Generate rather than Retrieve: Large Language Models are Strong Context Generators
Authors: Wenhao Yu
Dan Iter
Shuohang Wang
Yichong Xu
Mingxuan Ju
Soumya Sanyal
Chenguang Zhu
Michael Zeng
Meng Jiang
Venue: ICLR 2023 poster (2023)
Keywords: prompt learning
large language model
open-domain question answering

Abstract: Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tasks is to employ a retrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such as Wikipedia and then predicts an answer conditioned on the retrieved documents. In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextual documents based on a given question, and then reads the generated documents to produce the final answer. Furthermore, we propose a novel clustering-based prompting method that selects distinct prompts, in order to generate diverse documents that cover different perspectives, leading to better recall over acceptable answers. We conduct extensive experiments on three different knowledge-intensive tasks, including open-domain QA, fact checking, and dialogue system. Notably, GenRead achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0 and +3.9, without retrieving any documents from any external knowledge source. Lastly, we demonstrate the model performance can be further improved by combining retrieval and generation. Our code and generated documents can be found at https://github.com/wyu97/GenRead.

Published as a conference paper at ICLR 2023
GENERATE RATHER THAN RETRIEVE: LARGE LANGU- AGE MODELS ARE STRONG CONTEXT GENERATORS
Wenhao Yu'+ Dan Mer',,huohhnn, Wang", Yichong Xu², Mingxuan Ju',
Soumya Sanyal", Chenguang Zhu", Michael Zeng', Meng Jiang'
!University of Notre Dame Microsoft Cognitive Service Research
University of Southern California
"wyulend edu; 2 itterda@@iirroooft.. com
ABSTRACT
Knowledge-intenn intensive tasks, such as open-domain question answering (QA), require access to large amount of world or domain knowledge. A common approach for knowledge-intensin tasks is to employ a relrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such Wikipedia and then predicts answer conditioned the retrieved documents. In this paper, we present novel perspective for solving knowledge-intent -ntteeeieeeeniveeeeeeeeeeee tasks by replacing document retrievers with large language model generators. We call our method gemerate-then- thaa- (GENREAD), which first prompts a large language model to generate contextual documents based on given question, and then reads the generated documents to produce the final answer. Furthermore, we propose a novel clustering-bar -based prompting method that selects distinct prompts, in order to generate diverse documents that cover different perspectives, leading to better recall over acceptable answers. We conduct extensive experiments on three different snowledge-intensivee tasks, including open-domain QA, fact checking, and dialogue system. Notably, GENREAD achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ. significantly outperforming the state-of of-tth-rrt retrieve-then- read pipeline DPR-FiD by +4.0 and +3.9, without retrieving any documents from any external knowledge source. Lastly, demonstrate the model performance further improved by combining retrieval and generation. Our code and generated documents can be found https: : / / /i thub. com/ com/wyu97//// Rende
INTRODUCTION
inoeleige-intensive tasks, such open-domain question answering (QA) and fact checking, require access to a large amount of world or domain knowledge (Petroni et al., 2021). These tasks are even challenging for humans without access to an external knowledge source such Wikipedia. common thread of existing methods for knowledge-intene intensive tasks employ a retrieve-then-read pipeline that first retrieves handful of relevant contextual documents from Wikipedia and then conditions the prediction of the answer on these documents along with the question (Karpukhin et al., 2020; Lewis al., 2020; Izacard & Grave, 2021). Nevertheless, these methods mainly suffer from three drawbacks. First, candidate documents for retrieval are chunked (c.g.. 100 words) and fixed, so the retrieved documents might contain noisy information that is irrelevant the question. Second, the representations of questions and documents are typically obtained independently in modern two-tower dense retrieval models (Karpukhin al., 2020), leading to only shallow interactions captured between them (Khattab et al., 2021). Third, document retrieval over a large corpus requires the retriever model to first encode all candidate documents and store representations for each document. These two operations limit the parameters of dense retrievers and the size of embedding vectors, and thus cannot enjoy the world knowledge or deduction capabilities of large language models (Levine et al., 2022).
Unless otherwise specified, the text-davinci-002 version of InstructGPT in our experiments.
Work done during internship at Microsoft Cognitive Service Research group.

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Published as a conference paper at ICLR 2023
In this paper, we propose to leverage large language models, such as InstructGPT (Ouyang et al., 2022), to directly generate contextual documents for given question, instead of retrieving relevant documents from external corpus, such as Wikipedia. Our approach has two main advantages. First, we show that generated contextual documents contain the correct answer more often than the top retrieved documents. We helieve this is because large language models generate contextual documents by performing deep token-level cross-attention between all the question and document contents, resulting in generated documents that are more specific to the question than retrieved documents. Second, we show that our approach significantly outperforms directly generating answers from large language models despite not incorporating any new external information. This is mainly because the task of generating document-level contexts is close to the objective of causal language modeling pre-training, so the world knowledge stored in the model parameters can be better utilized.
We show, on multiple datasets, that generated documents are more likely to contain correct answers than the top retrieved documents. Notably, in dense retrieval methods, as more documents are retrieved, the recall of documents containing the correct answer increases (Karpukhin et al., 2020), However, the recall performance does not scale as well with generated documents because even with sampling methods, generated documents tend to contain duplicate information. In order to improve the recall performance of generated documents, we propose a novel clustering buuserring-baee prompt method. We synthesize prompt with in-context demonstrations of question-documen pairs sampled from diverse clusters. These prompts result in generated documents that cover different perspectives of the question and improve the scaling of performance more documents are generated per question.
In contrast to the retrieve-then-readd pipeline, our method is essentially generate-then-redd pipeline. Specifically, it first prompts large language model generate contextual documents based on a given question, and then reads the generated document to produce the final answer. The reader can still be a large model (e.g.. InstructGPT (Ouyang al., 2022)) used under zero-shot setting, or a small one (e.g., FiD (Izacard & Grave, 2021)) fine-tuned with generated documents on the training split of the target dataset. We evaluate our proposed method on three different Knowledge-intensive tasks and demonstrate its effectiveness on both zero-shot and supervised settings.
Overall, our main contributions can be summarized as follows:
1. We propose a novel generate-then-ree pipeline for solving knowledge-intense intensive tasks, i.c., replacing the process of retrieving documents from Wikipedia or searching for related documents on Google, by prompting large language model to generate relevant contextual documents.
2. We propose novel clustering- based prompting approach to generate multiple diverse contextual documents that increases the likelihood of covering the correct answer. We demonstrate this approach can significantly improve performance end QA and other downstream tasks.
3. We conduct extensive experiments with three knowledge-intenee inttegivveieeivveeeeee NLP tasks under both zero- shot and supervised settings. Notably, our method can match or even outperform perieve-then-Pee pipeline methods, without retrieving any documents from any external knowledge source.
RELATED WORK
KNOWLEDGG--NTENNIVE NLP VIA RETRIEVE-THEN REAA- PIPELINE. Mainstream methods for solving knowledge-intenne intengivveeeetvveeeee NLP tasks employ a retrieve-fhen-read model pipeline. Given question, this model first leverages a retriever over a large evidence corpus (e.g. Wikipedia) to fetch set of relevant documents that may contain the answer. A reader is then used to peruse the retrieved documents and predict an answer. Recent follow-up work has mainly focused on improving the retriever (Karpukhin et al., 2020; Qu al., 2021; Sachan et al., 2022) or the reader (Izacard & Grave, 2021; Cheng et al., 2021; Yu et al., 2022), or training the system end-to-end (Lewis et al., 2020; Singh et al., 2021). Early retrieval methods mainly employed sparse retrievers, such as BM25 (Chen et al., 20.7). Recently, ORQA (Lee et al., 2019) and DPR (Karpukhin et al., 2020) have revolutionized the field by utilizing dense contextualized vectors for document indexing, leading to superior performance traditional approaches. We propose alternative approach which forgoes retrieval, instcad extracting the knowledge from the model parameters of a large language model. We show that our approach is can be combine with dense retrievers outperform both methods independently. Our method can also be combined with any reader mechanism, allowing generated context documents to be plugged into any current mnowledge-intensve NLP pipelines.
GENERATOR AS RETRIEVER FOR OBTAINING CONTEXTUAL DOCUMENTS. Recent works have investigated using auto-regressive language models to generate identifier strings for documents, an
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Published as a conference paper at ICLR 2023
intermediate target for retrievals, such as entity names (De Cao et al., 2020) or distinctive n- n-grams that can be mapped to full passages (Bevilacqua et al., 2022). However, one needs to create the identifiers, not thoroughly evaluated on a large-scale benchmark (Bevilacqua et al., 2022). Other works have demonstrated that the knowledge stored in the parameters of pre-trained language models could be "retrieved" some extent by directly generating text (Petroni et al., 2019; Roberts et al., 2020). However, the previous work only used generation for query expansion (Mao et al., 2),1), which did not exploit the potential of directly generating contextual documents for open-domain questions. Different from the above approaches that aimed to train a generator model to produce contextual document identifiers (which is still using the original Wikipedia text) or provide data augmentation to retrievers, our work directly generates contextual documents for given questions.
hence the structure
NLP MODELS ENHANCED BY LARGE LANGUAGE MODEL. OUTPUTS. A line of recent work has shown that relevant knowledge can be elicited from large language models, especially for those domains that lack appropriate knowledge bases with sufficient coverage (Liu et al., 2022b; Fang et al., 2022). For example, Liu et al. (2022b) proposed leveraging GPT-3 to generate relevant contexts, then providing the contexts as additional input when answering a commonsense question. Another line of work focused prompting large language model to generate a series of intermediate reasoning steps, often referred to as chain-of chain-of-thoud of-thought (Wei et al., 2022b; Kojima et al., 2022; Li et al., 2022). The prompt consists of instruction (e.g.. Let's think step by step!). a few demonstrations that fixed for each task, and a new-question placeholder. The demonstrations are human-written and each consists of question in the style of the task and series of intermediate reasoning steps that is helpful for answering the question. Our work does not require any human annotation, but adds to this line of work of leveraging model generated text to guide further generations. In our case, we apply this approach to nowledge-intensiie tasks, which have not been explored by previous work.
PROPOSED METHOD
In this section, we present details of our proposed novel generate-then-reat (GENREAD) pipeline for solving various knowledge-intensive tasks. Specifically, first prompts large language model to generate contextual documents with respect to a given query, then reads the generated documents to predict the final answer. The reader can either be large model (e.g., InstructGPT) used for the zero-shot setting, small one (e.g.. FiD) fine-tuned with generated documents on the training split of the target dataset. We introduce the zero-shot setting 53.1 1 and supervised setting $3.2.
3.1 ZERO-SHIOT SETTING
Under the zero-shot setting, there is no training data neither questions nor contextual documents. When tested on the open-domain QA task, most existing large language models directly encode the given question and predict the answer (Brown al., 2020; Du et al., 2022; Chowdhery et al., 2022). Specifically, the question q. associated with some text prompt, is input to the model, which then generates the answer, denoted as p(alg, 9), where 0 represents the pre-trained model parameters. In practice, the maximum a posteriori estimation (MAP) is the final answer, i.e., = arg max, p(a lg. f). However, this way of directly asking large language models to output answers often leads poor performance, as it leaves a considerable amount of additional world knowledge unexploited (Levine al.. 2022). On the contrary, the zero-shot retrieve- teeereethe--raad pipeline first uses an off-the-shel retriever to fetch relevant documents from an external knowledge source such as Wikipedia, then asks the large language model to read the documents and predict the answer.
In this work, we improve the performance by introducing an additional auxiliary generated document variable d, and then extend the model to have the form p(alg) = E, p(ald,, 4)p(d.)g) In practice, we cannot sum over all possible documents d. Therefore, the most common approach is to compute the MAP estimate d arg max p(d) using beam search, and then to approximate the sum over d with this single value. This two step approach, we label it as a generate-then rhen-readd pipeline.
