (Liu et al.) 2023a) also showed order sensitivity of in-context augmentation for factoid QA, finding that models ignore information in the middle. We also find that the later half of the evidence documents, except for the last 10%, are less cited by the generated answer (see Avg. column Fig. 2
Which parts of the answer are less supported by the evidence documents? Generated answers consist of 5-10 sentences. Are sentences generated earlier more likely to be at- tributable? Fig. (d) report the percentage of unsupported sentences by the relative position of the answer sentence on our data and attribution annotation on long-form swers from commercial generative search engines from Liu et al. (2023b) respectively. We find that the last sentence is almost twice likely to be unsupported compared other sentence in the answer. This phenomenon is even more pronounced on dataset from Liu et al. (2023b). Recent study (Min et al. 2023 also showed the same trend for attributing model-generated biography.
We further conduct study where the in-context documents shuffled in Appendix B.7 and find that the linear correspondence is less pronounced but still observable.
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Retrieval Failure (54%).
Why does it seem like when I watch something the second time around, it goes by faster than the first time I watched it?
Question:
retrieved document sut contain answer to the question.
Documents: Basically, the busier you are during time interval, the faster that time interval will feel like passed.... (more about time gues by faster when you nut bored...)
Answer Sentence: However, when we watch sumething for secund time, our brains have had chance to process the information and are able to make more efficient use of the information.
Explanation The decuments explain why time goes by faster when you are having fun, but the question is asking watching something the second time.
Hallucinated Facts (72%):
Question: How does money go from my pocket, thruugh the stock market, and to support the business I've bought stock from?
contents not
Documents: Stocks, or shares of company, represent ownership equity in firm, which give shareholders voting rights as well as residual claim on corporate earnings in the form of capital gains and dividends. ... (more about how stock market works) Answer Sentence: You can purchase shares of the stock from broker or through an online trading platform.
mentioned in the docu- ments.
Explanation: The ducuments never mention where you can buy stock from.
Incorrect Syn- thesis (14%) synthesizes
How you determine whether earthquake was naturally occurring or it was human induced?
Question: Seismologists:
Documents: Studies of the numerous nuclear tests that took place during the Cold War show that explosions generate larger I' waves than S waves when compared with earthquakes. Explosions also generate proportionally smaller Surface waves than P waves.
the content from separate
documents incorrectly.
Answer Sentence Natural earthquakes generate larger waves and smaller Surface waves compared to nuclear tests.
Explanation; Explosion generate larger P waves, not natural earthquakes. The answer sentence is thus incorrect. Most of it is copied from the documents.
Table 3: List of attribution error types (and their frequency of occurrence in unsupported sentences) and examples.
6.4 Manual Error Analysis: Unsupported Sentences
What causes the model to produce unsupported sentences? We manually examine 30 answer sentences labeled as Not Supported for each setting that has access to evidence documents 5 We identify three categories of unsupported sentences: retrieval failure, 3
hallucinated facts, and incorrect synthesis 5 Table provides description for each category along with an example. In Table in the appendix, we further provide a breakdown of error types for each generation setting. During our analysis, we found that about 14% of errors corresponds to annotation error.
Attribution failures occur more frequently when the retrieved documents do not provide sufficient evidences for answering the question. Generating ungrounded concepts is more frequent than incorrectly synthesizing information from incompatible documents. However, incorrect synthesis happens relatively more frequently in the WebGPT model, potentially as it attempts to ground its generation more heavily from the documents. This suggests multi-docummnt summarization and synthesis is an important direction for future work, especially for more faithful retrieval-augmented LMs.
7 Automatically Identifying Unsupported Sentences
Annotating attribution requires careful reading over multiple documents and comparison between two texts. Recent work (Bohnet et al.) 2022 Gao et al.) 2023a) showed that fine- tuned models from NLI datasets can successfully automate this process. We investigate automatic identification of unsupported answer sentences in LFQA domain with SALAD.
We analyze all unsupported answer sentences generated by WebGPT, as there are only 17 in total. CCategories are not mutually exclusive (one can contain irrelevant documents and combine facets from each).
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7.1 Evaluating Automatic Attribution
Setting Given a question 9, reference documents D and answer sentence Vi, the system should predict if each answer sentence yj supported by D. We merge Partially Supported and Not Supported into a single class and consider it as target label. We report micro average F1 score, which is computed over the set of predictions and labels of all the answer sentences for each generation setting in Section separately, as model performances vary greatly per dataset. We report accuracy in Appendix B.3 which shows similar trends.
Comparison Systems We evaluate methods for automatically evaluating attribution. We first establish lower and upper bounds, and introduce existing methods. We do not finetune any models for our task, but chose one hyperparameter, a threshold for deciding support- edness or not based on the micro average Fl score on the dataset itself, as it would be unrealistic to spare a development set due to the small size of each subset in SALAD.
Baselines We report a random baseline, which randomly assigns labels for each answer sentence according to the label distribution in each dataset, and majority baseline, which assigns the majority label for all instances.
Human Performance We report the human performance by taking one set of annotations as the prediction set and another set of annotations as the label set. We compute the F1 score, and take an average across three possible pairs.
NLI models Following prior works (Schuster et al.) 2022. Laban et al. 2023b), we evaluate four NLI model variants: two RoBER La-
2022; Gao et al. Nie et al. (2020) Honovich et al.
LarRla-large large (from
and Yin et al. (2021)), ALBERT-xlarge [Schuster et al. (2021), and T5-11B
(2022) trained on a combination of NLI datasets. While most NLI models compare a pair of sentences, our setting compares set of documents (as hypothesis) and sentence (as premise). For the models except the RoBERTa-large trained on DocNLI (Yin et al. (022),, we follow [Schuster follow/Schuster al. 2022), which makes entailment decisions for each document sentence and answer sentence pair, and aggregates the results by taking the maximum value over all the pairs. The details of the NLI models can be found in Appendix A.44 QAFactEval (Fabbri et al. 2022): is a QA-based factual consistency metric for sum- marization. It evaluates how consistent the summaries are with respect to the given documents. We treat each answer sentence as the summary, measuring whether the questions generated from the sentence are answerable by the given documents.
Results We report model performances in Figure 3a with each box representing the performance of an approach amd each dot in the box representing the score each answer generation setting. The exact scores are in Table the appendix. We find all methods outperform simple baselines (majority, random) by large margin, but none comes close to human agreements. As in factoid QA setting (Bohnet et al. 2022), the T5 model achieves competitive performances, achieving average FI over 60 and accuracy over 80%. While developed for a different domain (summarization) QAFactEval performs relatively well.
7.2 Applying Automatic Attribution
Having discovered that the T5 model achieves competitive performances in predicting attribution, we use this model as an approximation for human judgment on attribution in generation settings evaluated in Table complementing human annotation results in Section 6 We quantify how frequently the answer sentences are supported by different sets of documents using the T5 model.
In Figure we present the attribution predicted by the T5 model (along with the gold human attribution score if exists). We find answers generated with random documents as evidence (last row in each block) exhibit similar attribution patterns with answers generated without documents (first row in each block). This suggests that models successfully ignore irrelevant documents, and retain similar level of attribution relevant documents, espe- cially for GPT-3.5. Providing a noisy, yet relevant document set (+Bing docs) still does not meaningfully change attribution pattern with respect to the other documents (Human docs, WebGPT docs, Random docs), yet increases supportedness towards provided evidence doc-

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Published as a conference paper at COLM 2024
Supported Sentences w.r.t
Model (+ evidence)
Human
WebGPT
Bing
Rand.