STEP1: GENERATE. In this step, we first prompt large language model (e.g., InstructGPT (Ouyang et al., 2022)) to generate documents based on the given question. For example, the input to the language model could be "Generate background document to answer the given question. {question placeholder)" We can use any decoding strategy (e.g., greedy decoding, beam search), but we used greedy decoding throughout the zero-shot experiments for simplicity and reproducibility.
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Initial d:
Generated d,
Generated dy:
Step Get one document df for each Question via retrieval generation.
Monsanto multinational
Monsanto Company Ame- rican multinational agrochemical and agricultural biotechnology corporation lon... ... leading producer of genetically engi- and (70
Monsanto multinational agricultural biotechnology corporation. The company also manufacturas agricultural chemicals, such insecticids.... (36 words)
agrochemical and agricultural biotechnology corporation one of world's leading
Quesiton
Document
Cluster
producers of roundup, gly- phosate herbicide. words)
What does
own? (W06)))
Rg
Step Given question training inference. cluster RI...):
sample (9t. R.y]1 n.., whose cluster id is create promit p. = "quiieei
agricultural chericals seed (also correct)
Ooxi dla
Step Get embeddingg, and cluster them K-means.
generate document with using large language model.
Using a reader (e.g. FIB), with q and the diverse documents (dy day de), find answers a.
Figure 1: An overall framework of clustering- prompting method. It leverages distinct question- document pairs sampled from each embedding cluster as in-context demonstrations to prompt large language model to generate diverse documents, then read the documents to predict an answer.
generated sentence d along with the input question to produce the final answer from the large language model. This is actually the same setting as "zero- shot" reading comprehension, as widely studied in existing works (Brown et al., 2020; Lazaridou et al., 2022). We choose appropriate prompts from P3 (Bach et al., 2022), such as "Refer to the passage below and answer the following question. Passage: {background placeholder) Question: {question placeholder)". Finally, the language model is fed the prompted text to generate an answer.
STEP 2: READ. In the second step,
3.2
SUPERVISED SETTING
Although large language models demonstrate impressive performance on zero-shot learning abilities, their performance still lag behind the supervised setting. Therefore, we also explore how the generated documents from large language models can benefit the supervised setting, As directly fine-tuning large language models on downstream datasets could be prohibitively expensive, we leverage a small reader model such as FiD to peruse the generated documents under the supervised setting.
Under the supervised setting, scaling the size of retrieved documents can lead to better perfor- mance (Karpukhin et al., 2020; Izacard & Grave, 2021). This is mainly because retrieving more documents can cover more relevant information and knowledge, i.e., a higher recall score. Nev- ertheless, asking a large language model to generate multiple high-qualit, contextual documents is challenging task. Dense retrieval methods can fetch multiple documents covering different perspectives of the answer. Compared to dense retrievers, simply prompting large language model to generate multiple contextual documents often leads to low knowledge coverage, since the contents generated by multiple decoding passes from the same input tend be similar. Sampling decoding methods, such as nucleus sampling' (Holtzman et al., 2020) can diversify the generation process to some extent, but the knowledge content of generated texts still tends to highly repetitive when used to generate documents for a given question. We further propose two novel solutions, including diverse human prompts and clustering- prompts, which will be elaborated on this section.
3.2.1 DIVERSE HUMAN PROMPTS
In order to avoid similar token distributions under a single prompt, we ask human annotators to provide different prompts, in order to make the generated document diverse. This method simple, but can effectively vary the token distribution during generation. In the experiments, we empirically found this method can bring improvement the retrieval performance (Figure 2). However, this method suffers from two drawbacks. On one hand, it requires human annotators to write different prompts, which cannot be easily generalized to different mnoweedeeinteesstc hand, different large language models might be sensitive to different prompt words, which might cause a set of good prompt words not work on different large language model.
tasks. On the other
3.2.2 CLUSTERING-BASSH PROMPTS
To increase knowledge coverage in generated documents, we propose a novel clustering bause prompt method. It first clusters the representations of set of documents into classes (K 2 in Figure
We treated nucleus sampling as baseline generate multiple documents, in which set p .95.
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Open-domain QA
Fact Checking FEVER FM2
Dialogue System WoW (F1 / R-L)
Models
NQ
TriviaQA WebQ
*with retriever, AND directly trained on these datasets
DPR + InstrucGGPPP*
29.1
53.8
20.2
79.8
65.9
15.4
13.7
*with retriever, BUT NOT trained on these datasets
BM25 + InstructGPT
19.7
52.2
15.8
78.7
65.2
15.7
13.7
Contriever InstructGPT
18.0
51.3
16.6
80.4
66.6
15.5
14.0
Google InstructGPT
28.8
58.8
20.4
82.9
66.0
14.8
13.2
*without retriever, and not using external documents
Previous SoTA methods
24.7'
56.72
19.0
InstructGPT (no docs.) 20.9
57.5
18.6
77.6
59.4
15,4
13.8
GENREAD (InstructGPT) 28.0
59.0
24.6
80.4
65.5
15.8
14.2
Table 1: Zero-shot open-domain QA performance. Our proposed GENREAD with the InstructGPT reader (named GENREAD (InstructGPT)) can significantly outperform the original Ins,ructGPT, achieving new state-of-the-arr performance on three open-domain QA benchmarks (previous SoTA: 'GLaM (Du al., 2022), 2FLAN (Wei al., 2021)) under this setting without using any external document. Our GENREAD can achieve comparable or even better performance than zero-shot retrieve-then-read models that use retriever or search engine to first obtain contextual documents. To ensure reproducibility, we use greedy search in decoding. All prompts used are shown in the B.
1), where the number of classes is equal to the number of documents that need to be generated in the end. Next, it randomly selects question-document pairs (n = 5 in Figure 1) from each cluster. Lastly, large language model presents the different question-documen pairs in-context demonstrations for generating documents to a given question. In this way, large language models are based on different distributions of examples, hence resulting in generated documents covering different perspectives. We show this in Figure and illustrate the details of each step as follows.
STEP 1: GET ONE INITIAL DOCUMENT PER QUESTION. Similar to the zero-shot setting, we first ask a large language model to generate one contextual document d for each question E Q, where Q is the set of questions in the training split. Alternatively, we can use an unsupervised retriever (e.g..
BM25) obtain a document from Wikipedia. We now have question-document pair set (g: d; ==1
STEP 2: ENCODE EACH DOCUMENT, DO K-MEANS CLUSTERING. We then use a large language
model (i.e., GPT-3) to encode each question-document pair, i.e., e; GPT-3((g,, d:]). resulting in 22288-aimensioonl vector per document. Then, we use K-means to cluster all embedding vectors
[e:) into K sets, so each question-documee pair is assigned unique cluster id E (1,...K) We vary the number of K the experiments, which will be illustrated in Figure 2.
[Q]
STEP 3: SAMPLE AND GENERATE K DOCUMENTS. Lastly, we sample n question- -irioonnndmmettt pairs hyperparameter"
from each cluster c, denoted as {yel, dei; (e2, da2; ...; Jen: den }. in which Then, the sampled question-document pairs from the same cluster serve as in-context demonstrations for the large language model to generate a contextual document. For example, the input to large language model could be "[ye1 placeholder) {dei placeholder) ... {gen placeholder) {den placcholder) {input question placeholder)" By enumerating the sampled documents these K clusters, finally get K -gennrrted documents. By conditioning different sampled in-context demonstrations collected from different clusters, the large language model has been biased for different perspectives. Although these different perspectives exist in latent manner, we empirically show it works well in practice, by comparing it with sampling methods, diverse human prompts (Figure and Table 2) and randomly sampling n pairs from the entire dataset (Table 11).
4 EXPERIMENTS
In this section, we conduct comprehensive experiments on three knowledge-intensive NLP tasks, including open-domain QA (NQ (Kwiatkowski et al., 2019), TriviaQA (Joshi al., 2017) and WebQ (Berant et al., 2013)). fact checking (FEVER (Thorne et al., 2018) and FM2 (Eisenschlos al., 2021)) and opmn-domain dialogue system (WoW (Dinan al., 2019)). More detailed dataset
II the experiments,
5 and found increasing does not bring improvement.
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90
80
72
Recall aall@k
Google search
Recall@K
Google search
66
DPR-Multi
56
DPR-Multi
Ours (sampling)
Ours (sampling)
58
Ours (human)
48
Ours (human)
Ours (clustering)
Ours (clustering)
40
5
10
15
20
10
TriviaQA
Topk documents
WebQ
Topk documents
Figure 2: Recall@K K on test sets, measured as the percentage of top-K documents that contain the answer. Our proposed clustering- based prompting method can outperform DPR and Google search, also two variants of using LLMs to generate documents. Exact numbers are reported in Table
information can be found in Appendix A.I. To evaluate the model performance, we use exact match (EM) score for evaluating open-domain QA (Zhu et al., 2021). An answer is considered correct if and only if its normalized form has match in the acceptable answer list. We also employ Recall @ K (R @ K) as an intermediate evaluation metric, measured as the percentage of top- retrieved generated documents that contain the answer. This metric is commonly used in evaluations of previous works (Karpukhin et al., 2020; Izacard & Grave, 2020; Sachan et al., 2022). For other knowledge-intensive tasks, we follow the KILT benchmark (Petroni al., 2021) use accuracy (ACC) for fact checking and F1 / Rouge-L (R-L) score for open-domain dialogue system.
4.1 ZERO-SHOT SETTING EXPERIMENTS
We first compare our proposed GENREAD approach with various large language models proposed in recent years, including GPT-3 (Brown et al., 2020), Gopher (Rae et al., 2021), FLAN (Wei et al., 2021), GLaM (Du et al., 2022), Chinchilla (Hoffmann et al., 2022), PaLM (Chowdhery al., 2022) and InstructGPT (Ouyang et al., 2022). Due to the space limitation, we only put the best performance on each dataset in Table I, which the line is called previous SoTA methods. addition, their corresponding model parameters and performance baseline methods use the same input formats, i.c., [prompt words; question].
listed in Table 9 in Appendix. All of these
GENREAD is based InstructGPT with 175B parameters. In order to fully evaluate the effec- tiveness our proposed method, we also compare with InstructGPT augmented with retrieved documents from Wikipedia Google search. The baseline methods (1) BM225 / Contriever In- structGPT; (2) Google + InstructGPT,; (3) DPR InstructGPT have the same input format as our GENREAD i.e., [prompt words; contextual document; question]. BM25 is traditional sparse retrieval method. Contriever (Izacard et al., 2022a) is state-of-the-art unsupervised dense retrieval model. DPR (Karpukhin al., 2020) is supervised dense retrieval model directly trained on NQ. TriviaQA and WebQ datasets. We note that comparing with above three methods is challenging because our method only relies on the large language model itself, without using any external corpus.
4.1.1 EXPERIMENTAL RESULTS
In the experiments, we use InstructGPT as our backbone model. As shown in Table 1, compared with state-of-the-art large language models, our proposed GENREAD with the InstructGPT reader improves its performance by generating contextual documents and conditioning on the generated documents, even though no new data is introduced, and the generator and reader have the exact same parameters. Specifically, GENREAD can improve the EM score by +6.9 on three open-domain QA benchmarks, compared to the original InstructGPT. We also make similar observation on fact checking and open-domain dialogue system. Our proposed GENREAD consistently outperform the baseline InstructGPT model without retrieving any contextual documents.