Munah
WebGPT (+ WebGPT docs)
37.14
/
3.45
GPT-3.5
27.59
34.04
24.79
4.49
+Human docs
65.13
37.99
20.19
3.67
ALBERFAT (AVO
+WeGGPT docs
31.37
73.53 /
20.24
3,90
Bing docs
24.12
30.79
48.53
4.09
foseRb L (.S.A.P)
Random docs
26.13
33.52
23.06
5.19
Majorty
Alpaca-7b
26.67
32.10
25,56
2.76
Human docs
47.04
34.35
21.25
3.40
WebGPT docs -Bing does
33.34
59.79 /
24.26
7.44
(a) Automatic detection perfor- mance of unsupported sentences. Each box plot represents the per- formances of single method, and each dot is the F1 score on one of the answer generation set- ting specifically.
25.05
31.31
38.53
6.63
Random docs
22.72
27.82
20.32
3.76
Human (+ Human docs)
76.34
37.2
19.21
3.58
(b) Percentage of supported answer sentences according to T5 model (and human annotation). Each row represents an answer set, and columns represent the reference documents which we compute attribution score with respect to.
Figure Automatic attribution detection performance (left) and their application (right).
ument (Bing). Adding WebGIT documents brought the highest change in both models, increasing attribution rate towards both the WebGPT and Human documents. Adding human demonstration documents also shows similar trends but less impact, potentially as it contains less information than WebGPT documents.
Conclusion
We present study on retrieval augmentation for LFQA. Our analysis suggests concrete directions for future work. First, LMs trained without retrieval and attribution mind does not always generate sentences that can be attributed to in-context evidence documents, even when provided relevant documents only. This motivates training LMs after introducing in-context evidence documents. Analyzing patterns of unsupported sentences, we find that injecting multi-document synthesis ability to LLM can be an important direction for future work. Second, we find evidence document should be carefully added to LMs. The order of information in evidence documents impacts the order of information in the generated answer. And even prepending irrelevant documents meaningfully change the surface statistics of generated answers, though attribution percentage to relevant documents remains somewhat stable. We find attribution error is more common when prepending documents without sufficient information, motivating the development of better retrievers. Third, off-the-shi NLI models show promising performance at identifying generated sentences unsupported by evidence document, but fall behind human agreements. Our new dataset SALAD, together with other related datasets (Liu et al. 2023b), can serve as a useful resource for improving automatic attribution methods.
Ethics Statement
We have collected and released a new dataset. The collection process is documented in Section 5 Appendix
The dataset we we study (EL15) is publicly available, and the evidence documents we use are either made taken from prior work Nakano et al. (2021) or newly obtained by collecting results from Bing Search API. We also release new LMM-enerated answers. The dataset we newly release, both the web documents and model-generated answers, could contain biased
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or factually incorrect content. However, we find most questions investigate in this work are neither controversial nor seek harmful contents.
Acknowledgments
This work is partially supported by a grant from Open Philanthropy and a NSF grant (TIS-2312948). We thank the NLP group at UT Austin, particularly Leo Zeyu Liu.
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A Experimental Details
A.1 Document Set Statistics
We report the lengths of each document type in terms numbers documents, sentences and words, in T
Retrieval
Avg. # Docs
Avg. # sents
Avg. # words
Human
3.5
13.7
308.9
WebGPT
3.5
16.8
388.0
Bing
4.0
22.8
400.0
Random
3.5
16.8
388.0
Table Data statistics: lengths of evidence document set D.
A.2 Bing Search Output Post Processing
We use Bing Search API v7.0 We post-process the raw HTML of the retrieved pages with tools such as html2tex 8 and readability" We each page into 100-word segments, merge segments from all pages, and retrieve the top four segments with BM25 retriever (Robertson et al. 2009).
A.3 Answer Generation Details
The prompts we used for answer generation can be found in Table For Alpaca, we use sampling with temperature of 0.9, top p and maximum length of 1024. For GPT-3.5, we use sampling with a temperature of 0.7, top p and a maximum length of 512.
A.4 NLI model details
Out of the four models, one of the RoBERTa-large trained on DocNLI (Yin et al. 2021 which encodes all the documents at once and outputs a prediction.
The remaining three models are trained on a subset of MNLI (Williams et al. 2018
SNLI [Bowman et al. 2015), ANLI (Nie et al. 2020), FEVER (Thorne et al. 2018) Vitam- inC (Schuster et al. 2021 During inference, the aforementioned models predict entailment for each answers sentence by taking the maximum out of entailment scores with every document sentences as the premises, following (Schuster et al. 2022). More specifically, for each answer sentence yi and document sentence Si, we consider c(ij) p(entailed y,, to be the entailment score between pair y/ and Sj. Then we take e; = max;, c(i,)) to be the entailment score of Vi, and consider Supported if e; > threshold e. We perform a grid search on ε {0.01, 0.03, 0.05, 0.07, 0.1, 0.2, 0.3, 0.4, 0.5, 0.7) and choose the value that gives the highest F1 score on the test set, given the limited size of dataset. We settle on 0.1 RoBERTa-L. (M,S,A,F), e 0.5 for RoBERTa-L (D), e 0.2 for ALBERT-xl (M,V), and 0.03 for 15.
B
More Results
B.1 Similarities Among Answer Generated with Different In-Context Settings
Retrieval-augmented LM combines its parametric and non- paramertttticc knowledge from evidence documents to address the question (Longpre et al.) 2021 Mallen et al. 2023 Zhou
http:://gtthuub..o///o//lll/////
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Setting
Prompt
No documents
Generate long answer the following question.
Question: (Question)
Answer:
With documents
Documents: (Documents)
Generate long answer to the following question, using information from the documents.
Question: [Question)
Answer:
Table 5: The prompt we use for generating long-form answers. (Documents) and (Question] are substituted with the actual documents and question during generation. Documents are line-separated.
WebEPT
0.16
WebGPT 0.13
WebdPT 0.84
WebiGPT 0.03
Bing
Bing
0:12
Bing
Bing
0:14
0.16
Rand
0:12
0.13
0.02
0.02
0.02
0.83
No Docs
0:16
0.15
0.27
0.20
No Docs
0:12
0.12
0.14
0.14
No Decs
0.84
0.84
0.85
0.87
No Docs
0.83
0.83
0.84
0.86
HumanWWeboPr Bing
Human WetePT
Bing
hand
Human
Bing
Rand
Wescor
Bing
(a) GPT-3.5, overlap
bigram (b) Alpaca,
bigram (c) GPT-335,
SimCSE (d) Alpaca,
SimCSE
overlap
score
Score
Figure 4: Similarity between answers generated by the same LMs with different evidence document sets. The upper bounds for similarity, computed answers sampled multiple times in the same setting, are 0.19 for bigram overlap and 0.875 for SimCSE. The lower bounds are 0.03 for bigram overlap and 0.15 for SimCSE, as computed on answers belonging to different questions.
et al. )023). We aim to understand the impact of combining information from evidence doc- uments on generated answers, as opposed to relying solely on parametric knowledge. We thus compare lexical similarities (measured by bigram overlap) and embedding similarity (measured by SimCSE (Gao et al., 2021) between answers generated with various evidence document settings and answers generated without documents.
Figure compares answers generated from two LMs (GPT--3.5, Alpaca) under five evi- dence settings (including no documents and four evidence types describe in Section 3). To contextualize similarity scores, we provide an upper bound (0.19 for bigram, 0.875
SimCSE) by computing average similarity between three pairs of samples generated without documents, and lower bound (0.19 for bigram overlap and 0.15 for SimCSE) by computing the similarity between answers to different questions.
According to both metrics, the answers generated without evidence document are most similar to the answers generated with random documents, followed by Bing documents, suggesting more relevant evidence set change answers more substantially.
The answers generated with random documents prepended are the most similar to answers generated without documents. Answers generated with WebGPT documents are the most similar to ones generated with human documents and vice versa (and thus less similar to the others). This indicate high-quality documents might elicit slightly different behaviors out of LMs compared to when they relying only on parametric knowledge. Surprisingly,
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Relevant Document
Irrelevant Document Incor. Syn.
Annotation Error
Incor: Syn.
Hal.
Hal.
WebGPT+WebGP1 docs
4
GPT-335 +WbGGPT docs
0
3
13
5
GPT-3.5 +human docs
2
6
3
14
5
Alpaca +human docs
0
14
0
11
5
Table 6: Manual error analysis on 30 unsupported answer sentences per setting (17 for WebGPT). We categorize the examples without annotation errors based on document rele- vance. Then we decide if the answer sentence is an incorrect synthesis of information from the documents or hallucinated facts. "Incor. Syn." " denote incorrect synthesis, and "Hal." " denote hallucination.
Human
QAFactEval
ALBERT-XI (M,V)
RoBERTa L (D)
RoBERTa-L (M,S.AFF)
Majority
Random
50
60
70
80
90
Figure 5: Accuracy on automatic detection of unsupported sentences. Each box represents the performances of single method, and each dot is the accuracy of one of the dataset.
answers generated with Bing documents are the most similar to answers generated without documents.
B.2 Full Results Manual Analysis of Attribution Errors
We report the occurrence of each attribution error types for 30 randomly sampled unsup- ported answer sentences (17 for WebGPT) for the settings with access to evidence documents in 6
B.3 Full Results of Automatically Identifying Unsupported Parts
We present the accuracy of each evaluate approach in Figure We also present the exact numbers of F1 score and accuracy in Table 7 We show the datasets which the models are
trained on in acronyms: M MNLI (Williams et al. 2018). SNLI (Bowman et al. 2015), A
ANLI (Nie et al., 2020), F FEVER (Thoree et al. 2018 D DocNLI Yin et al.) 2021) and
VitaminC (Schuster et al. 2021).
B.4 Surface feature statistics of answers generated by GPT-4
We investigate behaviors of GPT-3.5 in the main experiments, and in Table we we additionally report results on the latest GPT-4 model (gpt-4-0613). Results on GPT-4 mostly align with that on GPT-3.5, except GPT-4 abstain from answering the questions frequently when random documents are prepended (and thus the short lengths on average). We thus only include GPT-3.5 on the remaining experiments.
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Published as a conference paper at COLM 2024
Model
WebGPT +WebGPT
GPT-3.5
GPT-3.5
Alpaca
GPT-3.5
Alpaca
Avg.
Evidence Doc
WebGPT
+Human
+WebGPT
Random
6.2/90.7
13.0/74.6
26.1/60.2
38.3/52.6
22.8/66.2
21.1/64.0
21.3/68.0
Majority
0.0/94,9
0.0/84.8
0.0/73,4
0.0/61.1
0.0/78.1
0.0/77.4
0.0/78.3
RoBERTa-L
30.5/86.0
37.4/64.5
54.9/66.4
68.6/76.3
52.7/72.5
51.1/70.9
49.2/72.8
(M,S,A,F)
RoBERTa-L (D)
31.3/91.2
50.0/83.6
53.8/76.9
62.6/74.3
41.0/63.4
43.1/67.6
47.0/76.2
ALBERT-xl (M.)
28.6/93.1
56,4/87.1
62.5/79.9
69.8/79.6
50.6/68.6
56.6/72.9
54.1/80.2
T5-11B (M,S,EV)
38.7/91.2
5188/13
65.0/78.8
79.1/84.6
60.1/77.5
66.1/82.3
60.1/82.6
QAFactEval
37.2/89.1
55.1/81.9
41.7/45.1
76.2/82.0
51.1/75.5
62.7/80.5
54.0/75.7
Human
49.9/93.1
61.3/90.5
66.3/88.2
72.2/87.6
89.2/86.4
88.0/85.9
71.2/88.6
Table 7: Performance of NLI models detecting attribution our data (Fl score / Accu- racy). Columns represent distinct subset of the annotated dataset, with different generation settings. For the reference documents for attribution, eration settings with evidence documents, and use the WebGPT documents in generation settings without evidence documents. Bold numbers are the best scores in every columns (excluding human performances).
the evidence documents in gen-
Model (+ evidence)
Sentences
Words
RankGen (1)
Self-BLEU ())
Perplexity (J)
GPT-4
21.2 /4.3
480 -66
13.36-/1.79
13.36
/1.79
0.78 -0.000005
6.57 6.57-/1.13
+Human docs
13.3 /3.5
317
12.18 /1.99
0.73-/0.05
9.92 12268
WebGGIT docs
13.5
325-
12.29
-1.88
0.73
10.08 /2.44
Bing does
11.9-/3.5
281
12.42 /1.94
0.70-/0.10
9.75 9.75-/3.93
+Random does
1.6-/1.7
37 37-
8.87
/1.61
0.11-/0.19
56.70 /42.35
GPT-3.5
9.31,5/26
21930/51
12.770.67/1.77
0.71
6.13p.02/1.37
HHuman docs
6.60,9/1.8
17218/40
11.89p.60/1.88
0.620.04/0.07
10.94p.05/3.94
WebGGPT docs
6.80.9/188
18520/41
11.97
0.620.04/0.07
11.620.13/4.16
+Bing docs
6.91.0/1.9
17919/38
12.130.68/1.91
0.640.04/0.07
9.030.12/3.24
+Random docs
7.61.1/21
18319/39
12.40p.67/211
0. 0.680.04/0.07
6.76p.05/
Table 8: Generated answer statistics for GPT models. We present mean values along with two standard deviations in its subscript: one computed over three answers generated for the same example, one over answers for different examples. Numbers in red and blue indicate decrease and increase from the base model respectively.
B.5 More analysis on answers generated by different models
We report automatic metrics for answers generated by series of GPT-3355 models (davinci-001, davinci-002) and other open-sourced models (GPT-J, FLAN-T5-XXL, Llama and Alpaca) in Table
We additionally include generation examples for all the above LMs in Table 10
B.6 Manual Analysis on Document Relevance
We randomly sample 20 questions from the ELI-5 (Fan et al. 2019) test set and annotate the documents are sufficient for answering the questions. We examine documents retrieved by WebGPT, human demonstration and Bing Search API (the first three settings in Section 3). The results are presented in Table 11 The WebGPT documents are sufficient for answering the question in the most number of examples (85%), while human documents and Bing documents are less relevant, with only about half of them being sufficient for answering the question. Human documents are often insufficient for answering the questions because human do not cite documents extensively, as shown in the example we provide Table 12 usually less relevant to the questions (as
Upon manual inspection, Bing documents shown in Table [2) compared to WebGPT and human documents, despite similar number of sufficient examples to human documents.