To further validate the effectiveness of GENREAD
compare against zero-shot retrieve-then-readd pipeline models, which first use a retrieval model or the Google search engine to get a relevant contextual document, then use InstructGPT to read the texts and produce the final answer. As shown Table 1, GENREAD can achieve on on-par performance with zero-shot petrieve-thert then-reeddddddee pipeline models on the NQ and FM2 datasets, and outperform them all other benchmarks. The knowledge

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Published as a conference paper at ICLR 2023
# reader # docu- ments
TriviaQA
WebQ open test
NQ
Models
Avg.
parameters
open test
open test
$baselines with retrieving from Wikipedia; all numbers reported by existing papers
DPR (Karpukhin et al., 2020)
110M
100
56.8
41.1
41.5
46.5
RAG (Lewis et al., 2020)
400M
10
56.1
45.2
44.5
48.6
FiD (Izacard & Grave, 2021)
770M
100
67.6
50.5
51.4
56.5
$baselines with retrieving frrrieeinnggoom from Wikipedia or Google; all numbers from our experiments
FiD-1 (DPR, Wikipedia)
770M
10
61.9
48.1
46.7
52.2
FiD-xl (DPR, Wikipedia)
3B
10
66.3
50.8
50.1
55.7
FiD-xl (Google search)
3B
10
70.1
53.6
45.0
56.2
our proposed method by leveraging a large language model to generate documents
GENREAD (FiD-I) (sampling)
770M
10
67.8
51.5
40.3
53.2
GENREAD (FiD-I) (clustering)
770M
10
70.2
53.3
43.5
55.6
GENREAD (FiD-xl) (sampling)
3B
10
69.6
52.6
42.6
54.9
GENREAD (FiD-xI) (clustering)
3B
10
71.6
54.4
45.6
57.1
merge retrieved documents with generated documents
74.3
56.2
54.0
61.5
Table 2: Supervised open-domain QA performance. By only using generated documents from In- structGPT, our GENREAD with FiD reader (named GENREAD (FiD)) can achieve better performance than baseline methods on TriviaQA and WebQ. Through our detailed analysis of NQ. found the performance gap mainly due to the temporality issue, which will be elaborated in $A.8.
learned by the large language models be retrieved via autoregressive text generation. Without seeing any examples from these datasets, GENREAD can outperform using the supervised retrieval model (i.e., DPR) recover relevant contextual documents.
4.2 SUPERVISED SETTING EXPERIMENTS
We compare our proposed GENREAD with retrieve-then-read models, including DPR (Karpukhin al., 2020), RAG (Lewis al., 2020), and FiD (Izacard & Grave, 2021). In addition, we compared with obtaining relevant documents from the internet using the Google search engine.
4.2. EXPERIMENTAL SETUP
For our proposed method, we replace the retriever with large language model to directly generate contextual documents. In the experiments, we use InstructGPT (Ouyang et al., 2022). After contextual documents are retrieved or generated, we employ Fil) reader with 770M parameter models (i.e., FiD-I) and 3B parameter models (i.e., FiD-xI) that are fine-tuned on the training split of target datasets. We note that we only use 10 documents during reading for the following reasons.
Why do we choose to use only 10 documents instead of 100 when reading?
As noted in Section 6.2 in DPR (Karpukhin et al., 2020) and Figure 3 in FiD (Izacard & Grave, 2021). increasing the number documents can lead to better model performance and achieve state-of-the-art when using 100 documents. However, there two major drawbacks to using 100 documents during the reading step. First, the operation very expensive, leading to a significant increase memory consumption and training time. As reported by Izacard & Grave (2021), the training process requires 64 Tesla V100 32GB running for around one day. Second, generating documents by using a large language model is slow and expensive, so only using 10 documents in our method. Therefore, in our experiments, we choose to use 10 documents during the reading process. When using FiD-770M (i.e., FiD--arge), the training process can be easily performed even on a single Tesla V100 32GB GPU. Mcanwhile, when only using 10 documents, we can also increase the size of FiD model from 770M 3B, which takes about the same amount of GPU memory as using 100 documents on a 770M model, but at the same time significantly shortens the training time. We note that training T5-3B model needs a bigger cluster such as 8 Tesla V100 A100 GPUs.
be significant cost saving
4.2.2 EXPERIMENTAL RESULTS ON OPEN-DOMAIN QA
We first use Recall @ to compare the retrieval accuracy of different models. As shown in Figure 2, GENREAD significantly outperform DPR and Google search for under 10 retrieved or generated
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54
53
52
70
51
68
EM
50
66
49
64
49
48
-o- DPR (K- 10) + LLM (10)
DPR only (K docu ments)
62
DPR (K-10) LLM (10)
DPR only IX documents)
48
DPR (K- 10) LLM (10)
DPR documents)
47
60
47
70
20
40
70
100
NQ
Topk documents
TriviaQA
Topk dacuments
WebQ
Topk documents
Figure 3: Combining DPR retrieved documents and large language model (LLM) generated docu- ments can achieve significantly better performance than using DPR retrieved documents only. For fair comparison, instead of adding LLM generated documents to the model, we replace 10 documents retrieved by DPR with 10 documents generated by LLM so the total number of documents is the same. In this experiment, we use FiD-1 (i.e., FiD-large) as the reader model because when the documents scale to more than 20, FiD-xl (i.e., FiD-3B) causes out-of-memory issues on A A10 GPUs.
documents. Compared to different GENREAD variants, including nucleus sampling, human written prompts, and clustering- -haeed prompts, clustering- prompts achieve the best performance. At the same time, we notice that the language model inevitably has the problem that the slope of the curve decreases as the number of generated documents increases. On one hand, this is due to the similarity of token distributions when large language models generate multiple documents. On the other hand, due the shallow interaction characteristics of the dense retrieval model itself, the retrieved documents might not be completely relevant to the given question, so that the increase in recall might come from false positive documents, as also mentioned by Sachan et al. (2022).
As shown in Table 2. we can first observe the FiD model performs the best among all baseline models. Using FiD-xl with only 10 documents achieves comparable performance with using FiD-I with 100 documents. The average gap is less than 1 % on three benchmarks. Compared with both close-book models and Wikipedia-bars based retrieve-then-reaud pipelines, our proposed GENREAD can achieve ththe-ff performance. Furthermore, compared with using sampling methods to generate documents, the clustering- based prompt method can improve the EM score by +2.2 on average. This indicates that the clustering- -asedd prompt method is effectively increasing the knowledge coverage of generated documents, and also leading to better downstream QA performance. We also show that GENREAD can outperform Google search all benchmarks. We observe both method and Google search perform worse than DPR, mainly due to the significant portion of time-dependent questions in the dataset, which is described in the following analysis.
4.2.3 EXPERIMENTAL RESULTS ON OTHER TASKS
We demonstrate the experimental re- sults in Table 3. Under the supervised setting, GENREAD can achieve on par performance on the fact checking task and superior performance on the dialogue system task, indicating that large language model can be seen as strong knowledge generator.
FEVER FM2
WoW
Models
Acc.
Acc.
F1 R-L
RAG (Lewis al., 2020)
86.3
71.1
13.1 1 11.6
FiD (Izacard & Grave, 2021)
90.2
77.6
17.5 16.1
GENREAD (FiD-xl) (sampling)
89.0
76.3
18.9 / 16.7
GENREAD (FiD-xI) (clustering)
89.6
77.8
19.1 / 16.8
t merge two source docs.
91.8
78.9
20.1 / 17.9
Table 3: Supervised performance on fact checking (FEVER
The main reason that GENREAD per- forms worse than the dense retriever for fact checking is that the task provides sufficient semantic information to reach strong performance on this binary decision task. So, there is smaller semantic gap between the given factual statement and contextual documents than that of question and document pairs in open- open-domain QA, which an easier retrieval setting for modern dense retrieval methods that are mainly based on vector similarity.
and FM2) and open- open-domain dialogue system (WoW).
4.3
OBSERVATIONS AND EXPERIMENTAL ANALYSIS
4.3.1
COMPLEMENTARITY OF GENERATED AND RETRIEVED DOCUMENTS
Generated documents can be combined with retrieved documents to outperform both. Even with a very large number of retrieved documents, including few samples of generated knowledge leads to large
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improvements. As shown in Table 2, merging retrieved documents with generated documents achieve state-of-the- performance compared to all baseline methods listed in the table. Specifically, it can improve +5.7 averagely three open- open-domain QA benchmarks compared to DPR alone, and improve +4.4 averagely compared to the large language model alone.
4.3.2 COVERAGE ANALYSIS OVER ALL POSSIBLE ANSWERS
The improvement in open-domain QA performance due to the fact that correct answers are included more frequently in the generated text Recall@K is the most commonly used met- ric in existing works to measure the retrieval performance, which computes the percentage of K retrieved or generated documents that contain any possible answer at least once. than in the retrieved documents. However, as many questions contain multiple correct answers, recall@ K cannot fully reflect the diversity of generated or retrieved documents. Each ques- tion in the WebQ has 2.39 correct answers, 1.79 correct answers NQ and 14.02 (includ- ing all entity alias) in the TriviaQA. NQ and WebQ do not include alias names in the labels.
In this section, we also demonstrate the answer coverage performance of different models Table 5. Answer coverage measures the percentage of the num- ber of answers that are con- tained in the documents over all possible answers. Coverage analysis showed that generated text tends to have lower coverage than retrieved documents because generated documents tends to have little diversity compared to retrieved documents. To improve coverage, propose GENREAD with clustering. where include examples in the prompt from different clusters of the training data to elicit more diverse generations.
NQ
TriviaQA
WebQ
Documents obtained by +
alias
w/o alias
BM25 (Robertson al., 2009)
48.4
17.1
63.8
41.2
Google search engine?
57.9
18.9
72.0
54.2
DPR (Karpukhin al., 2020)
67.9
17.9
67.3
58.8
GENREAD (nucleus sampling)
56.6
19.6
74.5
59.8
GENREAD (10 human prompts)
57.4
20.1
74.8
61.1
GENREAD (clustering prompts)
61.7
20.4
76.5
62.1
Table 4:
Answer coverage (%) over 10 retrieved or generated docu-
ments. Case studies are provided in Tables 16-19 in Appendix.
EPILOGUE
CONCLUSION, In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing the dense retrieval models with large language model generators. We call it generate-then- read, which first prompts a large language model to generate contextual documents, then read the generated document to infer the final answer. Notably, without retrieving any documents, it reaches 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly outperforming the current retrieval-reader model DPR-FiD, as well as other two knowledge-intensive tasks.
LIMITATION AND FUTURE WORK. Despite the strong performance on the presented datasets, our approach is limited in its ability to update knowledge state and adapt to new domains. A major feature of retrieve-then-rede is the ability to swap in new documents when new information learned, such temporally more recent documents, a new downstream task. Our approach relies on a large language model to contain all this knowledge and adding new knowledge would likely require some retraining. Future work will explore how to efficiently incorporate new knowledge into our Sinerate-then-te documents might suffer from hallucination error, resulting in incorrect predictions. We demonstrated case study in Table 15. Consideration in combination with recent approaches (Creswell & Shanahan, 2022) to boost generative faithfulness is also direction worthy of future research.
adding in documents from new domain to quickly adapt to
method. Besides, generated
ACKNOWLEDGEMENTS
This work was supported in part by NSF IIS -IS--11111 19531, 113-3133396, 214-224 CCF-1901059, and ONR N00014-22- 1-2507. Wenhao is supported in part by Bloomberg Data Science Ph.D Fellowship. We are grateful to the reviewers for their insightful suggestions that have improved the quality of our paper. Their dedication ensuring the completeness of our research is greatly appreciated.

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Published as a conference paper at ICLR 2023
ETHICS STATEMENT
Large language models have a wide range of beneficial applications for society, but they also have potentially harmful applications. Previous work has shown various forms of bias, such as racial and gender bias, in large language models like GPT-3, even after explicit efforts to reduce toxic language (Chan, 2022). The importance of addressing these societal harms is acknowledged by OpenAl themselves in their 2020 paper introducing GPT-3 (Brown et al., 2020), which stated "we focus on two primary issues: the potential for deliberate misuse of language models like GPT-3... ... and issues of bias, fairness, and representation within models like GPT-3." page 34.