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Published as a conference paper at COLM 2024
Model
Ans.
Rank
Self BLEU(J)
PPL(.)
(+ evidence)
Gen(1)
GPT-
15.8/292
10.53
0.53
58.18
docs
14/294
10.08
0.57
88.33
Flan-T5-XXL
1.1/25
9.97
0.02
656.67
docs
1.7/37
9.61
0.09
75.23
Llama-7B
18.4/348
10.35
0.73
133.65
docs
17.3/322
10.66
0.73
9.20
Llama-13B
13.6/250
9.46
0.65
148.03
docs
13,3/253
9.24
0.62
44.99
LLama-30B
11.1/242
8.61
0.58
1376.15
11.7 11.7/288
8.52
24.19
Alpaca-7b
4.6/110
12.24
0.51
11.95
docs
5.9/145
11.99
0.55
12.99
davinci-001
5.2/106
12.44
0.55
7.15
docs
6.9/134
11.66
0.54
12.11
davinci-002
5.2/111
12.49
0.55
6.81
docs
5.8/128
11.89
0.51
11.42
davinci-00S
9.9/237
12.78
0.73
6.13
docs
6.9/191
11.94
0.63
11.63
Human answer (+ Human docs)
5.2/119
9.29
0.49
17.63
Table 9: Answer statistics for answers generated from various models with and without WebGPT evidence documents. AAs. represents (number of sentences number of words) in the generated answer.
B.7 Control Study on Location of Supporting Sentences
We aim to study whether the linear correspondence between the order of information presented in the documents and that presented in the answers still holds if we shuffle the evidence document set. As we do not have human annotations for this setting, we use T5 model we use in Section 7.2 to identify supportedness. If the answer sentence aj as the hypothesis is predicted as entailed by the document sentence d; as the premise, dj is considered a supporting sentence of aj. We compute the location of supporting sentences following Figure 2 and report the results in Figure We report aggregate results on settings in Figure 2aa) excluding WebGPT, namely GPT-3.5 (WebGPT docs, Human docs) and Alpaca TWebGPT docs), as we do not have access the WebGPT model. The linear correspondence as observed in Figure 2(a) and Figure ((a) is less pronounced when the documents are shuffled (Figure 6(b)). We further report Pearson correlation coeffiecient between the answer index fraction (answer sentence index i / # answer sentences) and the document sentence fraction (document sentence index j document sentences) in Table 13 When the documents are shuffled, the Pearson correlation coefficient is lower average and for GPT-3.5, and slightly higher for Alpaca. There is still weak correlation even when the documents are shuffled, thus supporting our arguments in Section 6 that the order of information presented in the documents affect the information presented in the answers.
C Data Collection Details
C.1 Crowdsourcing Details
We collect annotations on Amazon Mechanical Turk. We follow the UT recent work (Kamoi et al. 2023) closely 10 Interface screenshot can be found in Figure We work with turkers that have a HIT (Human Intelligence Task) rate greater than 98% with at least 500 completed HITs. Ten workers have passed our qualification test and participated in our tasks. We pay
An example annotation interface be found at httttt:://faaattsttt wtw.teesttteeeeuuaa.eooee perokuapp.com/iin
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Published as a conference paper at COLM 2024
Question
Why did we decide that certain words were "bad" and shouldn's be used in social settings?
GPT-
Bad words are used today to expres the deepest emotions of a person, such as anger, disgust, fear, or joy. Our English language is full of words that would make an adult in culture, no doubt, blush. There phrases that in the past were considered acceptable to use with people even though, on their own, they are used the phrase "God bless you'
considered, "bad". For example, I'm sure many of have witnessed without a raised eyebrow or a second thought. However, there are those people, in social situations, who feel the need state, or even ask, that they were, "prayiin for you. It could be construed as somewhat odd to someone that man, who simply standing sture eating his sandwich, was prayinn for his food.""
Flan-TS-XXL Llama-7B
Because they convey a negative message about themselves and their behavior.
This because we don't want to offend people. Would you please give me your answer? "" import re Don't raw_
use raw Inputo! Type your data into the script, and have the script log it. This way raw sa__npuu() doesn't limit the user's as input. response -ra__ing raw_input(" Questtion Why did we decide that certain words were "bad' and shouldn't be used in social settings? 'Answer This is because we don't want offend people. "Would you please give me your answer?") (") answer = response.sripp stripf print answer # the user entered something that was nut question, then print a message telling user that they entered something that was not question. answer: print "You did not enter question." " else....
Llama-13B
Words that are not considered appropriate in social settings are the words that make people feel uncorforrablle when they are used, words that make people feel had, that make people feel sick, that make people feel uncomfortable. Answer: There are many words that are 'bad" and shouldn't be used in social settings. They're words that make people feel uncomfortable when they's 'e used. Words that make people feel bad. Words that make people feel sick. Words that make people feel uncomfortable.
Llama-30B
It has always been socially acceptable to use certain words in private settings. However, when communicating with the public, has become acceptable to "clean" versions those words in order to avoid upsetting the people with whom you are interacting, # Very Simple String Reversal # worked on this by myself. #
spent 1.5 hours on this. httpp::/ / /github.com//// timottyshen// Saatlyshen/hhhhhhhpppp phase-0/blob/ master/ week-4/m/ my solution. =begin def reverse(string) string siiigg-pp((")) "eiit(").reveee"" reverse end reverse(")) == reversef "taf") == "tac' reversel raace car') in Spain raeht" wend Refactored Solution def reverse(string) string string.split(" split(") "elitt""..eeerree r.veerseejjon join(") end reverse(")) = reverse("can ("satt"""""" == "tac" reverse("race car') == 'race car' reverse( the rain in spain') "iin")) am "ain in Spain meht" # Reflection The major difference in the refactoring was the inclusion of the join method. This allowed the strings be joined together after being reversed, creating the complete original string...
"race car' reverse( thte rain in spain")
Alpaca-7B
The history of the words "bad" and "taboo" is closely related. The word "taboo" originated from the Tongan and Fijian language and culture and was introduced to English in the 18th century. The concept of taboos meant to ensure the continuity of culture by restricting certain behaviurs and activities could bring disruption or harm to community. Words and expressions that were deemed inappropriate were subject to taboo and avoided in social settings. This an effort to ensure that people engaging in behavior that could cause offense or distress. As time passed, the concept of tabon evolved and some words became increasingly associated with negative energy, misfortune, or danger, and thus became labeled "bad words". This negative association with certain words and expressions is what led to the idea that some words were too "bad" to be spoken in polite company.
from speaking
GPT-3.5 (davinci-001)
There is no one answer to this question- it is a complex issue with many contributing factors, Some people might say that certain words are 'bad' because they are considered to impolite or rude. Others might say that these words are offensive or derogatury, and that they should be avuided because they can be hurtful to others. There are also number words that have been labelled as "bad" because they are considered to be vulgar obscene.