The goal of this paper is to utilize knowledge stored in the parameters of large language models to answer open-domain questions and solve knowledge-intensive tasks. Unlike retrieve-then-rent where an external corpus can be curated to be trustworthy, the use of a model to generate contextual documents may further permeate existing biases in common models. First, work shows that generated documents suffer from challenges of stale information from outdated documents used for training. Second, we show that generated documents tend to be less diverse, potentially biasing answers towards more common entities and terms from the training data. Finally, we conducted experiments on only three large language models. It is possible that some of our conclusions or observations may not necessarily hold for other models trained with different data or objectives.
Regarding ethical solutions, future work includes (i) further exploring potential bias and intentional or unintentional harm that may result from using generated contextual documents; (ii) better aligning language models with user intent to generate less biased contents and fewer fabricated facts.
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A
APPENDIX
Datasets
Splits
Train
Valid
Test
Test labels
open domain
78,785
8,837
11,313
public
TriviaQA (Joshi et al., 2017)
wikipedia split
7,993
public
WebQ (Berant et al., 2013)
open domain
3,478
300
2,032
public
NQ (Kwiatkowski et al., 2019)
open domain
79,168
8,757
3,610
public
FEVER (Thorne et al., 2018)
kilt challenge
104,966
10,444
10,100
hidden
FM2 (Eisenschlos et al., 2021)
official split 10,149
1169
1380
public
WoW (Dinan et al., 2019)
kilt challenge
63,734
3,054
2,944
hidden
Table 5: Datasets splits and statistics. For FEVER and WoW, labels in the test are hidden, so the model performance should be evaluated at https::///// / //ai///a///// facebook. www./ooos///eeoooooooooooooooooe
A.1 DATASETS AND SPLITS
TRIVIAQA (TQA) (Joshi al., 2017) contains set of trivia questions with answers that were originally scraped from trivia and quiz-league websites.
WEBQUESTIONS (WebQ) (Berant et al., 2013) consists of questions selected using Google Suggest API, where the answers are entities in Freebase.
NATURAL. QUESTIONS (NQ) (Kwiatkowski al., 2019) were mined from real Google search queries and the answers are spans in Wikipedia articles identified by human annotators.
We explore the train / dev / test splits for the open-domain QA setting as used by Izacard & Grave (2021); Karpukhin et al. (2020). For TriviaQA, GPT-3 / GLaM PaLM (Brown et al., 2020; Du al., 2022; Chowdhery al., 2022) evaluate the Wikipedia dev of 7.993 examples, we ran an additional evaluation on that dev set in order to compare with their performance.
FEVER (Thoree et al., 2018) is one of the largest datasets for fact checking that requires retrieving evidence from external corpus to support if statement is supported or refuted.
FOOL ME TWICE (FM2) (Eisenschlos et al., 2021) a challenging fact checking dataset collected by gamification. Players write challenging claims either entailed or refuted by evidence from Wikipedia. They are then tasked to spot the refuted claim among a group.
WIZARD OF WIKIPEDIA (WoW) (Dinan et al., 2019) is an open-domain dialogue task for training agents that can converse knowledgeably about open-domain topics. One speaker the conversation must ground their utterances in a specific knowledge sentence from Wikipedia page.
We use the same train dev / test splits in KILT challenge (Petroni et al., 2021) for the FEVER and WoW datasets. Their test labels are hidden, so the performance can only be evaluated through https: / //ai.//a//// facebook. www/toolllooooooooooo / kilt. For FM2, we use its official dataset splits.
A.2 IMPLEMENTATION DETAILS
We use T5-770M (Raffel et al., 2020) and T5-3B our backbone models to implement FiD (Izacard & Grave, 2021). We use AdamW the optimizer, with 2,000 warm-up steps. We set the dropout probability to 0.1 and weight decay to 0.01. We use one A A100 for running T5-770M and set the batch size of 16. We use 8 A100 for running T5-3B and set the GPU batch as 2, leading to the total batch size as 16. We searched different learning rates, ranging from 5 to 4e-5, and we found 3e-5 to 6e-5 performed the best under the T5-3B setting and 5e-5 to 1e-4 performed the best under the T5-770M setting. We refer to more individual implementation details in Table 6.
We implement other baseline methods by using repositories:
BM25: http://thttto..ccoocccooooaaaaaa
-DPR:
Contriever: https: http:://s/tttttcc///////oa//aaar
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Settings Datasets
NQ
TriviaQA
WebQ
FEVER
FM2
WoW
Peak learning rate
le-4
le-4
le-4
le-4
le-4
5e-5
Total batch size
64
64
64
64
64
16
Total training steps
15,000
10,000
10,000
10,000
10,000
20,000
Best validation steps
6,000
500
8,500
5,000
6,000
20,000
Validation performance
43.27
69.47
60.33
88.97
73.57
18.60
Best validation + test
43.50
70.22
53.33
87.25
74.21
18.49
Peak learning rate Total batch size Total training steps
5e-5
6e-5
3e-5
5e-5
5e-5
3e-5
16
16
16
16
16
20,000
15,000
15,000
15,000
15,000
20,000
Best validation steps
14,000
8,500
11,500
10,000
6,000
16,500
Validation performance
44.83
70.61
61.00
90.53
76.30
19.12
Best validation = test
45.55
71.55
54.36
89.58
77.78
18.87
Table Hyperparaters settings and validation performance for open-domain QA (numbers reported in Table 2), fact checking and dialogue system (numbers reported in Table 3). The upper part numbers are from GENREAD (FiD-I) and the lower part numbers are from GENREAD (FiD-xl).
A.3 REPRODUCIBILITY VIA OPEN SOURCE LARGE LANGUAGE MODELS
We note that reproducing experiments on the OpenAl API, though publicly available, costs money. For this reason, we further add an evaluation on two open-source large lan- guage models OPT (Zhang et al., 2022) and Codex (OpenAI, 2022). As shown in Table 7, OPT performed worse than InstrucIGPT, but still achieved comparable performance with DPR; OpenAI Codex achieved the best performance on both TriviaQA and WebQ.
Documents obtained by +
TriviaQA
WebQ
DPR (Karpukhin et al., 2020)
66.3
50.8
OPT (Zhang et al., 2022)
62.1
51.8
InstructGPT (Ouyang et al., 2022)
71.3
54.5
Codex (OpenAI, 2022)
72.6
55.4
Table 7: Exact match (EM) score with using DPR and different open-source large language models such as OPT and Codex to generate contextual documents.
A.4 SCALING WITH NUMBER OF LARGE LANGUAGE MODEL PARAMETERS
Figure 4 shows the scaling of performance with In- structGPT generator parameters, including Ada- 150M, Babbage-1.3B, Curie-6.77 7B and Davinci-175BB We note that for both FiD and our GENREAD we use the FiD-xl with input documents either retrieved from Wikipedia generated by InstructGPT. The perfor-
75
65
EM score
(52.6)
DPR-FID (50.8)
(44.8)
mance both TriviaQA and WebQ continues to im- prove as the generator model parameters increase, does the slope. Only with the largest size InstructGPTT GENREAD outperform the DPR-FiD. This indi- cates using large language model to generate contextual documents an "emergent ability" of scaling, which is not present in smaller models but is only present in larger language models (Wei al., 2022a).
(40.3)
(29.8)
TrivieQA
(27.6)
WebQ
25
1e8
1e9
1e10
lel1
GPT-3 model parameters
Figure Model performance with different size of InstructGPT as context generators.
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A.5 READABILITY ANALYSIS OF RETRIEVED AND GENERATED DOCUMENTS
After we manually compare some re- trieved documents from DPR and gen- erated documents from InstructGPT, we observe that the readability of dif- ferent documents, when they contain the correct answer string, is different. In other words, documents containing answers might also contain noisy in-
Documents obtained by
NQ
TriviaQA
WebQ
DPR (Karpukhin et al., 2020)
63.1
80.2
63.3
GENREAD (nucleus sampling)
58.7
83.7
63.8
GENREAD (clustering prompts)
64.0
86.8
66.7
Table 8: Readability study on retrieved documents and gen- erated documents. See detailed analysis in $A.5. formation that is irrelevant to the question, which could affect both the model and human reading.
In order to further validate the readability of retrieved documents and generated documents, we extracted subset of data examples from NQ. TriviaQA and WebQ datasets, in which both retrieved and generated documents contain the correct answer. As shown in Table 8, when both retrieved and generated documents contain the correct answer, the FiD reader can produce more correct answers when reading the generated documents from large language models (e.g., InstructGPT).
We also provide some case studies in Tables 16-19. For example, in Table 18, the question is "What city was Zeus the patron god of?". The first document retrieved by DPR "Like the other Panhellenic Games, the ancient Olympic Games were religious festival, held the sanctuary of Zeus at Olympia.". Although it contains the correct answer, it hard to infer the answer "Olympia" from On the contrary. InstructGPT generates the document "Zeus was the patron god of the city of Olympia, which was located in the northwestern Peloponnese region of Greece. Olympia was the site of the Olympic Games, held every four years in honor of Zeus.", which is much easier read.
A.6 ADDITIONAL NUMBERS FOR TABLES IN THE MAIN PAPER
Table 9 contains additional evaluation results for Table 1. It demonstrates zero-shot open open-domain QA performance, compared to recent large language model.
Figure 5 contains additional retrieval performance evaluation for Figure 3 of experiments combining DPR retrieved documents and large language model generated document.
Table 10 contains additional retrieval performance evaluated by Recall@K K of baselines and different GENREAD variants. Some numbers in the table overlaps with those in Figure 2.
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total parameters
NQ
TriviaQA
WebQ
Models
open test
open test
wiki split
open test
GPT-3 (Brown et al., 2020)
175B
14.6
49.2
64.3
14.4
Gopher (Rae et al., 2021)
280B
10.1
43.5
52.8
FLAN (Wei et al., 2021)
137B
20.7
56.7
68.1
GLaM (Du et al., 2022)
64B
21.5
68.0
19.0
Chinchilla (Hoffmann et al., 2022)
70B
16.6
55.4
67.0
PaLM (Chowdhery al., 2022)
540B
21.2
76.9
10.9
InstructGPT (Ouyang et al., 2022)
175B
19.5
57.4
68.5
19.9
GENREAD (InstructGPT)
175B
28.2
59.3
70.3
24.8
Table 9: Additional numbers for Table 1. Zero-shot open-domain QA performance, compared to recent large language models. All models in the table do not leverage any external corpus for document retrieval. Compared to InstructGPT, by +6.9 on average. GENREAD can achieve state-of-the-an performance on open test sets.
proposed GENREAD improve the EM score
90
90
87
Recallgk
86
Recallgk
86
Recallggk
83
84
81
82
82
79
80
80
documents)
77
documents) DPR Ik- LLM (10)
-a- DRR (k-10) LLM (10)
DPR (k-10) LLM (10)
78
75
Topk documents
Topk documents
Topk documents
(a) NQ-test (retrieval)
(b) TriviaQA-test (retrieval)
(c) WebQ-test (retrieval)
Figure 5: Additional retrieval performance evaluation for Figure 3 of experiments combining DPR retrieved documents large language model generated documents. Merging documents from two sources achieved significantly better performance than using DPR retrieved documents only.
TriviaQA
WebQ R@
NQ
Models
R@I 1
R 10
R@20 R@I
R@20 R@I
R@ I0
R@20
BM25 (Robertson et al., 2009)
46.2
71.7
76.4
19.1
51.8
62.6
22.8
55.6
63.9
Contriever (Izacard et al., 2022a)
34.0
67.9
74.3
18.2
55.7
65.7
18.8
54.8
65.1
DPR (Karpukhin al., 2020)
53.2
75.3
79.0
45.4
70.5
75.2
44.6
74.5
79.5
Google Search engine API
50.0
78.8
40.0
65.6
35.5
67.5
GENREAD (nucleus, p=5
65.1
81.6
83.8
49.5
71.4
74.4
40.1
66.2
70.6
GENREAD (10 human prompts)
65.5
81.8
50.8
72.7
40.5
66.9
GENREAD (clustering prompts)
69.6
82,9
85.1
54.5
73.3
75.4
48.0
70.9
74.5
Table 10:
Retrieval performance evaluated by Recall @ K baseline and different GENREAD variants. Some numbers in the table overlaps with those in Figure 2. The table aims to show the performance of more methods, and to provide accurate recall numbers for future research comparisons.
TriviaQA
WebQ
NQ
R@10
EM
R@
EM
R@10
EM
Sample 5 documents from entire data
81.5
70.9
72.5
53.3
69.2
44.2
Sample 5 documents from each cluster
82.7
71.8
73.3
54.4
70.6
45.3
Table 11: Ablation study on the strategy of sampling documents as in-context demonstrations.
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A.7 DISCUSSION ON INFERENCE COST OF DPR AND INSTRUCTGPT
We now compare the costs of using DPR and InstructGPT to retrieve or generate contextual documents. We consider DPR using the BERT-base (Devlin et al., 2019) version with 110M parameters and InstructGPT using its largest version with 175B parameters. For simplicity, we use the FLOPs-per- token estimates for Transformer- baased language models, which introduced by Kaplan et al. (2020). It should be noted that FLOPs are not a direct measure of real-world computing costs, as latency, power consumption, and other costs can vary widely based on other factors (Liu al., 2022a).
For the DPR model, all Wikipedia documents (around 21M) only need to be encoded once. Therefore, as the number of input questions increases, the marginal computational cost gradually decreases. For fair comparison, we first use DPR to encode all 21M Wikipedia documents once. Encoding all Wikipedia documents requires 110e6 (BERT-base parameters) x 21e6 (total number of documents) x 100 (tokens per document) 2.3 FLOPs. When the embedding of all candidate documents produced, retrieving documents for a given question requires 110e6 (BERT-base parameters) x 20 (tokens per question) +21e6 (total number of documents) x (768 + 768 1) 3.2e10 FLOPs.
For InstructGPT, it requires 175c9 (InstructGPT parameters) x 10 (number of documents) x 55 (generated tokens per document) 9.6c13 FLOPs generate 10 documents for given question.
Therefore, the equation for the total cost DPR--cos to retrieve 10 documents using DPR versus the number of input questions X is: YDPR cost = 3.. + 2.3e17. Besides, the equation for the total cost YGPT3 to generate 10 documents using InstructGPT versus the number of input questions X is: YaPt3 9.6. When YDP cost YGPT3-catt X z 2473. In conclusion, if the number of input questions is less than 2473, the total cost of InstrucIGPT is lower than the DPR; if the number of input questions greater than 2473, the total cost of InstructGPT exceeds the DPR.
A.8 ERROR ANALYSIS AND CASE STUDIES ON THE NQ DATASET
As stated in Zhang& & Choi (2021), NQ contains significant proportion, roughly 16.5% of questions that have time-dependent answers. Similarly, Izacard et al. (2022b) observed using the latest version of Wikipedia (12 / 2021) could lead to 4.4 drops of the EM score, compared to the Wikipedia version (12 / 2018) that the NQ questions are created from. We provide case studies in Table 13 in Appendix.
We did case studies of 100 examples from the NQ dataset. The results shown in Table 12. Among these 100 examples, we found that 29 examples have data collection and annotation mistakes, mainly including the temporal question issue (13 / 29) and the incomplete answer issue (16 / 29). A typical temporal-dependeen question that no specific time condition is provided. For example, "Who won the MVP for the National League?" could have different answers in different years. In 2017, the MVP is Giancarlo Stanton, and in 2018, the MVP is Christian Yelich. Besides, some answer labels provided in the NQ dataset are not complete. For example, person names in the NQ dataset usually consist of first, middle, and last names, but most names in the generated documents are first and last names. For the question "who played lionel in as time goes by?", the labeled answer "Geoffrey Dyson Palmer". DPR-FiD produces "Geoffrey Dyson Palmer" but GENRFAD produces "Geoffrey Palmer", both of which should be considered correct. More examples are provided in Table 14.
Besides, GENREAD produced correct answers for 49 questions. Among the 22 incorrect predictions, 12 of them could be classified as retrieval errors (i.e., step-I error) and 12 as reading errors (i.e., slep-II error). In all cases of retrieval errors, none of the generated documents contain the correct answer. In all cases of reading errors, at least one generated document contains the correct answer but the reader model failed to infer the correct answer from the documents..
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Query: Who gol the first Nobel Prize in Physics?
The first Nobel Prize in Physies was awarded in 1901 to Wilhelm Conrad Röntgen for his discovery of the remarkable rays subsequently named after him.
Document:
Correct prediction (49%)
Predicted answer: Wilhelm Conrad Röntgen
Correct answer: Wilhelm Conrad Röntgen
Query: Who died the first episode of Stranger Things? Document: In the first episode of Stranger Things, the character Will Byers dies. He is killed by Demogorgon, a monster from the Upside Down.
Hallucinations (8%)
Good Q&A (71%)
Predicted answer: Will Byers Correct answer: Benny Hammond
Wrong retrieval (12%)
Query: When was coffee first made into drink?
Document: The history of coffee goes back to the 10th century, with coffee trees native to Ethiopia. The earliest substantiated evidence of either coffee drinking know-
No hit answers (4%)
ledge of coffee tree is from sixth century AD in Ethiopia.
Predicted answer: the 10th century
Correct answer: the 15th century
Query: When is the fourth movie the Divergent series coming out?
Wrong reading (10%)
Document: The fourth movie in the Divergent series originally scheduled to be released in June 2017, but was delayed indefinitely.
Predicted answer: June 2017
Correct answer: never made
Query: Who won MVP the National I. League? Document: In 2017, the National League MVP was won by Giancarlo Stanton of the Miami Marlins. In 2018, the National League MVP was won by Christian Yelich of the Milwaukee Brewers.
Temporal questions (13%)
Predicted answer: Christian Yelich
Bad Q&A (29%)
Correct answer: Giancarlo Stanton
Query: Where do the greasers live the Outsiders?
Document: The Outsiders is novel by S.F. Hinton. It is about gang of greasers in Oklahoma in the 1960s. The National League MVP was won by Christian Yelich of the Milwaukee Brewers.
Incomplete answers (16%)
Predicted answer: Oklahoma
Correct answer: Tulsa, Oklahoma
Table 12: Case study on 100 GENREAD predictions in the NQ dataset. Among 100 examples, there 49 correct predictions, i.e., EM = 49%. We further categorized 51 incorrect predictions of our GENREAD, including errors caused by data collection and annotation, and errors caused by model prediction. In addition, we provide more case studies in Tables 13-15 (Table for the temporal question issue; Table 14 for the incomplete answer issue; Table 15 for the hallucination issue).
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Original question
NQ labels
Correct labels
Q: When the last time philadelphia won the superbow!? Super Bowl LII: 2017 2018; February 4, 2018
DPR: 2017 x; Google search: 2018 w:: GENREAD: February 4, 2018 d
Q: Who has the most big ten championships in football?
Michigan
Ohio State
DPR: Michigan x; Google search: Ohio State v: GENREAD: Ohio State /
Pittsburgh Steelers;
Q: Who has the most super bowls in nfl history?
Pittsburgh Steelers
New England Patriots
DPR: Pittsburgh Steelers v/: Google search: New England Patriots v: GENREAD: : New England Patriots v/
Q: How many casinos are atlantic city new jersey?
11; eleven
9; nine
DPR: eleven x; Google search: nine s/; GENREAD: nine V/
Q: When did the not go the olympics?
1980
1980; 1984
DPR: 1980 v: Google search: 1980 and 1984 v: GENREAD: 1984 V
Q: Largest cities in the world by population?
Beijing
Tokyo
DPR: Beijing x: Google search: Tokyo v:: GENREAD: : Tokyo
Q: Who has most followers on instagram in world?
Selena Gomez
Cristiano Ronaldo
DPR: Instagram x; Google search: Cristiano Ronaldo v: GENREAD: Cristiano Ronaldo
Q: Who is the no. ranked tennis player in the world?
Rafael Nadal
Novak Djokovic
DPR: Rafael Nadal x: Google search: Novak Djokovic v: GENREAD: : Novak Djokovic
Table 13: Case studies of temporality issues of the NQ dataset. All these questions are drawn from Zhang & Choi (2021), which contains subset of NQ data examples with time-dependent questions.
Original question
DPR-FiD predictions
GENREAD predictions
Q: Who played lionel in as time goes by?
Geoffrey Dyson Palmer
Geoffrey Palmer
Explanation: The labeled answer is "Geoffrey Dyson Palmer", however, "Geoffrey Palmer" is also correct. DPR retrieved documents: Geoffrey Dyson Palmer, (born 4 June 1927) is an English actor known for his roles in British television sitcoms playing Jimmy Anderson in "The Fall and Rise of Reginald Perrin", Ben Parkinson in "Butterfies" and Lionel Hardcastle in "As Time Goes By". His film appearances include "A Fish Called Wanda", "The Madness of King George", "Mrs. Brown", and "Tomorrow Never Dies". GPT generated documents: As Time Goes By is a British sitcom that aired on BBC One from 1992 to 2005. The show starred Geoffrey Palmer and Judi Dench as Lionel and Jean Pargetter, middle-aged couple who reunite after many years apart. Lionel was played by Palmer, who also writer the show.
Q: How many cracker barrels the united states? Explanation: The labled answer is "639" or "over 600", so "over 630" is also reasonable answer.
645
over 630
Q: Where do the greasers live in the outsiders?
Tulsa, Oklahoma
Oklahoma
Explanation: The labled answer is "Tulsa, Oklahoma", but "Oklahoma" is also correct answer.
DPR retrieved documents: The movie received mostly positive reviews from critics, and performed well at the box office, grossing 33 million on a 10 million budget. In Tulsa, Oklahoma, greasers are a gang of tough, low-income working-class teens. They include Ponyboy Curtis and his two older brothers, Sodapop and Darrel, well as Johnny.
GPT generated documents: The Outsiders is novel by S.E. Hinton. It is about a gang of greasers in Oklahoma in the 1960s. The greasers are from the poor side of town and constantly in trouble with the law.
Q: Where are unipolar neurons found in spinal cord? the granule region Explanation: The labled answer is "the distal dorsal root". but the output "dorsal root ganglia" is the same.
dorsal root ganglia
Table 14:
Case studies of the incompleee answers issue of the NQ dataset. Since the labels in NQ are spans identified from Wikipedia passages, it is easier for DPR-FiD to predict correct answers.
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Question:
Who wrote the first declaration of human rights? Answer: Cyrus Cylinder
Generated document:
The first declaration of human rights was the Virginia Declaration of Rights, which was written by George Mason in 1776.