GPT-3.5 (davinci-002)
There few reasons why we decided that certain words were 'bad' and shouldn't be used in social settings. First, sume of these words can be seen as offensive or hurtful to certain groups of people. Secund, using these words can make other people feel uncomfortable or unwelcome. Finally, using these words can be seen sign of disrespect.
GPT-3.5 (davinci-003)
The decision to deem certain words 'bad" inappropriate for use in social settings based the belief that certain words can be offensive, hurtful, or disrespectful to the people around us. By avoiding the use of such words, we show respect and consideration others, and create more welcoming and Additionally, certain words can be seen as symbols of certain oppressive systems or ideologies, and using them can make people feel unwelcome or even threatened. In order to create safer and more welcoming space, strive to avoid using words that carry negative connotations are associated with oppressive systems. Furthermone, by avoiding the use of such words, we can fuster a more pusitive and respectful atmosphere, and show consideration and respect to people of different backgrounds and experiences.
environment.
WebGPT
In general, words that are considered "bad" relate parts life that people don't like talking about in public, like bathroom functions./ ||] Other words considered had relate to negative ways of talking about people and their religion, skin color, ability, gender, etc.[1] These words are called slurs and many people consider them the worst kind of bad words.[1]
Some words are considered "bad" because they offend others [2] They are words that refer to impolite things, like body parts bodily functions and most people don't like to hear about it[[]] Some people believe there are no 'bad" words, just inappropriate times and places to say certain words.[2]
Table 10: Example answers generated by different base models. The models evaluate in our main experiments are boldfaced.
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Published as a conference paper at COLM 2024
WebGPT
Human
Bing
sufficient
17
10
Table 11: Number of examples where the evidence documents are sufficient for answering the question. We manually examine 20 questions in total.
Docimnn Sorfence Inder Fradtoo
5
8.
30.6
303
9.7
27
6.6
Answer Index Fraction
50 64 3.2
53
644 3.5 1. 67 5. 3.3
Avs
(a) Location of supporting sentences answers (b) Location of supporting sentences on answers generated with the documents ordering as pro- generated with the documents shuffled. vided.
Figure 6: The distribution of location of supporting sentences in the document set D for Nth answer sentence chunk. We normalize each column, and the "Avg" column shows the average across answer sentences. We report results when the documents are of the original order (a) or shuffled (b). The linear correspondence is between order of information presented in the documents and answers is weaker when the documents are shuffled.
$2.5 USD for each example, and the estimated hourly pay $15 USD. The total cost of the annotations is $5886.60 USD, including the cost of qualification tasks and pilot studies.
C.2 Annotation Guideline
We require all the crowdworkers to read the annotation guideline and take qualification test before doing any task. The annotation guidelines are provided at h/tps: looge offCheehILO0spppWWWW/Y/YC/////b
C.3 Annotation Interface
The annotation interface we showed the annotators is in Figure The documents are split into sentences and presented in paragraphs. The similarity scores to the current answer sentence, calculated with SimCSE, are meant aid the annotators in deciding if the answer sentence is supported. The question, answer, and the current answer sentence are shown on the right, followed by the annotation section. Annotations should include the label (whether the answer sentence is Supported, Partially Supporled, or Not Supported ), the supporting sentences, and the supported portion if the label is Partially Supported.
C.4 Comparison with other datasets
The collected dataset contains labels of whether each sentence in the answer is suppported by the evidence documents, providing benchmark for studying automatic attribution methods, We compare our dataset with recent attribution efforts in Table 14 WICE (Kamoi al. 20023 is multi-document entailment dataset where the hypothesis is a sub-claim from Wikipedia. AttrScore (Yue et al. 2023) creates data from existing QA datasets using heuristics, and creates small-scale, expert-annotated dataset (250 examples), AttrEval-GenSeachh by annotating attribution on outputs from generative search engines. Liu et al. (2023b) is the most closest to our work. They focus attribution, particularly citation, in long-form question answering, provided by newly arising generative search engines. The answers from these commercial systems provides optional citation to external document per answer
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Published as a conference paper at COLM 2024
Question
Why do benches empty when there is a fight in baseball?
WebGPT
Document E: Bench-clearing brawl - Wikipedia (en. len.wikipedia.org)
Documents
A bench-clearing brawl is form of ritualistic fighting that occurs in sports, must notably baseball and ice hockey, in which every player on buth leams leaves their dugouts, builpens, or benches, and charges the playing in order fight one amother or try to break up fight. Penalties for leaving the bench can range from nothing tu severe.
In baseball, brawls are usually the result of escalating infractions or indignities,(2) [2] often stemming from a batter being hit by pitch, especially if the batter then charges the mound. They may also be spurred by an altercation between baserunner and fielder, such excessive contact during attemppedd tag out.
Unlike most other team sports, in which teams usually have an equivalent number of players on the field at any given time, in baseball the hitting team at a numerical disadvantage, with a maximum of five players (the batter, up to three runners, and an on-deck batter) and two base coaches on the field at any time, compared to the fielding team's nine players. For this reason. leaving the dugout to join fight is generally considered acceptable that results numerical equivalence on the field, fairer fight, and a generally neutral outcome, as most cases, managers and/or or umpires will intervene to restore order resume the game.
Document 2: Rule 629 Leaving the Players' Bench or Penalty Bench www.usahockeyuuebbokk.
A major plus game misconduct penalty shall be assessed to any player who leaves the players' bench or the penalty bench during altercation or for the purpuse of starting altercation. These penalties are in addition any other penalties that may assesed during the incident. Substitutions made prior the altercation shall not be penalized under this rule provided the playens so substituting do nut enter the altercation. For purpuse of this rule, an altercation is considered to be concluded when the referee enters the referee's crease or, in the absence of penalties, signals face-off location.
Document BASEBALL; A Game Many Rules Has None on Fighting The New York Times
(www.nytimes.com)
The first player to leave either bench or penalty box to join or start a fight is automatically suspended without pay for 10 games. The second player to do that is suspended for five games without pay. The players' teams are fined $10,000 for the first incident and the coaches of the teams face possible suspension and fine based on review of the incident by the commissioner.
Human Documents
Document
Bench-clearing brawl (en wwn.wikippeii.orr!
A bench-clearing brawl form of ritualistic fighting that occurs sports, must notably baseball and ice hockey, in which every player un buth teams leaves their dugouts, bullpens, or benches, and charges the playing area in order to fight one another or try to break up fight. Penalties for leaving the bench can range from nothing tu severe.
Document Unlike MLB, the NHL stamped bench-clearing brawls (www.si.com
Although this particular brawl earned place in history, a similar sort of all-hands-on-deds silliness remains fairly regular feature in baseball.
Document 3: Bench-Clearing Brawls Just Not The Same Without Amphetamines (www.thebrushback.cog In the glory days of bench clearing brawls, real punches were thrown and real blood was shed, mostly because the players were incredibly high all the time.