Retrieved document: John Peters Humphrey, OC (April 30, 1905 2013 March 14, 1995) was Canadian legal scholar, jurist, and human rights advocate. He is most famous as the author of the first draft of the Universal Declaration of Human Rights. ... (Wikipedia page: John Peters Humphrey)
Golden Wikipedia evidence:
George Mason's ' writings, including substantial portions of the Fairfax Resolves of 1774, the Virginia Declaration of Rights of 1776, and his Objections this Constitution of Government (1787) opposing ratification, have exercised a significant influence on American political thought and events. Mason prepared the first draft of the Virginia Declaration of Rights in 1776, and his words formed much of the text adopted by the final Revolutionary Virginia Convention. (Wikipedia page: George Mason)
The Cyrus Cylinder was dubbed the "first declaration of human rights" by the pre- Revolution Iranian government, reading prominently advanced by Shah Mohammed Reza Pahlavi, in 1967 book, The White Revolution of Iran. (Wikipedia page: Cyrus Cylinder)
Hallucination: In GPT generated documents, first declaration of human rights was considered as Virginia Declaration of Rights, which is factually incorrect. This error might occur because Wikipedia page of George Mason contains similar descriptions, causing the model to confuse their meanings. DPR-FiD John Peters Humphrey: GENREAD George Mason
Model predictions:
Question: Who died in the first episode of stranger things Answer: Benny or Barb
Generated document: In the first episode of Stranger Things, the character Will Byers dies. He is killed by the Demogorgon, a monster from the Upside Down. Retrieved document:
was the character of Barb, the nerdy friend and classmate of Nancy who is taken and killed by the monster early in the season. According actress Shannon Purser, Barb Wasn't supposed to be a big deal and the Duffer Brothers had not gone into great detail about the character since the focus was on finding Will. (Wikipedia page: Stranger Things)
Golden Wikipedia evidence:
One area of focus from these fans was the character of Barb, the nerdy friend and classmate of Nancy who is taken and killed by the monster early in the season. According to actress Shannon Purser, Barb wasn't supposed to be a big dealy and the Duffer Brothers had not gone into great detail about the character since the focus was finding Will. ... (Wikipedia page: Stranger Things)
The owner, Benny, learns from tattoo of "011" on her arm that her name is Eleven. Brenner, monitoring the phone lines, sends agents to the diner after Benny calls social services. The agents kill Benny, but Eleven manages to escape using telekinetic abilities. Joyce's phone short circuits after receiving mysterious phone call that she believes is from Will. While searching for Will in the woods, Mike, Dustin and Lucas come across Hleven. (Wikipedia page: Stranger Things) Hallucination: In GPT generated documents, Will Byers said to be dead, which is actually incorrect. Will Byers is actually fake deaths in season I and he is still alive at the end of the season.
Model predictions:
DPR-FiD Barb; GENREAD Will
Question: Who played the judge in good will hunting? Answer: James P. Flynn
Generated document:
The character of the Judge in Good Will Hunting was played by actor Robert Sean Leonard. Leonard is best known for his role as Dr. James Wilson in the television series House, Retrieved document: Janitor could show such facility with matrices applied this way. Good Will Hunting Good Will Hunting is 1997 American drama film, directed by Gus Van Sant, and starring Robin Williams, Matt Damon, Ben Affleck, ... (Wikipedia page: Good Will Hunting) Golden Wikipedia evidence:
James P. Flynn appeared in many films shot in the New England area. Flynn was cast as judge in the Boston- based film Good Will Hunting in 1997. Later, acted in the 1999 film The Cider House Rules and What's 's the Worst That Could Happen? in 2001. (Wikipedia page: James P. Flynn)
Cast: James P. Flynn as Judge George H. Malone (Wikipedia page: Good Will Hunting) Hallucination: In GPT generated documents, Robert Sean Leonard is considered as a judge in Good Will Hunting, which is factually incorrect. This error leads to wrong answer prediction.
Model predictions: DPR-FiD Stellan Skarsgrd; GENREAD Alan Arkin
Table 15:
Case studies of hallucination errors in InstructGPT generated documents. The documents contain contents that contradict to the facts and world knowledge, resulting in wrong predictions.
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Query: What type of music did Mozart composed? Answers: opera, classical music, art song, chamber music, ballet
DPR #1: The following is a list of surviving works that Mozart composed for performance at gatherings Masons. The story and music of his opera "The Magic Flute" also considered to have strong Masonic influences. The following is partial list of family members, patrons, and colleagues who were Masons. Mozart's grandfather Johann Georg. a bookbinder, was raised among the extended Mozart family in Augsburg. in the house of Johann's 's own grandfather David Mozart. David and his children were distinguished architects and master ... (Wikipedia id: 10988165 "Mozart and Freemasonry")
DPR #2: Progressively, and large part at the hands Mozart himself, the contrapuntal complexities of the late Baroque emerged once more, moderated and disciplined by new forms, and adapted to a new aesthetic and social milieu. Mozart was versatile composer, and wrote in every major genre, including symphony, opera, the solo concerto, chamber music including string quartet and string quintet, and the piano sonata. These forms were not new, but Mozart advanced their technical sophistication and emotional reach. He almost hugle-handedly developed and popularized the Classical piano concerto. He wrote great deal... ... (Wikipedia id: 45 1968 "Wolfgang Amadeus Mozart")
Among top-10 retrieved documents: opera: 2 times chamber music: time
prolific composer and wrote in many different genres. He composed symphonies. operas, concertos, chamber music, and more. Many of his works are considered some of the greatest ever written in those genres.
GPT nucleus #1: Mozart
GPT nucleus #2: Mozart composed a variety of music, including operas, symphonics, chamber music, concertos, and sonatas. His early works were influenced by the music of Haydn and Bach, and his later works show the influence of the French and Italian composers of his time. Mozart's music characterized by its beauty, elegance, and precision, and is considered one the greatest composers of all time.
GPT nucleus #3: Mozart composed a wide variety of music, including operas, symphonies, concertos, solo pieces, and chamber music. He also wrote a number of religious works, including masses and motets. Among top-10 generated documents: chamber music: 9 times
GPT cluster #1: Mozart found work in Vienna as a piano teacher and composer for opera and other works. There he met fellow Mason, Joseph Haydn, who was to become an important mentor. Mozart married Constanze Weber in 1782; their marriage is thought to have been happy. in contrast his earlier unhappy arranged marriage to his cousin, Maria Anna NannerlMozart, Mozart composed more than 600 works, including works widely.
GPT cluster #2: Mozart was a prolific composer, and wrote in a wide variety of genres. He composed over 600 works, including operas, symphonies, concertos, solo works, and chamber music. His operas include The Marriage of Figaro, Don Giovanni, and Cosi fan tutte. His symphonies include the well-known Symphony No. in G minor, and his concertos include the Piano Concerto No. 21 in C major, known as the Élvira Madiganconcerto.
GPT cluster #3: Mozart was prolific composer who wrote music in a wide variety of genres. He is best known for his work in the classical genre, which includes such works his Symphony No. 40 in G minor and his piano concertos. He also wrote operas, including The Marriage of Figaro and The Magic Flute. In addition to his work in classical music, Mozart also composed works in the popular genre, such as his Eine kleine Nachtmusik.
Among top-10 10 generated documents: opera: 2 times chamber music: 7 times classical music: time
Table 16: Case study of retrieved documents and generated documents from language models (1/4).
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Query: What does Monsanto own?
Answers: agricultural chemicals, seed, agriculture, chemical industry
DPR #1: In September 2016, Monsanto agreed to be acquired by Bayer for US$66 billion. In effort to receive regulatory clearance for deal, Bayer announced the sale of significant portions its current agriculture businesses, including its seed and herbicide businesses, to BASF. The deal was approved by the European Union on March 21, 2018. and approved in the United States May 29, 2018. The sale closed on June 2018; Bayer announced its intent discontinue the Monsanto name, with the combined company operating solely under the Bayer brand... (Wikipedia id: 935 126 "Monsanto")
DPR #2: Behind all is of course an evil corporation: Monsanto. GMO conspiracy theories GMO conspiracy theories are conspiracy theories related to the production and sale of genetically modified crops and genetically modified food (also referred
genetically modified organisms "GMOs"). These conspiracy theories include claims that agribusinesses, especially Monsanto, have suppressed data showing that GMOs cause harm, deliberately cause food shortages to promote the use of GM food, or have co-opted government agencies... ... (Wikipedia id: 19304897 "GMO conspiracy theories")) Among top-10 retrieved documents: agriculture: time seed: times
GPT nucleus #1: Monsanto is an American agricultural biotechnology corporation founded in 1901. It is headquartered in Creve Coeur, Missouri. Monsanto is leading producer of genetically engineered seed and Roundup. a glyphosate-base based herbicide.
GPT nucleus #2: Monsanto is multinational agrochemical and agricultural biotcchnology corporation hcadquartered in Creve Cocur, Missouri. It onc of the world's leading producers of Roundup, glyphosate- based herbieide. The company also manufactures genetically engineered seed and Roundup Ready crops, including corn, soybeans, and cotton. As of 2018, Monsanto is world's largest producer of glyphosate. In 2018, Bayer acquired Monsanto for $63 billion.
Among top-10 generated documents: seed: 6 times
GPT cluster #1: Monsanto Company is an American multinational agrochemical and agricultural biotech- nology corporation headquartered in Creve Cocur, St. Louis, Missouri. It is leading producer of genetically engineered (GE) seed and of the herbicide glyphosate, which it markets under the Roundup brand. Founded in 1901 by John Francis Queeny. Monsanto spent most of 20th century as a chemical company, becoming one of the world's leading manufacturers of plastics, resins, and other chemicals.
GPT cluster #2: Monsanto is a biotechnology and agriculture company that specializes in genetically modified organisms (GMOs). The company owns a number of patents on GMOs, and it produces a range of products that contain GMOs, including herbieides, pesticides, and seeds.
GPT cluster #3: Monsanto is a multinational agricultural biotechnology corporation. world's leading producers genetically engineered seed and Roundup, a glyphosate- herbicide. The company also manufactures other agricultural chemicals, such as insecticides and fungicides. Among top-10 generated documents: agriculture: times seed: 5 times agricultural chemicals: time
of the
Table 17: Case study of retrieved documents and generated documents from language models (2/4).
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Query: What city was Zeus the patron god of?
Answers: Olympia, Elis
DPR #1: Many were specific only particular deity or city-state. For example, the festival of Lykaia was celebrated Arcadia Greece, which was dedicated the pastoral god Pan. Like other Panhellenic Games, the ancient Olympic Games were religious festival, held at the sanctuary of 7eus at Olympia. Other festivals centred on Greek theatre, of which the Dionysia in Athens was the most important. More typical festivals featured a procession, large sacrifices and feast cat the offerings, and many included entertainments and customs such as visiting friends, wearing fancy dress and unusual behaviour in the (Wikipedia id: 1945872 "Ancient Greek religion")
DPR #2: Theatre of Dionysus The T'eetre of Dionysus Eleuthereus is major theatre in Athens, considered to be the world's first theatre. built the foot of the Athenian Acropolis. Dedicated Dionysus, the god plays and wine (among other things). the theatre could seat as many as 17,000 people with excellent acoustics, making an ideal location for ancient Athens' biggest theatrical celebration, the Dionysia. It was the first theatre ever built, cut into the southern cliff face of the Acropolis, and supposedly the hirthplace of Greek tragedy. The remains of restored and redesigned Roman version still (Wikipedia id: 3732515 "Theatre of Dionysus") Among top-10 retrieved documents:
Olympia:
time
GPT nucleus #1: 7eus was the patron god the city Olympia Greece. GPT nucleus #2: The city of Olympia Greece was the site the ancient Olympics, and it was also the home of Zeus, the patron god of the Olympics.
Among top-10 generated documents:
Olympia: 6 times
GPT cluster #1: Zeus was the patron god of the city of Olympia, which was located in the northwestern Peloponnese region of Greece. Olympia was the site of the Olympic Games, which were held every four ycars in honor of Zeus.
GPT cluster #2: Zeus was the patron god of the city of Olympia. Olympia was sanctuary of ancient Greece Elis on the Peloponnesos peninsula. south-western western Greece. The sanctuary was known the Panhellenic Sanctuary Zeus, and the altar of Zeus was located in the Temple of Zeus. The Olympic Games were held in Olympia every four years from 776 BC to 393 AD.
GPT cluster #3: Zeus was the patron god of the city of Olympia.
Among top-10 10 generated documents: Olympia: 9 times Elis: time
Table 18: Case study of retrieved documents and generated documents from language models (3/4).
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Query: What is the collection of the districts to the cast of the Jordan River?
Answers: Jordan, Golan Heights
Water politics in the Jordan River basin Water polities in the Jordan River basin refers to political issues of water within the Jordan River drainage basin, including competing claims and water usage, and issues of riparian rights of surface water along transnational rivers, as well as availahility and usage of ground water. Water resources in the region are scarce, and these issues directly affect the five political subdivisions (Israel, the West Bank, Lehanon, Syria and Jordan) located within and bordering the basin, which were created since the collapse. , during World War of the former single controlling entity, the ... (Wikipedia id: 12822413 "Water politics in the Jordan River basin")
DPR #1:
DPR #2: Jordan's 's western border follows the bottom of the rift. Although an earthquake proneer-reeeee region, no severe shocks had been recorded for several centuries, By far the greatest part of the East Bank is desert, displaying the land forms and other features associated with great aridity. Most of this land is part of the Syrian Desert northern Arabian Desert. There are broad expanses of sand and dunes, particularly in the south and southeast, together with salt flats. Occasional jumbles of sandstone hills or low mountains support only meager and stunted vegetation that (Wikipedia id: 210052 "Geography of Jordan")) Among top-10 retrieved documents: Jordan: times
GPT nucleus #1: The collection of the districts to the east of the Jordan River is known as the West Bank. The West Bank is landlocked territory situated in Western Asia. It is hordered by Jordan to the east, Egypt to the southwest, and Israel to west and north.
GPT nucleus #2: The collection of the districts to east the Jordan River the West Bank. The West Bank is landlocked territory near the Mediterranean coast of Western Asia, bordered by Lebanon to the north, Syria to the northcast, Jordan to the cast, and Egypt to the southwest. is the smaller of two political entities that comprise the Palestinian territories, the other being the Gaza Strip.
Among top-10 generated documents: Jordan: 10 times
GPT cluster #1: The collection of the districts the east of the Jordan River is known the West Bank. The West Bank a landlocked territory situated in Western Asia. It is bordered by Jordan to east, Egypt to the southwest, and Isracl to the west and north.
GPT cluster #2: The Jordan River is a major river in the Middle East, the source of which is in the northern part of Israel. The river flows southward through Israel and then Jordan, emptying into the Dead Sca. East of the river is the collection of districts known as the East Bank.
GPT cluster #3: There is no single answer this question the east bank of the Jordan River is home to a numher of different districts and regions, each with its own unique history, culture, and eustoms. However, some of the more well-known districts on the east bank include the West Bank, the Gaza Strip. and the Golan Heights.
Among 10p-10 generated documents: Jordan: 10 times Golan Heights: 2 times
Table 19: Case study of retrieved documents and generated documents from language models (4/4).
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Published as a conference paper at ICLR 2023
B PROMPTS CHOICES
B.1 ZERO-SHOT LEARNING PROMPTS (FOR TABLE 1)
B.1.1 PROMPTS FOR "INSTRUCTGPT (NO DOCS.)"
We observed the prompts (i.c., "Q: {query) n'"") used in GPT-3 paper (Brown et al., 2020) perform poorly its text-davinci-002 version. Therefore, experimented with multiple prompts and found the following two prompts work best on open-domain QA datasets.
-(1) "[query] } \n' n'TnThe answer is" (no space between {query) and \m)
- -2) "[query] \n)n The answer is" (performance reported in Table 1)
For fact checking and dialogue system, used the following prompts.
Fact Checking "[claim} \,n)n Is the claim true or false?"
Open-domain Dialogue System "(query) An/nn
B.1.2 PROMPTS FOR BACKGROUND GENERATION (STEP-1)
Open- Question Answering "Generate background document from Wikipedia to answer the given question. \n'nn {query) In|n"
Fact checking "Generate a background document from Wikipedia to support or refute the statement. Anin Statement: {claim) 'nnnn"
Open-domain Dialogue System "Generate background document from Wikipedia to answer the given question. (n)n (utterance) \n'nn
B.1.3 PROMPTS FOR READING COMPREHENSION (STEP-2)
We collected the prompt from P3 (Bach al., 2022), which includes 2,000 open-source prompts for roughly 170 datasets. For zero-shot QA, we experimented with three different reading comprehension prompts. We reported the performance for each prompt in Table 20.
- -(1) "Refer to the passage below and answer the following question with just a few words. Passage: (background} \n),n Question: {query] \n',n The answer is"
(2) "Passage: [background} \n)in Question: {query] \nnn Referring to the passage above, the correct answer (just one entity) the given question is"
(3) "Refer to the passage below and answer the following question with just one entity, \n) Passage: background (nn,n Question: query in),n The answer is"
For fact checking and dialogue system, we chose the simplest prompt from P3.
Fact Checking " "[background} \n' claim: {claim} \n),n Is the claim true or false?"
Open-domain Dialogue System "[background) \,n utterance \nnnn"
B.2 HUMAN PROMPT ANNOTATIONS (FOR SECTION 3.2.1)
In order to get a better prompt for large language models to generate better contextual documents, we asked 30 students in the computer science department to write different prompts. We first constructed a small validation set with 200 examples by combining 50 random question-answe pairs from NQ. 100 random pairs from TriviaQA and 50 random pairs from WebQ. When an annotator wrote down prompt, our system can immediately evaluate the prompt by using the validation set and return the performance to the annotator. Then, the annotator can modify the previous prompt until the recall performance reaches a threshold, which is set as 50 in our experiments. Finally, we got 29 prompts from human annotators due to two of them the same. We used the top-10 prompts (shown in Table 21 and Table 22) in the human prompt setting, as described in $3.2.1.
26

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Published as a conference paper at ICLR 2023
NQ
TriviaQA
WebQ
Models
22)
(3)
avg.
(3) avg.
avg.
*with retriever, AND directly trained on these datasets
DPR + I
28.8 28.5 29.9 29.1 56.0 50.1 55.3 53.8
18.9
21.7
20.1
20.2
wwtt retriever, BUT NOT trained on these datasets
BM25 InstructGPT
20.1 18.4 20.5 19.7 54.2
49.0
53.4
52.2
14.9
16.6
16.0
15.8
Contriever + InstructGPT
18.3
16.5 19.1 18.0
53.1
48.5
52.4
51.3
14.9
18.2
16.8
16.6
Google + InstructGPT
29.1 29.3 27.8 28.8 60.3 57.5
58.7
58.8
19.5
21.8
19.9
20.4
"without retriever, and not using external documents
GENREAD (InstructGPT) 27.0 28.7 28.2 28.0 58.5 59.3
59.3
59.0
22.7
26.4
24.8
24.6
Table 20: Zero-shot QA performance under different prompts. The prompts are listed BB.
No.
Prompts
Validation
#1
Generate background document from Wikipedia answer the given question.
66.0
#2
Provide background document from Wikipedia to answer the given question.
65.0
#3
Generate a background document from web to answer the given question.
64.0
Generate a Wikipedia document support given question.
63.5
#5
Provide background document for the given question.
63.0
#6
Prepare background document support the given question.
63.0
#7
To support the given question, prepare background document.
62.5
Create a background document that supports the given question.
61.5
Retrieve a document from Wikipedia answer the given question.
60.5
#10
Retrieve Wikipedia article to address the posed question.
59.5
Table 21: Top- 10 human prompts, evaluated on merged validation set of NQ. TriviaQA and WebQ.
Prompt No.
Validation
NQ
WebQ
TriviaQA
Avg.
#1 (Generate
66.0
45.9
51.9
68.7
55.5
#2 (Provide ...)
65.0
43.9
51.0
68.3
54.4
#3 (Generate ...)
64.0
44.0
50.6
67.7
54.2
#4 (Generate
63.5
43.2
51.2
67.5
54.0
#5 (Provide ...)
63.0
43.6
50.3
67.9
54.0
#6 (Prepare-. ...)
63.0
43.5
50.5
67.7
54.0
#7 (To support... ....
62.5
43.5
50.3
67.5
53.8
#8 (Create ...)
61.5
42.7
50.2
66.8
53.3
#9 (Retrieve.... ...)
60.5
41.6
49.0
68.2
53.0
#10 (Retrieve.... ...)
59.5
40.7
49.5
67.7
52.7
Table 22: Performance on NQ, TriviaQA and WebQ test sets of 10p-10 human prompts.