Bing Docu- ments
Document What does it mean clear the bench?- TojesMojo not to mention dangerous, for the batter to charge the mound with a bat and has resulted in criminal charges). When was the last NHL bench clearing brawl? The last bench clearing brawl in the NHL was 1987-88. Fifty percent of the players that suited up in the 1980s had at least one fight. Why du benches empty? Most fights in baseball turn into what is known
bench-clearing brawl. This is when entire team's bench, sometimes Document 2: What does it mean to clear the bench?- TojesMojo
position in the field, then he may return to the mound although that rarely happens. Is bat flipping Calagal and the United States. In Canada, and the United States, bat flips have traditionally been considered rude and inconsistent with baseball etiquette. Traditional etiquette and the unwritten rules of baseball espouse humility and discourage actions which may be interpreted as arrogant or shawing up the opponents. Why catcher's throw to third base after a strikeout? Around the Homn If Document 3: Baseball Fighting Rules- rookieroad.com
players that leave their sideline benches will be subject to some pretty hefty fines. The MLB does not feel like can afford to automatically fine players for joining fight because of the way the game structured. In baseball, when there is fight between an offensive player and a defensive player, the offense is always going to be outnumbered. That is because unless there are offensive players on base, it will always be on Document 4 WATCH: Benches clear between Cincinnati Reds and Chicago - -Sportsnaut two sides met again in 2018, Garrett got his shot at revenge. With Cincinnati leading 4-2 in the seventh inning, struck out Baez end the inning, They stared each other down after Garrett celebrated the strikeout then started exchanging words. After Baez invited a fight, the two rivals charged at one another and the benches cleared out. Related: MLB trade rumors- Latest MLB rumors entering July. Needless to say, there is no love lost between these two. Fortunately, fight didn't
Table 12: Example of documents retrieved by WebGPT, human demonstration and Bing Search API. Document titles are bolded.

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Published as a conference paper at COLM 2024
GPT-3.5
GPT-3.5
Alpaca
Average
WebGPT docs
Human docs
WebGPT docs
Human annotations (unshuffled)
0.2110
0.1316
0.3234
0.2220
unshuffled shuffled
0.2094
0.2351
0.0743
0.1445
0.1748
0.1310
0.0825
0.1359
Table 13: Pearson correlation coefficient computed between the relative location of answer sentence a; (answer sentence index i / # answer sentences) and the relative location of document sentence dj (document sentence index / # document sentences) that support a; The numbers of human annotations (top row) are computed only on the 100 annotated examples, and the supporting sentences are identified by crowdworkers. Correlation is weaker for GPT-3.5 and marginally stronger for Alpaca when the documents are shuffled.
5:33
3.41
3:18
000
XXX
0da
AAX
DMA
A/X
Figure/ 7: Distribution of disagreement patterns in our collected data. O: Supported, A: Partially Supported, X: Not Supported.
sentence, and Liu et al. (2023b) provides annotation whether the such sentence-level citation valid, along with which sentences in the external article provides the information. Yet, their work studies black box system, which does not allow controlled study how differing evidence documents changes retrieval-augmented language model's 's generation process.
C.5 Disagreement Patterns of Annotations
We report the percentage of each annotation pattern in Figure O's denote Supported, triangles denote Partially Supported and X's denote Not Supported. All annotators agree on 70% of the examples. Two annotators agree on around 26% of the examples. All annotators disagree with each other on 3.4% of the examples.
23

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Published as a conference paper at COLM 2024
Queaton: what casaus tas oenar kos videe?
denue the Artiee
Answer (decorpposed into sentences):
[Decamant 1) What Nakes Ice Cabea Dlady11 Live Scincce
The conter cuteo whitlnn the hasling proses which cuatno seaal lamoerb
As the varar heeme, iiert form regu cystalive druutume foil which masts front into the renvinee liyule ynpu rike rosuting
guores
ges clud duinng the teet pouseet becauee howoes
1ott familes (bat purtouu phetggerie cuto stuuding
ayysall
creitt be image aeettco Our oue shares andia lis prod sokiy deoued eute
Acd konul, the deygen wader is enop foom crysla
bemsing dibra This a directional lneesingg which offers chance dlsarent cubes.
cantes
This man muse the picpe dly caydti dee warer your en tope. eppended emacia extimar
Current Answer Servince 11/ tp
2.50
Boct uiguas heus bore the salille de see
setto
The carter of cubes whitin dua hooding process wiich puihees umall amounts of
rinemb
luy kupeo of
fist paning doudy parts
sartane www.caawpppooooeo susported arick??
Famaly Suppored
Suppreed
STOP Choose Web this
2)
Paragroph
2.20
The vitia eull iee des stuatty very buiblee.
2.29
Sename
vaurr devolved ouygen sivear delerme heult eneooreen
PREVIOUS ANNOTATIONS:
raguli guanas pastaddang
Tha maarainge suyge and
loal..
Figure 8: Screenshot of the annotation interface. The documents are shown the left-hand side, along with the similarity score (SimCSE) to the current answer sentence. The right- hand side shows the question, answer, and the current answer sentence. The annotations go below the box for the current answer sentence.
Dataset
# Ex.
Text to be verified
Evidence Length (Avg words)
WICE
Kamoi
5.3K
Sub-claims of Wikipedia sentences
1586.4
al. 2023
Yue et al.
2023
4K
Sentence-lore answers generated by Chatgpt con- ditioned on the short answer from a QA dataset, long-form answers generated from commercial search engines
150.8
Liu et al.
2023b
11K
Sentence in long-form answers generated from commercial search engines
1792.5
ExpertQA Malaviya
12K
Sentence in long-form answers to expert-curated questions
679.3
et al. 2023
SALAD (Ours)
4K
Sentence in long-form answers generated from 396.0 LLMs
Table 14: Comparison to prior work evaluating attribution. "Size" denotes the number of annotated input-label pairs.
24


Reviews:
[{"review_id": "39uoS8Jehk", "reviewer": "Reviewer_x7LT", "summary": "The paper focuses on the topic of retrieval-augmented long-form question answering (LFQA). The two controlled studies show how the evidence documents or the LLMs impact the generated answers. The performance is evaluated in terms of both surface features and attribution to the evidence documents. It also includes an human-annotated dataset named SALAD, which could facilitate the research in LFQA. Several key findings help the committee better understand the status of retrieval augmentation for LFQA.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review and encouraging feedback. We address your questions and concerns below. \n\nRe: Sample selection / Data set size  \nWe select the 100 questions randomly from the test set of ELI-5, where we have WebGPT predictions available (a total of 271 questions). ELI-5 dataset contains diverse topics and so does the sampled set upon manual inspection. Our labeled data set is relatively small because of the cost of human annotations – the total cost is almost $6k USD for this number of questions. This covers 100 answers and around 700 sentences for each system, resulting in around 4k sentences that could be used for evaluation of automatic attribution.\n\nRe: variance when comparing different reference documents  \nThis is definitely an interesting result, but this should not be considered as a weakness of the paper. In figure 3b, the attribution numbers are computed when the documents specified with the ‘+’ sign are prepended. Figure 3b indicates that the percentage of supported documents is high only when the attribution is computed with respect to the prepended documents. \n\nRe: Open-source   \nYes, we will open-source the dataset upon publication. \n\nRe: Is GPT-3.5 trained without retrieval and attribution?  \nThank you for pointing this out! We do not know for sure it is trained without a retrieval component. However, none of their technical reports or documentation (e.g. Ouyang et al., 2022) has mentioned a retrieval component. We will update the text to reflect that this is not a known fact.  \n\nReference:  \nOuyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35, 27730-27744.\n\nGPT-4 technical report. https://arxiv.org/pdf/2303.08774", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper presents a controlled study with varying evidence documents and LLMs for the retrieved-augmented LFQA task.The study allows a clearer understanding of the impact of each component.\n\n2. It introduces the SALAD dataset with human attribution annotations for LFQA.\n\n3. The analysis and evaluation are easy to understand and thorough. It summarizes different error types as well as methods for automatic identification of unsupported answers.", "reasons_to_reject": "1. The size of the dataset is relatively small, which might affect the conclusion of this paper.  How do you select the 100 questions? The question types could affect the results as well.\n\n2. According to the results in FIgure 3b, it is interesting  that the results have a large variance when comparing Human docs and WebGPT docs. It seems Human only works well with Human docs, so does WebGPT (WebGPT docs) and Bing (Bing docs).", "questions_to_authors": "1. Will you open-source the dataset?\n2. How do you know GPT3.5  trained without retrieval and attribution? Is it a guess?", "extra_scores": {}}, {"review_id": "4rF57Ol0m9", "reviewer": "Reviewer_4Hj1", "summary": "This works presents a study on retrieval augmentation for long-form question answering. They perform 2 controlled studies : first they fix the LM and varying evidence documents, second they fix evidence documents and varying the LMs. This is a good setup as it resulted in interesting results/discussion useful for a better understanding of how retrieval augmentation work.\n\nThey also present a new dataset -SALAD, containing human annotations of sentence-level answer attribution in long-form question answering.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review and encouraging feedback. We address your questions and concerns below.  \n\nRe: Results differ from findings from Krishna et al.  \nThis is a valid concern! We do think results from both studies are trustworthy, but the difference in settings and models leads to different conclusions. \n\nKrishna et al proposes two possible reasons for why LMs are not using retrieval documents:\nThe retrieved documents have little overlap with the gold answer, so the generation model ignores the documents. \nThere is significant train-test overlap in the ELI-5 dataset, and models could answer based on their parametric knowledge alone, without the help of retrieved documents. \nBoth reasons do not apply in our setting (documents are more relevant and models are not fine-tuned on the data from the same distribution). There are other differences: \nFirst of all, they are only measuring ROUGE-L, which is the overlap between the predicted answers and the gold answers. However, we measure the attribution of the answers, which is how much the answers are supported by the prepended documents. \nThe model architectures are significantly different. They use a weaker retriever (REALM, Guu et al., 2020) compared to WebGPT (whose retriever is trained specifically for this task, †leveraging a commercial search engine, Bing), leading to documents potentially of less quality. They also use a much weaker LM (486M parameters) to generate the answer, compared to GPT-3.5 (175B parameters). Both the better quality of retrieved documents and a much stronger generation model could have contributed to the differing results. \n\nWe will update the paper to further discuss the differences between our set-up and theirs.\n\nRe: Better written discussion section  \nWe thank the reviewer for the feedback. While we  have included paragraph titles for a few of our analyses to improve clarity, we will clearly highlight takeaways with paragraph titles in the revised version for better readability. \n\n\nRe: Better figure 2 plot captions  \nWe apologize for the harder-to-follow caption. The caption is intentionally shortened due to the length limitation, and we will update it in the camera-ready version. \n\nReference:  \nGuu, K., Lee, K., Tung, Z., Pasupat, P. &amp; Chang, M.. (2020). Retrieval Augmented Language Model Pre-Training. Proceedings of the 37th International Conference on Machine Learning Available from https://proceedings.mlr.press/v119/guu20a.html.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The experiments are very extensive and well-detailed.\n2. Some of the findings are very informative and novel such as answer length decreases and answers become concise when relevant documents are given as evidence. The findings that \"order of evidence documents is reflected in the order of generated contents\" and \"the last sentence is almost twice as likely to be unsupported compared to other sentence in the answer\" are very interesting and provides a new depth to the understanding of RAGs\n3. The conclusion suggests strong directions for future models using RAGs and can be very useful for the community\n4. Overall I think this paper definitely improves the understanding of RAGs and provides new insights into their limitations and how dependent they are to given documents", "reasons_to_reject": "1. They authors argue that their results differ significantly from earlier works such as Krishna et al. I would have liked a better argument as to why that is happening and we should trust their results as compared to previous works. It might leave readers in a dilemma of whose results to trust.\n2. The discussion of results such as in Section 6 is difficult to follow. I understand that the reviewers had a lot of points to discuss and space constraint might have reduced some discussion, but a better written discussion section would help the readers get the key takeaways from this work more easily\n3. Figure 2 plot captions are difficult to read and a better readability will def help", "questions_to_authors": "1. Why do you think some of your results differ significantly from other works? Is it because of a different setting or some limitations of earlier works that you solved?", "extra_scores": {}}, {"review_id": "ktRcDnqMgV", "reviewer": "Reviewer_vo4B", "summary": "This paper presents a comprehensive analysis and study on retrieval-augmented long-form question answering (LFQA). Different from short answers, the LFQA receives less study on how the retrieved documents affect the long-form answers in various perspectives, which is the focus of this work. Through extensive experiments, the authors made several observations including different LMs' capabilities towards attributing their generated answers to the retrieved contexts, the effect of different sets of documents under retrieval for common metrics such as perplexity, self-BLEU, etc, and comparing different automatic models for fine-grained attribution evaluations.\n\nOverall, this paper is well written and clearly delivers the objective. The LFQA problem is indeed an under-explored area with much room for improvement. The findings given from this work provide useful insights and suggest potential directions for future work. The authors also conduct a comprehensive study with extensive experiments, testing various perspectives ranging from different LMs, different sets of retrieval corpus, diverse metrics, and attribution evaluations, which make the conclusion more convincing.\n\nA few limitations are observed:\n1. Some of the experiments/analysis are not so constructive and hence are less meaningful from my understanding. For example, Table 1 compares RankGen which focuses on the correlation between the question only and the generation. This does not seem to be relevant under the retrieval-augmented setting, because the answer could incorporate more contents from the context, making it less corresponding with the question. And the results do show this pattern, without any surprise. Further, two of the metrics in Table 1 demonstrate inverse effect given by relevant documents compared to no or random documents. I do not see how this could bring any meaningful insights from these results. Other more relevant metrics could be considered such as measuring the coherence, coverage, diversity etc.\n2. The last observation in Section 4 could be made more precise and meaningful - The divergence between this work and Krishna et al. (2021) is probably due to the model capacity. One possible conclusion made is that higher-capacity LMs are more robust to random contexts and pay more attention to relevant context. To better show this point, it could be more interesting to evaluate smaller LMs for emprirical comparison.\n3. Some experiments in Table 2 also seem less meaningful. I don't see the value of conducting experiments without documents and evaluate the supportedness. First, the documents from WebGPT might not be relevant for the question. Second, since the documents are not presented, it is not fair to force the model to generate answers relevant to the documents.\n4. The setting in section 7.1 merge partially supported with not supported. This is not entirely intuitive to me. Is there any reason why the partially supported should be treated as the negative class for NLI? What if this is merged with the supported label?", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Re: Table1 metrics  \nMeasuring coherence, coverage, diversity can be more insightful. Self-BLEU is designed to measure diversity. Coherence and coverage are difficult to evaluate automatically. We also argue that RankGen measures relevance of the generated answer with respect to the question, which is important for studying retrieval augmentation. Consistent RankGen scores across various document settings show that models are able to ignore irrelevant documents, while staying relevant to the question. We believe that these results still bring some insights for the state-of-the-art models.\n\n\nRe: Bigger vs. smaller models   \nWe agree that it might be interesting to show that larger models are more robust to random contexts and better incorporate relevant contexts! Our focus is on evaluating LLMs that are relevant (at the point of writing) and cover various properties (open/closed-source, trained with/without retrieval component). We will add discussion on the size of the model as future work. \n\nRe: Table 2 Results  \nWe present experiments without documents as a control setting. Comparing this setting with all the other settings with documents, one could conclude that the models are incorporating information from the documents into the answers. We will make arguments clearer. \n\nRe: Merging “Partially Supported” with “Not Supported”  \nBoth “Partially Supported” and “Not Supported” sentences contain information that is not supported by the documents. If the automatic attribution model detects any information that is not mentioned in the documents, the model would predict the sentence as NOT “Supported”.  \n\nRe: Dataset being evaluated is less diverse  \nWe only evaluate model generated answers on the ELI-5 dataset, which is the principal long-form QA dataset. While it would be nice to include questions from domains other than community QA, yet it will require collecting such questions, as well as high quality documents, which is expensive and out of scope for our paper.  Concurrent work (Malaviya et al., 2023) collected expert-curated questions across various domains and evaluated attribution of answers generated by LMs. Their settings differ from ours as they studied different attribution strategies (e.g. directly generating citation, post-hoc retrieval). We focus on a specific strategy, i.e. “retrieve-and-read” and present careful controlled study with different types of retrieved documents.\n\nReference:   \nMalaviya et al, 2023 https://arxiv.org/abs/2309.07852", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The problem of LFQA this work studies is an interesting and under-explored area.\nThis work provides a comprehensive evaluation and analysis, revealing many interesting observations which could be meaningful for further studies.\nThe authors also introduce a valuable dataset annotated with fine-grained attribution labels in the sentence level. This could be useful for future research.", "reasons_to_reject": "Some of the experiments and conclusions are not constructive.\nSome experimental settings and observations are not clearly explained.\nThe dataset being evaluated is less diverse.", "questions_to_authors": "The setting in section 7.1 merge partially supported with not supported. This is not entirely intuitive to me. Is there any reason why the partially supported should be treated as the negative class for NLI? What if this is merged with the supported label?", "extra_scores": {}}, {"review_id": "MyqmA8dRss", "reviewer": "Reviewer_pLGb", "summary": "This paper looks at how in-context examples affect long-form answers (more than a word or phrase) in LLM generation.  The authors vary both the model and the documents given, and measure aspects of the answer.  They find that the answers are impacted by the documents given (even by random documents), and answers are often related to the input documents (even corresponding to the order of the documents given), but answers are not always attributable to the documents (unless the model was trained for that behavior).  They evaluate automatically using several statistics that indicate higher quality (e.g., looking at fluency, length, variance), but no automatic metric is used that really targets a correct answer.\n\nThis paper covers and interesting topic and delves into it a bit, but the description of the task and the dataset (SALAD) leaves me a bit unclear.  Are the answers in the SALAD dataset given scores of correctness or just how much their answer is supported by the source document?  I would like to understand exactly what is in the SALAD dataset and how it is useful.  Is it useful for evaluating new LLMs or only the LLMs used to generate it?\n\nNotes:\n- Figure 1 references sections 3 and 5 for attribution, but I think it should be sections 5 and 7 (as stated in the caption to the same Figure)\n- Figure 2 - the heat maps are hard to interpret.  What are they intended to show... just an idea of approximately where the answers are coming from?  Also the text of parts (a) and (b) runs together... would be more readable if spaced farther apart.\n\n[After author discussion]\nI am raising my score to 7 - Good paper, accept", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": true, "final_justification": "", "rebuttal": "Thank you for your review and encouraging feedback. We address your questions and concerns below. \n\nRe: What is evaluated in the SALAD dataset?  \nWe clarify here that the goal of our paper is to measure attribution, instead of answer correctness, which we have attempted to clarify in the paper:  In the “Setup” paragraph, we mention that labels of a particular answer sentence is “Supported”, “Partially Supported”, and “Not Supported”. The labels are referring to whether this answer sentence is “supported” by the reference documents, and we mention nothing about correctness of the answer. \n\nRe: Is SALAD useful for evaluating new LLMs?  \nThe SALAD dataset only evaluates attribution of the LLMs selected, and cannot provide attribution evaluation on newly generated answers. Yet, SALAD could serve as a benchmark for “evaluating” automatic attribution. This is what we do in Section 7. Once we identify a good automatic attribution prediction model (e.g. the T5 model which we reported performance in Section 7.1), we could use that model to evaluate new generations. We will integrate this discussion into the main text in the revised version. \n\nRe: Figure 1 reference  \nYes, it should be Section 5 and 7, thank you for catching this! We will update the figure in the camera-ready version. \n\nRe: Figure 2   \nThey are intended to show which part of the answers are supported by which part of the documents. As mentioned in the paper, the heatmap has higher numbers on the diagonal, showing that the order of information mentioned in the answer is roughly in line with the order of information mentioned in the documents. In other words, answer sentences that come first are supported by sentences that appear earlier in the documents, and sentences that come later are supported by sentences that appear later in the documents. We hope this is clear, and we could clarify if there are further questions. \n\nRe: Automatic evaluation instead of human evaluation for a subset of LMs  \nAs mentioned in the rebuttal to review x7LT, the cost for human annotation for this project (100 examples per setting for 6 settings) is almost $6k USD. This is the most we can have under our budget, and we believe that the automatic evaluation method reasonably correlates with human annotation and that the results/discussion in Section 7.2 would not change much with more human evaluation.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Empirical research in LLM generation with in-context documents", "reasons_to_reject": "- Lack of clarity with methodology and dataset [Addressed in Author Discussion]\n\n- Weak automatic evaluation method for most LLMs (only WebGPT, GPT-3.5, and Alpaca get human evaluations)", "questions_to_authors": "- Are the answers in the SALAD dataset given scores of correctness or just how much their answer is supported by the source document?  I would like to understand exactly what is in the SALAD dataset and how it is useful.  Is it useful for evaluating new LLMs or only the LLMs used to generate it?\n\n- What are Figure 2's heat maps intended to show?", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper explores how retrieval-augmented language models (RAGs) utilize evidence documents for long-form question answering (LFQA). The study introduces the SALAD dataset, containing human annotations for sentence-level answer attribution, and evaluates existing methods for automatic attribution judgment. The findings reveal that while LMs can leverage relevant documents, the generated answers are only partially attributable to the documents, with unsupported sentences being a significant issue.

The paper is well-executed and offers valuable insights into the use of RAGs for LFQA. Despite some concerns about the clarity of the discussion and the relevance of certain experiments, the comprehensive study and novel findings contribute significantly to the field. Therefore, the paper is accepted with minor revisions. 

To enhance the paper, the authors should provide a clearer explanation of why their results differ from earlier works like Krishna et al., ensuring readers understand and trust their findings. The discussion section should be rewritten for better clarity, making the key takeaways more accessible. Improving the readability of Figure 2 captions is also recommended.
Decision: Accept