27


Reviews:
[{"review_id": "Pe04pT7ANB6", "reviewer": "Reviewer_2jcY", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes the generate-then-read (GENREAD) approach to solve knowledge-intensive tasks. Compared to the previous retrieve-then-read approach, the proposed method first generates the contextual documents by prompting a large language model. The authors provide two variants of reader, zero-shot and supervised setting, and prove the effectiveness of generation in both settings. ", "summary_of_the_review": "In my opinion, this paper proposes a novel approach for knowledge-intesive tasks, replacing retrieval with generation. GNEREAD proves its effectiveness in both zero-shot and supervised settings. The experiment results are interesting while more discussions about the need of generation and generated documents would give more intuition. ", "strength_and_weaknesses": "Strength:\n1. The proposed method to generate whole contextual documents effectively utilize the internal knowledge stored in a large language model, without a retrieval process from external sources.\n2. Experiments on three knowledge-intesive tasks show the strong performance of GENREAD on both zero-shot and supervised settings.\n3. The authors further provide the reproducibility results with open-source large langue models which is beneficial for future research. \n\nWeakness:\n1. Although experiments show the effectiveness of generation, more analysis on the need of generation would be helpful.\n- In Table 2 and 3, merging the retrieved documents with generated documents further enhance the performance. Which kinds of knowledge can be additionally captured by internal knowledge (generation) that might be missed during retrieval?\n2. Generation with large language models can suffer from hallucination, while there is no discussion on this aspect. Qualitative analysis of the generated documents would be interesting.\n3. Generating whole documents might require more computational cost than retrieval during inference. However, there is no discussion on inference time or the cost in the paper. \n4. The zero-shot performance can be changed according to the choice of prompts, while the details on the choice and the performance variance with different prompts are not provided.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "MCAkCVWAm_", "reviewer": "Reviewer_a9DF", "summary": "", "questions": "", "limitations": "", "rating": 10, "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": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper propose a new paradigm to solve the task of question answering.\nIn contrast of the standard paradigm retrieve and read, where first a set of documents are retrieved according to a question, and then they are \"read\" to answer the question, the proposed model first generates documents relevant to a question with a large language model, and then a reader answer the question based on the generated documents.\n\nThe authors propose a clustering approach to diversify the generated documents to increase recall.\n\nThe model is tested in two settings, where the reader is a large language model, and is a fine tuned QA system.\nResults on three tasks and six benchmarks show that the approach is very promising, outperforming standard retrieve and read sota models.\n", "summary_of_the_review": "The paper is sound and I don't see any major points for it to get rejected.\nIt is a novel idea, well executed and well presented.", "strength_and_weaknesses": "The experimental setting is clear and sound, there are many ablation study and experiments that give insights on the tradeoff input docs and model size.\nRelated work is well covered.\n\nI appreciate the study on the performance/model size tradeoff. Figuring out if emergent abilities can be pushed on smaller models (like has been done with PET for few shot learning), maybe using different prompting or light finetuning etc. it is an interesting research direction.\n\nIn the introduction the authors claim ther are 3 drawbacks of retrieval models, but apart for point 3 I don't see the proposed approach to directly address the first two points. Please, clarify this.\n\npage 5 bottom of the page Gppher -> Gopher\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "JZGkYtefnU", "reviewer": "Reviewer_Vpy9", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper presents a generate-and-read approach for solving knowledge-intensive tasks. Different from the retrieve-and-read approach that first retrieves evidence (paragraphs) from a corpus before feeding it to reader models, the current approach uses large language models (LLMs) to generate contextually relevant evidence which is then fed to a reader model. \n\nTo generate multiple diverse contextual evidence, the paper proposes a clustering-based prompting technique where the prompt is synthesized from (question, evidence) pairs from diverse clusters. Specifically, for each query in the training set, the top-1 document is retrieved using a standard retriever such as BM-25 or by prompting an LLM to generate a document. Next, the query-doc pairs are encoded with an LLM (GPT-3) to obtain 12,288-dimensional vectors. These vectors are then clustered using k-means. Next, the “n” query-document pair from each of these clusters form an in-context example which is used to generate a paragraph. This step is repeated k-times for each cluster to generate “k”-diverse documents. In contrast, retrieve-and-read approaches often do not enjoy this type of control during the retrieval process and likely retrieve multiple redundant paragraphs sacrificing diversity. \n\nThe proposed approach was evaluated on several knowledge-intensive tasks such as open-domain QA, fact-retrieval, and on wizard-of-Wikipedia (open-domain dialogue) tasks. \n\nThe proposed method achieves marginal improvement over retrieve-and-read baselines and significant improvements when the evidence from both retrieval and generation are combined.", "summary_of_the_review": "The paper proposes a simple and effective way of generating relevant contextual evidence for knowledge-intensive tasks. The proposed method achieves marginal improvement over retrieve-and-read baselines and significant improvements when the evidence from both retrieval and generation are combined. I am not fully convinced about the diverse retrieval (one of the primary motivations for the paper) and would like to see more experiments and clarification. Hence I am leaning toward weak acceptance.", "strength_and_weaknesses": "Strengths:\n\n- There is a growing body of recent work that is increasingly using LLMs to generate knowledge instead of retrieving it from external knowledge sources. This paper proposes a model in that line of work that is fairly interesting and obtains good experimental results.\n- The proposed model gets promising results on open-domain QA, fact-retrieval, and on wizard-of-Wikipedia (open-domain dialogue) tasks. The performance of just the model is comparably or modestly better than retrieve-and-read approaches, but combining evidence from retrieval and generation leads to nice gains on tasks, suggesting that the proposed approach is practical.\n- The paper is overall well-written and easy to understand\n\nWeakness:\n\n- A weakness of the proposed model is that the clustering and selecting of the prompts is *independent* of the given question. Essentially, if the same n (question, document) pairs are selected from each cluster, the prompts are exactly the same for each question. This might be suboptimal. For example, do you have a sense of how many prompts on average are useful for retrieval for a given question? It would also be interesting to devise question-dependent prompting.\n- Although I like the idea of the power of LLMs to generate highly contextual evidence for a given question, I believe the paper would be stronger if it included two more evaluation\n    - Since the paper focuses on the LLM's ability to do diverse retrieval, it would be good to test that on a benchmark that is explicitly designed to test that ability. For example, Qampari (Amouyal et al 2022) is a recent benchmark that tests for questions with multiple answers. It would be interesting to see the results on that.\n    - Multi-hop questions: I can imagine that LLMs might have the ability to generate contextual evidence that fuses information from two or more documents required to answer a multi-hop question. I think that might be a powerful way for answering multi-hop questions and it would be nice if the authors report some results on whether that is happening or not.\n- In Fig 2, the difference between clustering and sampling method doesn't seem to be too much. Could you report (in numbers) what is the exact difference in recall %? Also, just to clarify, does “sampling” refers to using nucleus sampling to generate K different documents (and not using other query-doc pairs in the prompt)? Is that correct? If so, I am a little worried about the very less difference between a method that does not use different (q,d) pairs in prompts and a method that uses.\n- I am confused by Table 4. What is the metric used since the values seem to be between 0.84 and 2.17? My guess is it is the average number of answers found by the model. If so, couple of comments:\n    - The difference between DPR and the proposed method is not that high (1.9 v/s 2.17 in TriviaQA and 1.1 v/s 1.22 in WQ; in NQ, DPR actually outperforms)\n    - I believe the table would be more readable if % coverage is reported and that would also help us to understand how much is the difference in performance between DPR. This is important and I would like to see this in the author rebuttal.\n- In Table 5, do you have any more insights as to why is the performance of OPT so behind that of GPT3 (e.g. in TriviaQA)\n- I would like to have the authors add an additional section regarding the failure modes of the model. For example:\n    - What happens when the evidence generated by the LLMs consists of incorrect factual information? For example, in Table 17, the LLM generates \"The collection of the districts to the east of the Jordan River is known as the West Bank.\" - this I believe is factually incorrect. Since Westbank is actually west of the Jordan River. To the east of the Jordan river is Jordan, which is the gold answer. Did the reader model still read this and output the right answer?\n    - Can adding evidence also from the retriever (i.e. real documents) help in mitigating some of the possible drawbacks that might arise because of LLMs hallucinating incorrect information?\n- In sec 3.1 where the joint probability of p(a,d | q) is described, I believe a summation of d is missing (marginalization).\n- “due to the high coincidence of token distributions in the decoding process under a single prompt.” - What does this exactly mean?\n- [Minor] - I think the related work section can be written better. Currently, it reads like a list of related work and there are no explicit points differentiating wrt current work. Currently, it only mentions that this work is the first work to apply it for knowledge-intensive tasks which, I believe is actually underselling this work.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "9du6ndTFENJ", "reviewer": "Reviewer_jnAy", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a “generate-then-read” approach that contrasts the “retrieve-then-read” approach that has been dominant in open-domain QA. It is based on large language models that encode vast amounts of knowledge and can recall it through prompting. Specifically, the paper proposes two models, one for the zero-shot setting and the other for supervised setting. In the zero-shot setting, the model is simply prompted to generate a relevant document, feeds it back, and generates the answer. In the supervised setting, the model generates a document per training question, clusters them, constructs in-context examples using one question-document pair per cluster, and uses them to generate the document and then the answer as it does in a zero-shot setting. Experiments done on 7 datasets effectively show the model outperforms a range of competitive baselines, including InstructGPT (one of the most competitive large LMs) &  InstructGPT followed by SOTA retrieval systems/Google (in the zero-shot setting) and FiD Followed by DPR/Google (in supervised setting).\n", "summary_of_the_review": "* In summary, this is a high quality paper that is definitely above the ICLR standard. The method is new, evaluation is solid and extensive, and results (that the generate-and-read approach outperforms the retrieve-and-read approach) are surprising and teach something new to the research community. The paper is also well written, is easy to follow, and tries to ensure experiments are reproducible.\n* As I stated above, the paper does not explain well enough on why generate-and-read achieves such a significant gain (especially compared to retrieve-and-read), which I think is a more important question. It does not include any qualitative examples or analysis in the main text (although it has some in the appendix), which makes it more difficult to understand what is going on with these models.\n\n", "strength_and_weaknesses": "### Strengths\n* The proposed approach “generate then answer” is new, and is well-executed.\n* The paper convincingly shows that the proposed models outperform a range of baselines, and I think this result is quite surprising and new to the research community. The experiments are very solid and extensive, and every competitive baseline I can think of is included in their comparison.\n\n### Weakness\n* Given that the findings are very surprising, the key question I have is – why does generate-then-read outperforms retrieve-then-read? And I think this paper does not answer this question, nor tries to answer this question. And I think the paper will significantly be better and more insightful if more analysis that tries to answer this question is provided. For instance, moving some qualitative examples in the appendix to the main text will help. Here are more concrete questions I have (which I don’t think authors should necessarily answer to all of them during rebuttals, but might help answering the question of “why it is the case”):\n     * Does it mean that we significantly underestimated LM’s closed-book QA performance, e.g., the LM already has all the knowledge to answer the question but we just didn’t prompt it in the right way, and this type of “elicit” prompting enables the model to get the correct answer more often?\n     * Based on examples provided in Appendix (Table 14–17), it looks like the model knows relatively early on what the answer is. For instance in Table 16, all GPT-generated documents already answer the question in their first sentence. Then, why is it significantly better than direct prompting?\n     * In fact, based on Table 14–17, I think the generated document has a very different distribution from actual Wikipedia documents. They really look more like a rationale that explains how the answer is arrived, just like chain-of-thought. It would be very interesting to see qualitative/quantitative analysis on how generated documents and retrieved documents are different. For instance, one possible quantitative analysis is to see what happens if the reader trained with retrieved documents is given generated documents, and vice versa.\n     * If I aggregate reasonings over the main paper and the appendix, it looks like the paper says retrieve-and-read underperforms because (1) the retrieval accuracy is not good enough due to lacking cross attention, and (2) even if the retrieval was successful, its “readability” is not good since it is written without a question in mind and thus contains information that is not directly related to the question. It would be informative to analyze cases where the proposed model gets correct but retrieve-and-read doesn’t, and see how many of them are (1) or (2).\n     * And if a lot of cases turn out to be (2), which the appendix seems to claim – why is finding answers from less readable documents difficult for huge LMs? In other words, how is “recalling knowledge seen during pre-training” easier than “finding the answer from a (less-readable but still answer-containing) document” for a large LM?\n* For the clustering-based method, the paper is treating “nucleus sampling” and “diverse human prompts” as a baseline, but isn’t “randomly choosing k question-document pairs” a more obvious baseline and should be included in the main table (Table 2)? Asking because based on Table 10, it looks like a clustering-based method is not really better than random k question-document pairs (~1% difference in R@10 on all three datasets – and  I think the difference will be even less in final answer accuracy). If it is the case, then I am not convinced with the claim in the paper that the clustering-based method is effective, especially given that the clustering-based method is significantly more complicated and expensive (requiring generating documents for all training questions, getting embeddings from GPT-3, etc).\n* Writing comment\n     * I think the first paragraph of p2 is a bit misleading, because “requiring the model to explain every token in the question and document” is not really shown in the paper (and is even unclear what it means), and “the generated contextual documents tend to be more specific to the question than retrieved documents” is also not really shown in the main paper (although partially shown with a few qualitative examples in the appendix). Also, “Second, large language models have been trained with considerably large amounts of data and parameters, allowing a type of “look up” of world knowledge stored in the model parameters” – seems to be more of a “reason” the method works, rather than the advantage of the method.\n     * It would be great to add limitations of the proposed models. For instance, the proposed models may not be able to answer questions about the knowledge LMs are not trained on (since there’s no knowledge to “recall”), may suffer from rare and less popular information, and won’t be able to update its knowledge when there’s a temporal shift (which is the real-world scenario). \n\n\n\n\n\n#### Side notes\n* There is a concurrent paper https://arxiv.org/abs/2210.01296 with a very similar (if not identical) method with different underlying LMs, and shows that generate-then-read does not outperform retrieve-then-read. I am curious what leads to the difference in results, although the methods look very similar. Of course, the authors are not required to discuss this paper in detail given that it is a concurrent (slightly later) work, but I think many potential readers will be curious as well.\n* The core idea of the paper is related to the literature in generative retrieval, where a document retrieval is done in a generative manner instead of dense retrieval (Cao et al. “Autoregressive entity retrieval” ICLR, 2021 and a few more). It might be nice to add discussion.\n* Question on a zero-shot setting: multiple papers have reported that zero-shot QA accuracy is significantly poor with an Exact Match metric, because the model does not know it should generate a concise text span (e.g., “Olympia”) rather than a sentence (“Zeus was the patron god of the city of Olympia”), and it tends to generate a sentence. This is mentioned in Liu et al. (https://arxiv.org/abs/2103.10385) and a few other papers. I wonder how much of this was the case in the zero-shot setting either in baselines and the proposed models? Was there a special prompt used to prevent this from happening? If “coverage EM” instead of “EM” is used as a metric (counts whether the generated sentence contains the answer or not), is the ranking between models the same?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster