Recitation-Augmented Language Models
Authors: Zhiqing Sun
Xuezhi Wang
Yi Tay
Yiming Yang
Denny Zhou
Venue: ICLR 2023 poster (2023)
Keywords: Large Language Models
In-context Learning
Memorization
Closed-book Question Answering
CBQA

Abstract: We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEneration (RECITE). Different from retrieval-augmented language models that retrieve relevant documents before generating the outputs, given an input, RECITE first recites one or several relevant passages from LLMs’ own memory via sampling, and then produces the final answers. We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance in various closed-book question answering (CBQA) tasks. In experiments, we verify the effectiveness of RECITE on three pre-trained models (In-house LM, UL2, and OPT) and three CBQA tasks (Natural Questions, TriviaQA, and HotpotQA). Our code is available at "https://github.com/Edward-Sun/RECITE".

Published as a conference paper at ICLR 2023
RECITATION-AUMEENTDD LANGUAGE MODELS
Zhiqing Sun Xuezhi Wang', Yi Tay', Yiming Yang", Denny Zhou'
1 Gooole Research, Brain Team
*Language Technologies Institute, Carnegie Mellon University
ABSTRACT
We propose paradigm help Large Language Models (LLMs) gener- ate more accurate factual knowledge without retrieving from an external corpus, called RECTTation-augmenter gEneration (RECITE). Different from retrieval- augmented language models that retrieve relevant documents before generating the outputs, given an input, RECITE first recites one or several relevant pas- sages from LLMs' own memory via sampling, and then produces the final an- swers. We show that RECITE NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, recite-and-answer scheme can achieve new state-of-the-art performance various closed-book question answering (CBQA) tasks. In experiments, we ver- ify the effectiveness of RECITE on four pre-trained models (PaLM, UL2, OPT, and Codex) and three CBQA tasks (Natural Questions, TriviaQA, and HotpotQA). Our code is available at https: / :ttpps:///tteeee gighhb..... com Edwwardwwwdddeeeeeeeeeaa Sun/RECITE.
powerful paradigm for knowledge-intensivg
INTRODUCTION
Large language models (LLMs) have achieved impressive in-context few-shot performance on noowledge-intessvvg NLP tasks (Brown et al., 2020; Rae et. 2021; Hoffmann et al., 2022; Chowd- hery et al., 2022). For example, in open-domain question answering (Chen et al., 2017), demon- strated by only a few examples question-answer pairs, LLMs are able to answer arbitrary factoid questions (Joshi al., 2017; Yang et al., 2018; Kwiatkowski et al., 2019). Recent research (Guu et al., 2020; Lewis et al., 2020; Izacard et al., 2022) shows that carieval--ugmeentn improve I.LMs' performance
can further knowledge-intensive tasks by conditioning the LLMs retrieved relevant passages from an external corpus.
Direct Generation [o.g., PaLM)
Retrievalleuugmente Generation [e-g. Atlas)
Recifation-augmennee Generation (ours)
LM Input
LM Input
LM Inpul
song
Question: who wrote the school good
Question: who wrote the song hate you
hate you love you
Recilation:
U,
(stylized
Answer: Gresh
lowercase) a song American singer and rapper Gresh featuring American singer Olvis O'Brien.
Retriever Results
Answer: Gnash
Question: who wrote the school for good and evil
The School for Good and Evil fantasy fairytale hexalogy Soman Chainani...
Question:
for good
and evil
LM Output
The School for Good and Evil was first published on May 14, 2013 by Soman Chainani..
Recitation:
Output
Outpul
Answer: Soman Chainani
Answer: Soman Chainani
Answer: Soman Cheinori
Figure I: Illustration of evaluating (few-shot) open-domain question answering with (closed-book) direct generation (Chowdhery et al., 2022), (open-boot book) eetrievll-augmens generation (Izacard et al., 2022), and (closed-book) recitation-aut ceutation-augmmeeeed generation (ours).
Work done during internship at Google.

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Published as a conference paper at ICLR 2023
This paper proposes new paradigm to help LLMs generate more accurate factual knowledge without retrieving from an external corpus, called wherein we tackle knowledge-intensiv NLP tasks by first reciting relevant information and then generating the outputs. Such a two-step paradigm decomposes the original knowledge-intensive task into two sub-tasks: knowledge-recitatee as a form of intermediate knowledge retrieval step (from the model weights), while the latter is the execution step that produces the final outputs.
DeerTaation-uummnnte gEneration (RECITE),
and task-execution, where the former can he regarded
The motivation of introducing an additional knowledge-recitation step comes from our observation that while few-shot prompting can help LLMs execute specific NLP tasks, these tasks are usually not in similar form as the original causal language modeling pre-training objective. This hinders LLMS from effectively reciting knowledge from their memory (Carlini et al., 2021). Consider a student taking a closed-book exam that contains wewwlede-intensiie questions, for example, "what the tenth decimal of πT ? They typically cannot directly answer this question because in studying stage (in analogy to the language modeling pre-training stage for LLMs), it is highly unlikely that they would read "the tenth decimal of π is 5". However, there can be some sentences like "the first N digits of Therefore, a student can possibly answer this question in a wecie-and-answe scheme: "The first 10 digits of are 3.14159 26535. So the answer is 5". Here, the knowledge-recitation step can serve as an intermediate step that mimics the language modeling pre-training task, and thus better helps the LLM to generate factual knowledge.
3.14159 26535..." existing in the textbook that can be recited by the student.
We verify the effectiveness of our recitation-augae augiented generation on few-shot Closed- Ques- tion Answering (CBQA) tasks (referred as recite-and-answed in the CBQA context), as illustrated in Figure 1. CBQA is an attractive open-domain QA task in that a fully parameterized LM can generate answers directly without an external corpus or separate retrieval models (Roberts et al., 2020). We show that the proposed recite-and and-answwrr scheme is an effective method for CBQA and compatible with other techniques for boosting few-shot performance of L.L.ss. We also show that, in addition to improving the few-shot in-context learning performance of RECCTF-enhanced LLM,, fine-tuning the pre-trained LLMs on synthetic generated pnestion-passage pairs can further improve the recitation performance and lead to a better downstream QA accuracy.
Experiments on four large language models (PaLM (Chowdhery et al., 2022), UL2 (Tay et al., 2022a), OPT (Zhang et al., 2022)), and Codex (Chen et al., 2021) show that recite-and-answer scheme can improve performance on various types of CBQA tasks, including Wikipedia-bart based single-hop QA (Natural Questions, Kwiatkowski et al. 2019), trivia questions (TriviaQA, Joshi et al. 2017), and Wikipedia- multi-hop QA (HotpotQA, Yang et al. 2018).
RELATED WORK
2.1 OPEN-DOMAIN QUESTION ANSWERING
Open-domain question answering (Prager et al., 2007) refers to the task of generating answers for arbitrary context-free questions. In the open-book setting, is typically assumed that the QA model can find the answer in an external corpus, e.g., Wikipedia (Chen et al., 2017; Izacard & Grave, 2021) or web pages (Lazaridou al., 2022). This is in analogy as taking an open-book exam where students can search over an external knowledge corpus. The standard pipeline (Chen et al., 2017; Izacard & Grave, 2021; 2020) usually consists of a learnable or non-learnable document retriever module and a learnable neural network- based reader module.
In the closed-book setting, the QA model not allowed to access any external knowledge, and needs to store all the knowledge in its parameters. It has been recently observed that large-scale pra-trained language models (Devlin et al., 2019; Radford al., a; Yang et al., 2019b) can internalize sort of implicit "knowledge base" after pre-training (Petroni et al., 2019; Jiang et al., 2020; Talmor et al., 2020). Roberts et al. (2020) show that after fine-tuning on open-book question-answer pairs, T5 (Raffel et al., 2020) can answer large portion of knowledge-intensive questions. This is similar as taking a closed-bood exam. However, Lewis et al. (2021) found that the high performance is mainly due to training set question memorization. Wang et al. (2021) also found that it is still challenging for relatively scall-scale pre-trained language models like RoBERTa (Liu et al., 2019) or GPT-2 (Radford et al., b) to answer closed-book questions.
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In this work, we focus evaluating the CBQA performance of large language models (LLMs) in the few-shot setting, which ideally minimizes the bias of train-test overlapping (Liu et al., 2021). We propose recite-and-ant and-answer scheme, which is similar student first recite the factoid knowledge about the question, and then answer the question.
2.2 IN-CONTEXT FEW-SHOT LEARNING
Large language models (LLMs) such GPT-3 (Brown et al., 2020) have the surprising ability to do in-context learning, where the model learns to do new tasks simply by being prompted a few exemplars. The LLMs learn from these exemplars without being explicitly pre-trained for in-context learning and without any gradient updates or fine-tuning. Recent study showed that such ability improves with the scaling of both model size (Brown et al., 2020; Rae et al., 2021; Chowdhery al., 2022) and number of tokens for training (Hoffmann et al., 2022). When evaluated on knowledge- intensive question answering tasks, these models are usually evaluated in the closed-book setting, where the factoid knowledge are completely stored in the model parameters of dense LLMs.
Recently, Atlas (Izacard et al., 2022) shows that for knowledge-intensii lite model with retrieval augmentations shot fine-tuning, which proves that memorization can be decoupled from generalization in LLMs. In contrast, we show that still large underestimated amount of knowledge be retrieved from LLMs' model weights through better-designed prompting.
NLP tasks, relatively achieve similar or even better performance through few-
2.3 RATIONALF-AUGMENTEE REASONING
Ling et al. (2017) pioneer the work of solving math word problems by generating step-by-step human-readable solutions described by natural language and math equations before the final answer. That fundamentally different from other works which directly generate the final answers or use formal languages. e.g. equations only, to illustrate the intermediate solving steps (Roy et al., 2016; Amini al., 2019; Chen al., 2019). Cobbe et al. (2021) extend (Ling et al., 2017) by constructing a much larger dataset to finetune pre-trained large language model to solve math word problems and a parameterized ranker is trained to rank candidate solutions to improve the solving rate. Wei et al. (2022) propose chain-of-thought prompting which combines the idea of natural language rationales (Ling et al., 2017; Cobbe et al., 2021) with few-shot prompting (Brown et al., 2020).
In this work, instead of generating a chain of thought for multi-step reasoning questions, we de- compose the process of answering a mnowledge-intensive question into two steps: recite the relevant knowledge stored in the model parameters, and then answer the question.
2.4 MEMORIZATION IN LARGE LANGUAGE MODELS
Recent study shows that large language models can memorize its training data, and generate texts from training data given certain prompts (Carlini al., 2021; 2022; Zhang et al., 2021; Kharitonov et al., 2021: Thakkar et al., 2020; Carlini et al., 2019; Tirumala et al., 2022). Most related to our work, Carlini et al. (2022) found that the memorization ability of LLMs significantly grows as the model capacity increases, the number of times an example has been duplicated, and the number of tokens of context used prompt the model. While these works mainly analyze the fundamental properties of memorization in the exact setting, where exactly N tokens are used as the prompt to reproduce the suffix of the prompt, our work relies on "fuzzy memorizaiton", where the prompts tend to not be exactly the same the training data, but still improve the memorization accuracy.
The proposed recitation -aggmenee generation idea is also related to the line work on utilizing Transformer memory as an information retrieval model (Tay et al., 2022b) and self-talk models for commonsense reasoning (Shwartz et al., 2020; Liu et al., 2022). Zhuang et al. (2022); Wang et al. (2022c); Zhou et al. (2022) proposed to augment documents indexing time with number of generated queries. Bevilacqua et al. (2022) proposed to directly generate n-grams grounded in one multiple documents with constrained decoding.
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family majorty
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Recitation 2:
4) multiple-recite and answer
Recitation
Figure 2: Illustration of prompt-based in-context learning for recitation generation, recitation- augmented question answering, self-consistency ensembling, and multiple-recit--an---- multi-hop questions (Sec. 3.1). In muliiple-reciee-nd---sss lize the information from the previous ones, such as "Oberoi Group" in this case. The prompts for self-consistenge and multi-hop
for
scheme, the latter recitaiton can uti-
dropped for brevity.
LEARNING TO RECITE FOR CLOSED-BOOK QUESTION ANSWERING
The goal of this paper is to mimic human's ability to recite relevant factoid knowledge (Mc- Daniel et al., 2009) before answering knowledge-intensive questions, such that these questions can be answered more accurately. In the following we describe our recite-and-answer scheme for few- shot flosed-book, question answering (CBQA), which consists two components: (1) a evidence- recitation module for reciting relevant passages, and (2) questioonanssered ating answers given the recited evidence. Notice that in this paper, we focus on few-shot setting, in which we assume only few question-answer demonstrations are provided. In Natural Ques- tions (Kwiatkowski et al., 2019) benchmark, since the questions are from queries issued to the Google search engine by multiple users, and thus can be regarded as unannotated data, we further assume that we have top-retrieved Wikipedia pages for these questions. The paragraphs in these top-retrieved Wikipedia pages will be used to generate synthetic paired question-recitation recitation data for fine-tuning the LM (described in Section 3.2).
module for gener-
3.1 PROMPT-BASED
FOR QUESTION-ANSWERING
Recitation-augmenned question answering We start with single-hop question answering (K (Kwiatkowski et al., 2019; Joshi et al., 2017), where the answers are usually supported by spe- cific document the corpus, which is sometimes referred as evidence (Joshi et al., 2017). Different from chain-of-though prompting (Wei et al., 2022) where rationale is directly generated to explain the generated answer (Joshi et al., 2017; Narang al., 2020; Lampinen al., 2022), we propose to first recite a passage about the question, and then answer the question based the recitation.
We propose a prompt-based learning-to-recite scheme by leveraging the LLM's in-context learning ability (Brown et al., 2020). We prompt the LLM with paired exemplars of questions and recited evidences, and the LLM can learn in an in-context manner to generate a recitation for an arbitrary question. To perform passages at the beginning of the original question-answer exemplars as single prompt, and then generate the final answer (Step & in Figure 2).
few-shot question answering, append the recited
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feweshot question generation
<Pasage
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Question
<Pamagge ftx
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fine diverse recitation
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<Resiaaiinn No
decoding recitations
Question answering with aggregated recitations
Sampling diverse passage hints
Recttation: The frst deied sappet United dnggtom...
when was child boneti paid for the first chile?
Paesage hint Chil support Short desceplics Puragraph
Frozen Language Model
Reciuaion: The frucchil bendli wis Fiscccedd in the Uill
Pancage Chili benelt Short desceplict Parsgraph #1
Fieelred Red UM
Recisstion: beneft paymert mode lo pannts the United Hingtem, halnnd
Maalory Paragraph
augmantation
Figure 3: Illustration of question answering with diverse recitation and the corresponding few-shot question generation and fine-tuning processes.
Self-consistency ensemble The factual knowledge about question can appear in several places in the language model's training corpora. For example, the fact of "Queen Elizabeth II opened the London Bridge 17 March 1973" appear in both Wikipedia page "London Bridge" and page "March 1973", so highly likely that there exists knowledge from different articles that could lead the same, correct answer. With this motivation, reasoning in chain-of-thought,, recitation-aummented question answering can also benefit from the self-consistency technique with multiple-path decoding (Wang et al., 2022b). Specifically, given an arbitrary question, we first use iop-k sampling to independently generate few recitations, and then greedy decode the answer of the question based on the sampled recitations. Finally, we determine the optimal answer by taking plurality/majority vote (Step 3 in Figure 2).
argue that similar to multi-step
for multi-hop question-answering Multi-hop question answering requires the QA system find and reason over multiple supporting documents. However, the nature of recitation restricts us to recite passages from one article at a time. In order to apply the recite-and- answer scheme to solve multi-hop questions, we introduce multiple-recite-and and scheme (Step 4 in Figure 2), that is, given the multiple-hop question,
Multiplerrecite-and-ame
the prompt words such "Recitation 1" and "Recitation 2" to clicit the LLM to generate recitation passages on different topics. Since the multiple recited passages generation of later passages can effectively utilize the information both in the original question and the previous recited ones. Our multiple- centtiple-reitt-aat andeae-- scheme for multi-hop question-answeriin is also compatible with the self-consistency technique, by applying top-k sampling when generating multiple recitations and performing majority voting for the final answers.
generated in one-pass from the LLM decoding sequentially, the
3.2 PASSAGE HINT-BASED BASED DIVERSIFIED RECITATION WITH FINE-TUNING
Passage hint-based diversified recitation While the sampling- based recitation and self- consistency improves the robustness of recite-and-answer method, one argument for its inefficiency that the evidence-recitation module samples the wrong facts about the question, the question- answering module will not be able to figure it out and tend to generate the wrong answer. Therefore, on the one hand, we need to use a low sampling temperature to avoid generating recitations with wrong facts, the other hand, want make sure the sampled recitations have enough diversity.
To tackle such a dilemma, we propose passage hint-based diversified recitation. We observe that in well-formed text knowledge bases, such Wikipedia, for each passage, by concatenating the section titles and the in-section order each passage. For example, the passage hint of the second passage in Section 5.2 "Enforcement" of Wikipedia page
usually find unique passage hint
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"Child support" would be "Child support Compliance and enforcement issues Enforcement Paragraph #2". In passage hint-based diversified recitation, we first use sampling to generate diverse set of passage hints, and then use greedy decoding to ensure the factual accuracy of the contents in each passage.
Since each passage hint corresponds to unique passage, and then generate the full passages to get more diverse recitation passages. Furthermore, as the recited passages are less likely be similar due unique passage hints, inspired by recent progress on muestion-answering with multiple retrieved passages (Izacard & Grave, 2021), we use aggregated diverse recitations as single context, and generate the answer with few more question-answ pair demonstrations. Figure (lower) illustrates the seite-and-answee scheme with passage hint-based diversified recitation.
first de-duplicate the passage hints
Fine-tuning on few-shot generated questions We found that although the training data of many existing LLMs (Devlin et al., 2019; Chowdhery et al., 2022) contains the Wikipedia corpora, which are usually regarded as the factoid documents for knowledge-intensio (Joshi et al., 2017; Kwiatkowski et al., 2019), the section titles are usually not explicitly included in training. This makes the pre-trained LLM hard to discover the mapping from the question to the passage hint, and to the full passage merely by few-shot prompting.
question answering tasks
To address this issue, we propose an additional fine-tuning stage to adapt LLMs to learn such map- pings. Assuming we have access to not only few question-answer pairs, but also the top-retrieved Wikipedia pages for queries issued to the Google search engine by multiple users (K (Kwiatkowski al., 2019), then finetune the LLMs on the generated data.
few-shot prompting to generated synthetic question-hint-pan hint-passage pairs and
Specifically, we use the ground-truth evidence and question pairs the prompt, and generate new questions with in-context learning for randomly sampled passages from Wikipedia pages. Next, based the few-shot generated questions, train the LLM to predict the original passage hint, as well as the passage content. Figure 3 (upper) illustrates the whole process of passage hint fine- tuning.
EXPERIMENTS
In this section, we report empirical evaluations of our proposed RECITE with recite-and-answe schemes diverse set of few-shot closed-book question answering tasks and different language models with varying scales.
4.1 EXPERIMENTAL SETUP
4.1.1 EVALUATION DATASETS
Natural Questions Natural Questions (Kwiatkowski et al., 2019) consists of questions aggregated from the Google search engine and the answers from the Wikipedia page in the top search results. We treat it as a single-hop question answering task. Since Natural Questions contains the so-called "long answer" annotations, which is a whole HTML bounding box containing enough information to infer the answer, directly utilize the "long answer" as the ground-trute -ruththhthhhhuo recitation exemplars in our prompt (Sec. 3.1). In order to make a direct comparison with recent LLMs (Chowdhery et al., 2022; Izacard et al., 2022), we evaluate our methods in 5-shot and 64-shot settings.
TriviaQA TriviaQA dataset (Joshi et al., 2017) is constructed by collecting Trivia enthusiast thored question-answer pairs and their retrospectively collected evidence. Since there is no obvious way to collect "long answer" in the retrospective evidence documents (the exact appearances of the answer may contain enough information to infer the answer), we evaluate TriviaQA in the single- hop 5-shot setting, and manually compose the recitation passage from Wikipedia for 5 randomly sampled training questions. The concrete prompt can be found in the appendix.
HotpotQA HotpotQA (Yang et al., 2018) is designed to explicitly test QA systems' ability to perform multi-hop reasoning. It is collected by explicitly composing questions requiring reasoning

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Table 1: Performance comparison Natural Questions (NQ), TriviaQA, and HotporQA, The num- ber of shots for each task are mentioned in parenthesis.
PaLM-62B
UL2-20B
OPT-30B EM/F
Codex-002 EM/F1
EM F1
EM/FI
25.76 36,471s)
10.16 20.17
Standard-prompiinn (direct)
14.97
/ 22.93(s) 31.45 / 44.75
28.98 / 11.44.1..g 13
12.70 21.97
NQ
28,70 / 39.7619)
14.16 / 23.13(5)
Recite-and answer (20-path)
17.84/
26.74 (5) 35.84/ / 49.12
31.34 42.48 64)
14.94 / 24,29
Standard Standard-prompiing (direct)
65,38 / (1.85
48.73 / 54.32(5)
45.90/ 50.68(5) 49.02
81.84/ 86.09(s) 83.50 / 88.03
TriviaQA
Recite-and-answer (20-path)
65.84 (25
53.42 / 58.69
54.22
Standard prompting (direct)
20.51 / (84
16.99 24.99(4)
16.70 25.21
28.32/ / 39.03
HotpotQA
Chain-of of-thought (20-path)
23.73 / 32.80()
17.68 24.87
16.89
24.03
34.38 / 45 50(m)
Recite-and-answe (20-path)
26.46 (5.67
19.04 27.32
17.77 26.58
4) 37.11 / 48.37
about multiple supporting context documents. Following Wang et al. (2022a), we evaluate Hot- potQA as multi-hop question answering task in the 4-shot setting. But instead of chain-of-thought prompting as in (Wang et al., 2022a), we use multiple-recite-an seee--nnd-nswwe (Sec. 3.1) to achieve multi- step reasoning. We also provide the concrete prompt in the appendix.
Metrics We calculate the Exact Matching (EM) and Fl scores for the normalized answers, while the specific text normalization applied on each dataset can be slightly different.
4.1.2 PRE-TRAINED LANGUAGE MODELS
We evaluate the effectiveness of RECITE on four langauge models: PaI.M, U1.2 (Tay et al., 2022a), OPT (Zhang et al., 2022), and Codex (Brown et al., 2020; Ouyang et al., 2022; Chen et al., 1021). Due to the space limit, the detailed descriptions of them are provided in Appendix D.
4.2 EXPERIMENTS
We use the test split for all tasks if the test split is available and has labels for evaluation, otherwise we use the dev split. In addition, TriviaQA and HotpotQA are too large to run large language models on, so we used the first 1,024 data points for evaluation.
4.2.1 PROMPT-BASED RESULTS
We report the single-hop closed-book question answering (CBQA) evaluation results on Natural Questions (NQ) and TriviaQA and the multi-hop CBQA evaluation results on HotpotQA. In Tab. 1, we report the results with prompt-based in-context learning and consisensst
From the tables, we can see that the proposed recite-and and-answer scheme can significantly improve the CBQA performance on both datasets with various pre-trained language models. While the per- formance improvements on NQ is more consistent across different language models, we find that the improvements from recite-and-answerr is more significant on smaller language models on TriviaQA. Our hypothesis is that the Trivia-style question usually contains more contextual information in the question, thus weakened the effectiveness of recitation for strong LLMs like PaLM.
that the recite-and-answe scheme can outperform the chain-of- of-thhught prompt- ing performance on the multi-hop reasoning task. Interestingly, we also find that for LLMs that have large gains from chain-of-though (i.e., PaLM), they also have large improvements from recite-and- answer.
Besides,
4.2.2 RESULTS OF PASSAGE
DIVERSIFED RECITATION
For Natural Questions dataset, since it has the collection of tep-retrieved Wikipeida pages corre- sponding to the unannotated queries issued the Google search engine, we additionally report the diversified recitation results of fine-tuned PaLM model in Tab. 2. From the table, we find that diversified recitation can further improve the performance of PaI.M the NQ dataset.
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Table 2: Performance comparison of Pal.M-62B Natural Questions (NQ) dataset with standard- prompting, recite-and-answar with self-consistency sampling, and recite-and-answe with diversi- fied recitation. The number of shots for each task are mentioned in parenthesis.
EM / (l
EM / Fl
Standard (direct)
25.76 / 36.47
28.98 / 40.13
Recite-and-answe (20-path) Recite-and w/ diversified recitation (20 30ttp
28.70 / 39.76
31.34 / 42.48
32.20 44.02
33.23 45.29
4.3 ANALYSIS
UL2-208 belkanasiency
30
OFT 321 (sef consitanccy cansiteery)
OFI-dDV (yciconooony)
adiioonateecy poltos
cal cOeG/teeyy patte
Figure 4: TriviaQA EM/FI on OPT-30B and UL2-20B with different # of self-consistency paths.
4.3.1 ON THE NUMBER OF SELF-CONSISTENCY PATHS
We analyze how the number of passages recited would affect the performance of recite-and under the self-consistency setting. Due to the costly inference of LLMs, we first sample up to k = 20 recitation passages, and then apply self-consistency simulate less paths. For each number of self-consistency paths, we evaluate the randomly selected subsets five times and report the mean and standard deviation. We conduct an analysis on OPT-30B and UL2-20B on the TriviaQA dataset and report the results in Fig. 4. We can that sampling more recitation passages tends to improve the seite-and-answer performance, while less randomness is observed with more self-consistency paths.
randomly selected subset of recitations to
4.3.2 ON THE ROBUSTNESS OF FEW-SHOT EXEMPLARS
A well-known problem of in in-context few-shot learning is its instability to the choices of exemplars and their orders (Zhao et al., 2021). We evaluate the robustness of standard prompting and our recite- and-answer method with 5 random seeds and report the mean and standard deviation of UL2 model running on the TriviaQA dataset in Tab. 6. The 5-shot exemplars are randomly sampled and shuffled that with recitation sampling, recite-and
for each seed. From the table, similar robustness (in terms small performance deviation) as standard prompting under different random seeds and numbers of self-consistency paths. The overall gains by recite-and-answer significant compared to standard prompting regardless of the choice of few-shot exemplars.
exhibits
4.3.3 RECITATION V.S. RETRIEVAL V.S. GROUND-TRUTH
One may ask without the external corpus, whether the quality recited passages with LLMs bet- ter than simple retrieval models, e.g., BM25 (Robertson et al., 2009)'. To answer this question, performance of UL2 and Codex on three kinds of context passages: retrieval, recitation, and ground-truth. We report the results first 1024 validation exam- ples in Natural Questions (NQ) dataset, since it is the only dataset that contains the "long answer" annotation that can be regarded as ground-truth context passage. From Tab. 3, we can see that the classic retrieval model, i.c., BM25, is still a very strong baseline for collecting information from the corpus. Nonetheless, compared BM25, our recite-and-on -ndee-aand-nswerr still achieves a quite competi- tive performance via generation only and without using any external corpus. Besides, we find that
evaluate the few-shot
We use the pre-indexed "enwiki-paragraphs" corpus in the pyserini package (https: //gi thub. castoring ni / which is originally designed for BERTserini (Yang et al., 2019a).
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Table 3: Natural Questions (NQ) results with different context passages.
UL2-20B FI
Codex- (02((5)
EM F1
No passage
10.16 / 20.17
31.45 / 44.75
Ground-truth passage
41.02 55.73
49.32 / 64.32
BMM25-Retreee (Top-1)
16.31 27.66
33,20 47.45
LM-R LM-Recitationss) (20-path)
14.16 23.13
35.84 49.12
Table 4: Per-question emorr analysis on TriviaQA.
Table 5: Per-path error analysis on TriviaQA.
UL2-20BBss)
OPT-30B,s)
Recit. Ans.
UL2-20B
OPT-30B
Hits Majority
53.42%
49.02%
/ 33.60%
30.06%
Not Recit.
21.09%
22.27%
X
7.87%
9.79%
Hits @ 22-Reecit
5.66%
8.01 %
/
12.10%
12.57%
Hits @ 20-Path
19.82%
20.07%
x
X
46.44%
47.58%
stronger models (i.e., Codex) tend to benefit more from the the model's own recitation than BM25 retrieved context.
4.3.4 ERROR ANALYSIS
We perform an error analysis on the 1024 evaluation examples in the TriviaQA dataset. We classify the errors into three categories: 1) Not Recit., i.e., the correct answer is not recited in any of the 20 recited passages self-consistency. 2) Hits @ 20-Recit., i.e., the correct answer can be found in one of the recited passage, but does not appear in the QA module's outputs. 3) Hits @ 20-Path, i.e., correct answer one the final outputs of the 20 self-consistency paths, but does not have the majority votes. The correct final answer is marked as Hits @ Majority (i.e., Exact Matching). An algorithmic description is given Algo. 1. We report the results of UL2-20B and OPT-30B in Tab. 4. We that "No Recit" and "Hits @ 20-Path" account for the majority of the errors, meaning that the QA module performs quite well (if the correct answer appears one of the recitation passages, it will be extracted by the QA module in most of the cases), and the main bottleneck still lies in the recitation quality and answer aggregation strategies.
We also perform per-path error analysis, i.e., how many questions be answered correctly (or not) when the recitation exactly contains (or not) the answer tokens. The results are shown in Tab. 5. We can see that around 7% 10% questions have the correct recitation but cannot produce the correct answer, while around 12% questions do not have the correction recitation but can be answered correctly anyway.
5 CONCLUSION & DISCUSSION
In this paper, we propose novel recitation-augmented generation framework improve lan- guage models' performance in the closed-book question-answering setting. We hypothesize that for knowledge-intensie NLP tasks, encouraging the model explicitly recite specific knowledge source would be helpful in augmenting its memory. In addition, we found that diversifying the recitation process can beneficial as well since usually there exists multiple knowledge sources that could be used to answer the same question. We show promising results over three large lan- guage models and across three different closed-book QA datasets, demonstrating the effectiveness of our proposed anite-andd-nswer approach.
One limitation of our method that updating tene-sensitive knowledge for a pure LLM-bbaee method requires training fine-tuning the LLMs the new corpus, which be costly. For future work, we plan to further validate the effectiveness of pecitatio-auummente generation for other knowledge-intensic NL. tasks in the closed-book setting, such fact checking.

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ACKNOWLEDGEMENT
We thank the support and feedback of many people from Google Brain team and the constructive suggestions from the anonymous reviewers.
ETHICS STATEMENT
The goal of this paper is recitation intermediate step generate accurate factual knowledge in the model's outputs. Therefore, our method should in principle improve the faith- fulness of the LLM systems. However, unlike eerrieal-augmeeter generation (RAG) models that can use external trustworthy corpus, all the intermediate steps in RECITE is generated by the LLM itself, RECITE may further enhance the existing biases in the LLMs' model weights compared to RAG.
REPRODUCIBILITY STATEMENT
Model weights The model weights of two LLMs used our experiments, i.c., UL2-20B (Tay al., 2022a) and OPT-30B (Zhang et al., 2022), are publicly released through GCP bucket (gs: l/scenic-buucke/ull1
and Github (https: /ttp//ggttttt/tt com/ facebookresearch.. metaseg), respectively. The Codex (code-davinci-002 model is publicly available through API calls (https: //betas///ba..... opwnai..nn........... wttp////w/we//////////
Evaluation datasets The three evaluation datasets used in our experiments (Natural Questions TriviaQA?, and HotpotQA*) are all publicly accessible.
Prompts We provide all the used prompts in the appendix.
Source code Though the prompt examples in the appendix should be enough reproduce all the results in our paper, we open-source all the evaluation code at https: //t::///tii /githtb....u..ggg thub. com/ Edward- Sun/RECIIT
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A ILLUSTRATIONS OF PROMPTS AND LANGUAGE MODEL OUTPUTS
Fig. 7-15 illustrate the evidence-recitation, question-answerinn prompt that we used for Natural Questions, TriviaQA, and HotporQA dataset. We also provide the example sampled recitations for these datasets. Notice that for Natural Questions, we use the "long answer" annotation as the recitation in the prompt, while for the other two datasets, manually compose few recitation passages based on web search.
B PRINCIPLES OF PROMPT DESIGNS
We mainly follow Chowdhery et al. (2022) and use two new line symbols "(n\n" as the separator between different components within exemplars, and use three new line symbol "|n)m)n" as the separator between different exemplars.
For the UL2 (Tay et al., 2022a) model, since its original SentencePiece (Kudo Richardson, 2018) vocabulary does not encode the new line symbol "n"", we instead use "; to replace "(nn" as the separator in all the prompts.
C DETAILS OF PASSAGE HINT-BASED FINE-TUNING
For Natural Questions (Kwiatkowski al., 2019) dataset, we assume that we have top-retrieved Wikipedia pages for the unannotated queries issued to the Google search engine by multiple user. We collect the passages in these pages as corpus, and use the rule to annotate the hints these passages.
To make fair comparison with prompting- 5 paired "long answer"
models both 5-shot and 64-shot, we only use answer"-question -question exemplars the prompt to generate the synthetic question for the sampled passages from the Wikipedia hint-passage corpus, and thus construct the synthetic question- hint-passage paired fine-tuning data.
We train PaLM in the constructed corpus for 10,000 steps with a batch size of 64, which takes approximately 1 day in 64 TPUv4 chips?. The fine-tuned model can be used for passage hint- diversified recitation without any further prompts.
D PRE-TRAINED LANGUAGE MODELS
PaLM PaLM is a family of densely activated decoder trained on the language modeling objective. It has strong capabilities in in-context few-shot learning, multilingual, well as reasoning tasks. In this paper, we use the PaLM model with 62B parameters.
UL2 UL2 (Unifying Language Learning, Tay al. 2022a) is an encoder-decoder model trained on mixture of denoising tasks in unified framework. In this paper, since we mainly focus on the in-context learning ability of language models, we use UL2-20B in the S-Denoiser mode (i.e., pre-trained with the prefix language modeling)
OPT OPT (Open Pre-trained Transformer language model, Zhang et al. 2022) is a family re- cently released open-source densely activated language model that aims to re-reproduce comparable results as GPT-3 (Brown et al., 2020). We use the 30B one? in this paper.
Codex Codex (Ouyang et al., 2022; Chen al., 2021) is variant of GPT-3 model (Brown al., 2020) that can understand code. We use the public OpenAl API to access the conditional generation outputs of the Sode-davinvi-OO model in this paper.
'ttpss: cww.d. google
"This can be achieved by append the "[NL.]]" and "[extraii_00"" token to the beginning and the end of the prompt.
com http:://ggghhb..ccccooccooobeeee http///eeooooeeeerrceee.....ccce
"https://opent http://pppaaa..ooa....oo/. coom/pp//
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16
22
12
6
10
18
Figure 5: Nature Questions EM/F1 UL2-20B with different # of shots.
direct
40
att-and-answer
35
30
25
5
0.06 (umal)
(bese) 0.77 (large)
2.500
11.0
0.06 (smel)
0.22 (base)
0.77 (large)
2.5 kx)l
11.0 (x0)
model parameters (B)
model parameters (B)
Figure 6: TriviaQA EM/FI on 5-shot FLAN-T5 with different model sizes.
Algorithm Per-question Error Analysis
Require: N ground- answer labels {A,,), for single question
Require: recitations [ and answer predictions {P:)
K from K paths
normalized answers + (normalize(A)))
normalize
E normalized answers then
return "Hits@Majority""
else Any A ((normalizee(P) (P)) E norrnalizeedansseerss... then
return "Hits@20 Path."
else Any Luy(Any(noommalize ((normalized answers, E (or)a ))) then
return "Hits@20 Recit."
else
return "NoRecit."
end if
ANALYSIS OF THE NUMBER OF EXAMPLES IN FEW-SHOT LEARNING
We analyze the influence of the number of shots on Natural Questions dataset for standard- standard-prompting model and our recite-and-answer model in Fig. 5. We see that recite-and-answer prompt- ing achieves consistent improvement over standard prompting, while the largest improvement is achieved in the I-shot setting.
F ANALYSIS OF MODEL SIZES ON INSTRUCTION--FNEETUNDD LANGUAGE MODELS
Instruction cnaructtoo-finetunedd language models (Sanh et al., 2021: Bach et al., 2022; Wei et al., 2021: Chung al., 2022) trained collection of datasets phrased instructions has been shown improve
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Table 6: Robustness evaluation of UL.2 TriviaQA with different few-shot exemplars over ran- dom seeds.
EM,y
Fl
Standard (direct)
48.42 (+: 0.71)
53.85 (4 0.57)
RECITE (5-path)
49.75 (+: 0.50)
54.78 (# 0.46)
RECITE (20-path)
52.68 (#: 0.62)
58.05 (# 0.58)
model performance and generalization unseen tasks. They tend to show better zero-shot or few- shot performance under the same model sizes compared their vanilla LM pre-trained counterparts. We analyze the performance (Chung et al., 2022), on TriviaQA dataset for standard- pprompddp model and our recite-and-answe wnwweer model in Fig. 6. We can that recite-and-ane -ad--anddd-neeeee prompting achieves consistent improvement over standard prompting, while the largest improvement is achieved the largest "xxl (1IB)" setting.
one representative instruction-finee finettnnffeteeed language model, FLAN-TS
G LANGUAGE MODEL HYPERPARAMETERS
We report the hyperparameters of LLMs we used Tab. 7.
Table 7: The model hyper-parameters of the large language models used in our experiments. Note that for Codex (i.e., sende-davinci--ie the hyperparameters of GPT-3 as common educational guess.
the details (including size) unknown, report
Model
Type
of Layers
of Heads
dmodel
# of Parameters (B)
PaLM-62B
decoder-only encoder-dencoder
64
32
8192
62.50
UL2-20B
32 32/32
32
16
4096
19.46
OPT-30B
decodcr-only
48
56
7168
29.97
Codex"
decoder-only
96
96
12288
175.0
Question:
where did united states drop the atomic bomb
The answer to the above question can be found in the following Wikipedia page, section, and paragraph table:
During the final stage of World War the United States detonated nuclear weapons over the Japanese cities of Hiroshima and Nagasaki August 6 and 9, 1945, respectively. The United States dropped the bombs after obtaining the consent of the United Kingdom, as required by the Quebec Agreement. The two bombings killed at least 129,000 people, most of whom were civilians. They remain the only use nuclear weapons history of warfare.
Answer:
Question: who sells the most oil in the world
The answer to the above question can be found in the following Wikipedia page, section, and paragraph table:
The top three producers have recent history been (alphabetically) Russia, Saudi Arabia, and the United States. Each of these countries experienced major production declines at different times in the past, but since 2014 all three have been producing near their peak rates of to 11 million barrels per day. Saudi Arabia and Russia also top the list of oil exporting countries, while Russia is not member of OPEC. The monthly U.S. production reached 10.07 million bid in November 2017, the highest monthly level of crude production U.S. history. early 2018, U.S. government forecast the country will become net gas exporter 2022, the first time since 1953.
Figure 7: Two sampled evidence-recitation exemplars in Natural Questions (NQ) dataset. Notice that NQ, we can directly use the "long answer" annotation as the recitation demonstrations.
17

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Published as a conference paper at ICLR 2023
Question:
when did day light savings start in the
Recitation:
In the UK, the first Daylight Saving Time was first proposed by William Willett, an English builder and Liberal Party politician, in 1907, and after public consultation
proposal was accepted 1908. The Daylight Saving March 1916, and the second 27 March the following year. It repealed May 1919. but reintroduced in May 1919.
UK was introduced on
Daylight saving time, also known as daylight savings, and several other variants, is practice of advancing clocks during summer months by one hour so that evening daylight lasts one more
Recitation:
Usually, clocks are adjusted forward hour the spring and adjusted backward in autumn. Historically, the time change intended conserve energy by making better use of the available daylight the equinox.
Daylight savings time in the United States is the practice of advancing clocks during summer months so that evening daylight lasts longer, while sacrificing normal sunrise times. practice allows people have more evening daylight and less morning daylight, which allows for productivity and leisure.
Recitation:
The United States began Daylight Saving Time in the year 1918, putting in effect a nationwide summer time arrangement, which had been put forward by the National Defense Act of 1916. Originally, Daylight Saving Time was started the United States to save energy and reduce practice, but such the United States, observe it regional basis..
Recitation:
of living. Currently, many
have discontinued the
Figure 8: Four recitation passages sampled from UL2 for the same example question from Natural Questions dataset.
Question: Triggered by Rosa Parks' refussal to give up her seal, the public transportation system what US city by a year long boyoott their busses?
The answer (he above question be found following Wikipedie pege, section. and peragraph:
Anawer: On December 1, 1955, in Monigomery, Alabama, Parks rejected bus driver James F. Blake's order to fille..
four the "colored" section fovar of White passenger, "Whitee section
Question:
What was Beathoven's last symphony?
answer the above question found following Wikipedia page, section, and paragraph:
Anawer The year 1823 the completion of some years, namely the Missa solemnis, the Ninth Symphony and Diabeli Variations.
of which occupled Beethoven
Question: In which 1972 John Boorman film ls leading cheracter, played by Ned Beatty, raped by s 'Hillblly?
The anewer to the above question can be found following Wikipedia page, section. and peragraph:
1972, Beatty Reynolds, and set in northern Georgia. Beatt's character is foroed to strip at gurpaint by two mountain men who humiiate and him, scene shocking that still referred screen milestone.
as Bobby Trippe Deliverance, starring Voight
Question: Which crossing The River Thames did Queen Elizabeth open 17th March 1973?
The answer to the above question can be found in the following Wikipediia section, and paragraph:
Answer Queen Elizabeth II of the United Kingdom opens the new London Bridge.
Question: "The song "My Kind Of Town"", written Sammy Cahn and Jimmy Van Housen in 1984, was about which
The anawer the above question be found the following Wikipedie section, and peragraph:
Anawer: "My Kind of Town' made minor eppearance on the U.S. pop charts, reaching #110 in 1964. was the second charting songs about Chicago recorded Sinatra. The other "Chicago (That Toddlin' Town)" from 1957, which reached U.S. #64.
Figure 9: The 5-shot prompt used for performing evidence-recitation TriviaQA dataset.
18

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Published as a conference paper at ICLR 2023
Question: Which physicist's principle asserts that momentum & position particle cannot both be precisely determined the time?
Recitation: In 1935, Heisenberg (along with his assistants Walther Gerlach and Walther von Braun) published a paper in which they described a "principle of uncertainty" that would later be dubbed the Heisenberg uncertainty principle. The principle asserts the momentum and position of particle
both precisely determined.
Recitation: One of the consequences of the principle of special relativity that the position and momentum of a moving object cannot both be measured simultaneously with high precision.
Recitation: The uncertainty principle states that impossible momentum and the exact position of object (particle) at the same time, that is, the momentum and position are incompatible and, therefore, complementary. The uncertainty principle is one of the fundamental results of quantum mechanics, and is often regarded as one of its most surprising consequences.
both the exact
It states that following statements cannot both the position particle cannot be both precisely determined and known exactly, and the momentum of particle cannot be both precisely measured and known exactly.
Recitation:
Figure 10: Four recitation passages sampled from UL2 for the same example question from Trivi- aQA dataset.
Triggered by refusal give up what US city was devastated by a year long boycott of their busses?
public transportation system
Montgomery, Al
Q: What was Beethoven's last symphony?
which 1972 Boorman film leading character, played by Ned Beatty, raped by a 'Hillbilyy?
A: Deliverance
Q: Which bridge crossing The River Thames did Queen Elizabeth 17th March 1973?
London Bridge
Q: "The song "My Kind Of Town"", written by Sammy Cahn and Jimmy Van Heusen in city?"
1964,
Chicago
Figure 11: The 5-shot prompt we used for performing question question-answering on TriviaQA dataset.
19

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Published as a conference paper at ICLR 2023
Queston: Which magazine starled first Arthur's Magaazine First for Women?
The the question found following Wikpeddaa page, paragraphs:
Answer Arthur magazine bi-monthly perfodical that founded October publisher Laris Kreslins and editor Babcock.
Answer 2: First for Women is woman's magazine published by Bauer Media Group in the USA. The magezine was started in 1989.
family part company that head city?
question can
following Wikipedia page, section, paragraphs:
Answer Oberoi the current chairman The Oberoi Group.
Answer Oberoi Group an award-winning luxury hotel group with office New Delhi, India.
Questin: What nationality James Henry Miller's wife?
The the above question found in following Wikipedia page, section, and paragraphs:
Answer 1967, Miller married his fifth wife, Japanese barn singer Hoki Tokuda.
Answer Haki Tokuda Japanese known Blind Woman's Curse (1970), The Abalone Girls (1985) Nippon Paradise (1964).
the question that Anubis" based first year?
Dutch-Belgian
following Wikppedia page, section, paragraphs:
mystery television developed
Belgian
Huis Anubie.
Answer 2: Het Huis Anubis (English: The House of Anubis) Dutch-Belgian children's television mystery drama. It first aired September 2006.
Figure 12: The 4-shot prompt we used for performing multiple-evidence recitation on HotpotQA dataset.
Q: Which magazine started first Arthur's Magazine First for Women?
A: The answer is Arthur's Magazine.
Q: The Oberoi family part of a hotel company that has a head office in what city?
answer Delhi.
Q: What nationality James Henry Miller's wife?
A: The answer is American.
Q: The Dutch Dutch-Belgian television series that "House of Anubis" was based on first aired in what year?
2006.
Figure 13: The 4-shot prompt used for performing question-answering on HotpotQA dataset.
20

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Published as a conference paper at ICLR 2023
Question: Are Local and Against both from United States?
Answer 1: Local is an American band from San Diego, California.
Answer For Against is an American punk band from Los Angeles, Califomia.
Answer 1: Local is a rock band from Los Angeles, California.
Answer For Against is an American rock band from Phoenix, Arizona.
Answer 1: Local is American rock band from Austin, Texas.
Answer 2: For Against is a rock band from Nashville, Tennessee.
Answer Local is American rock band from Chicago, Illinois,
Answer 2: For Against is an indie rock band from New York City, New York,
Figure 14:
Four recitation sampled from UL2 for the same example question from HotpotQA dataset.
Q: Which magazine was started Arthur'ss Magazine or First Women?
A: Arthur's Magazine started in 1844. First Women started 1989. Arthur's Magazine was started first. The answer Arthur's 's Magazine.
Q: The Oberoi family is part of hotel company that has head office what city?
A: The Oberoi family is part of the hotel company called The Oberoi Group. The Oberoi Group has its head office in Delhi. The answer is Delhi.
Q: What nationality was James Henry Miller's wife?
James Henry Miller's wife is June Miller. June Miller is American. The American.
Q: The Butch-Belgian television series that "House of Anubis" was based on first aired in what year?
A: "House Anubis" based on the Biech-Belgian television series Het Huis Anubis. Het
Huis Anubis is firstaired September 2006. The answer is 2006.
Figure 15: The 4-shot prompt used for the Chain-of- ofain-of-Thought (Wei al., 2022) baseline HotpotQA dataset. The prompt is taken from (Wang et al., 2022a).
21

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Published as a conference paper at ICLR 2023
Question: Were Scott Derrickson and Ed Wood of the same nationality?
Ground-Iruth
Answer (slandard prompting): American
(chain-af-thought path-1): Scott American.
American.
Answer (chain-of-thought path-2): Scott Derrickson is an American. Ed Wood is an American. So Scott Derrickson and Ed Wood are same nationalily. The answer yes,
Answer chain-of-tthought path-3): Scott Derrickson and Ed Wood were of the same nationality, Both Scott Derrickson and Ed Wood were American. The answer is Yes.
Answer (recite-and-answer path-1):
Answer 1: Scott Dericcksn an American screenwriter, director and producer.
Answer 2: Edward Wood (October 1924 December 10, 1978) American filmmaker, actor, and author, The answer is American.
Answer (recite-and-answer path-2):
1: Scott Derrickson American film director, screenwriter and producer.
Answer 2: Edward Davis Wood, Jr. (October 10, 1924 December 10, 1978) was American filmmaker, actor, writer, producer, and director.
The answer is yes.
Answer (recite-and-answer path-3):
Answer 1: Scott Derrickson (born July 16. 1966) is an American film director, screenwriter and producer. Answer 2: Edward Davis Wood Jr. (October 10, 1924 December 10, 1978) was an American filmmaker, actor, writer and producer.
The answer is
Figure 16: Qualitative comparisons between standard prompting, Shain-of-thoughh and recite-and- answer on HotpotQA evaluation example (I).
22

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Published as a conference paper at ICLR 2023
Question: The director of the romantic comedy "Big Stone Gap" is based in what New York city?
Ground-truth Answer: Greenwich Village, New York City
Answer (standard prompting): New York City
Answer (chain-of-thought path-1): Adriana Trigiani the director of the romantic comedy Big Stone Gap. Adriana Trigiani is based in New York City. The answer is New York City.
Answer (chain-of-thought path-2): The director the romantic comedy "Big Stone Gap" is Adriana Trigiani. Adriana Trigiani based New York City. The answer is New York City.
Answer (chain-of-thought path-3): The director of "Big Stone Gap" is Adriana Trigiani. Adriana Trigiani lives in Manhattan. The answer is Manhattan.
Answer vrectte-add-assee path-1):
Answer Big Stone Gap is 2014 American romantic comedy film directed by Adriana Trigiani. Answer Adriana Trigiani is an American author, playwright, filmmaker, and entrepreneur. lives Greenwich Village, New York City.
The answer is Greenwich Village.
Answer (recite-and-answer path-2):
Answer Big Stone Gap is American romantic comedy film directed by Adriana Trigiani and written by Trigiani and Andrea Berloff.
Answer Adriana Trigiani is an American bestselling author, filmmaker, and playwright. She lives Greenwich Village, New York.
The answer Greenwich Village.
Answer (recite-and-answe) path-3):
Answer 1: Adriana Trigiani is an American writer and filmmaker.
Answer 2: Big Stone Gap is 2014 American romantic comedy film written and directed by Adriana Trigiani, based her 2000 debut novel of the same name. The answer New York City.
Figure 17:
Qualitative comparisons between standard prompting, answer on HotpotQA evaluation example (II).
and recite-and-
23


Reviews:
[{"review_id": "yVg2-6kjkFI", "reviewer": "Reviewer_9YM6", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "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 proposed a LLM-based method to improve accuracy in factual knowledge generation. Instead of retrieving from external resources, the proposal trains a model to recite relevant knowledge given a query and subsequently answer the query conditioned on the recitation. The proposal is empirically shown to be effective on the closed book question answering task, with some ablation and analyses on various model settings.", "summary_of_the_review": "The paper is of overall good quality, with a sound idea and good execution by putting many moving parts together into a working system with empirical gains. The analysis part is a bit unclear and underwhelming.", "strength_and_weaknesses": "Strengths\n- The proposed method is intuitive and well motivated.\n- Evaluation is sensible and yield positive results both wrt performance and data/resource efficiency.\n- Writing is mostly clear.\n\nWeaknesses\n- The analysis section on what works and why is a bit thin; Table 5 overall is quite confusing. I believe answering the following questions  may help add more clarity and strength to this part.\n  - What does Hits mean? What's the difference between @1 and @20? What is Hits@1 (no explanation in text)? What is (EM) following Hits@1? I'm guessing that's the subset of examples that the model answers correctly?\n  - If I'm guessing correctly, the definition of Not Recit, hits@20-recit., and hits@20-path follows a binary decision tree, on whether the ground truth answer appears in any recitation or any QA output. It is rather difficult to figure this out based on the text in 4.3.4. Maybe a logical tree of some sort can help improve readability here.\n  - Are there cases where, e.g., the ground-truth answer is not recited but the model still answers the question correctly? E.g., a more detailed characterization of Hits@1 would be interesting, unless reciting the gt answer is an empirical necessity – which by itself is interesting to point out if true.\n  - How are the 1024 examples selected?\n  - On a more minor note, the names for categories are very not self-explanatory and could be improved for better readability.\n- There are some missing details that might slightly hurt readability\n  - Judging from Fig 2-(1), <Recitation N> is generated from – and directly related to – <Question N>, with other Q-R pairs as in-context examples. Moving on to Fig 2-(2), it seems there are multiple QA pairs (Q0, A0, ...) inserted between <Recitation N> and <Question N>. Are the questions in these QA pairs the same as those in the QR pairs in the top-left block of Fig 2? Are they somehow related to <Question N>? If not, is there going to be some long-distance memory issues between <Recitation N> and <Question N>?\n  - In Fig 2-(3), are the multiple paths (containing Recitations-dots-Answers) repeating Fig 2-(2)? \n  - In the multi-hop setting, do the numbered prompts like \"Recitation 1\" and \"Recitation 2\" naturally give rise to generating recitations of different topics, or does one have to train/update the LM to solicit such behaviour?\n  - \"Since the multiple recited passages are generated in one-pass from the LLM decoding sequentially, ...\" How to use multiple numbered prompts to generate multiple recitations in one-pass sequentially?\n  - I'm personally curious about more details on the generated recitations, e.g., in NQ dataset, do the recitations resemble the \"ground truth\" passages in number of words/sentences/paragraphs? What percentage of recitations contain the correct answers?\n- Minor points on motivations\n  - One motivation mentioned for recitation is to match the form of causal LM pre-training objective (with the decimal of $\\pi$ example). Personally, I tend to believe that recitation helps by making certain knowledge more explicit (value of $\\pi$), and when primed on this explicit piece of information (in the form of recitation), the LM is then more likely to arrive at the correct answer (the 10th decimal of $\\pi$). In other words, I'd argue that with or without recitation, the objective remains the same (i.e., masked or next-word prediction), so this motivation to me feels a bit mis-directed.\n  - \"Human's ability to recite relevant factoid knowledge before answering knowledge-intensive questions.\" Do humans have this ability? Regardless of the answer, having this ability doesn't necessarily help humans answer knowledge-intensive questions more accurately, which I guess should be the true human-inspired motivation for the proposal. In any case, citations are perhaps needed to make either claim.", "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": "Not applicable"}, "extra_text": {}}, {"review_id": "n2hVWdUjBd6", "reviewer": "Reviewer_HxiC", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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 new prompt engineering algorithm for few-shot open-domain question answering with pretrained language models. The key idea is that instead of asking language models to directly generate answers to factoid questions, ask it to first generate a paragraph of text which will contain the answer (\"recitation\"). After recitation is complete, language models are instructed (via prompt examples) to use their recitation to answer the question. The paper additionally adopts several tricks to improve the performance of their system, including \"self consistency\" (generating through multiple recitations and taking majority vote), multiple recitations (for multi-hop questions). Finally, the authors explore fine-tuning a model to generate diverse recitations via passage hints. The authors use synthetic data generated by the frozen LM to achieve this goal.\n\nThe authors conduct experiments on Natural Questions, TriviaQA and HotpotQA, and find performance improvements over standard prompting techniques.", "summary_of_the_review": "The paper has interesting ideas and surprising results, but I have two main concerns - (1) the paper does not evaluate the method on larger LMs which are available; (2) I don't think there's justification that this method is a replacement for retrieval in any way (weakness #2, #3).\n\nI am currently leaning reject, but will be happy to move to the accept range if weakness #1 is addressed via experiments on GPT3-170B and InstructGPT3-170B.\n\n------\n\n**After rebuttal**: Thanks to the authors for the very detailed response! I've decided to raise my score to 6 (accept range) due to the improvements shown on Codex. I would still suggest the authors to take a more balanced take in their conclusion, mentioning that while there are improvements over direct generation, there is still a gap behind retrieval-augmentation on NQ.", "strength_and_weaknesses": "**Strengths**\n\n1. This is an interesting idea, and an exciting way to incorporate the ideas of \"chain-of-thought\" prompting and \"self-consistency\" for open-domain QA tasks where the answer is a factoid.\n\n2. The authors observe 2-6% improvements over standard direct prompting across all 3 tasks / 3 models. This is quite good and surprising for me --- I had thought the model would be able to answer the factoid question directly if it's able to generate a much longer paragraph containing the answer. It is interesting that the method requires multiple recitation paths to work (in Figure 4 performance is lower than standard prompting with just one recitation path). However, I think of this as a strength of the proposed method, since you cannot really use multiple paths if you are generating a direct answer (since the answer is so short, sampling doesn't make sense).\n\n3. The authors perform several insightful analysis experiments discussing robustness to prompts, comparison to BM25 retrieval, and an error analysis.\n\n**Weaknessess**\n\n1. The paper would be much stronger with experiments on GPT3, Instruct-GPT3 (davinci-002), and larger language models (larger in-house LMs?). It's not really clear from the paper whether recitation helps with larger scale, which I think is important for the generalizability of the method [1]. This could work both ways --- I'm suspecting larger LMs will be better at both recitation and directly performing QA. I think experiments on InstructGPT [4], T0 [3] or FLAN [2, 7] will be especially interesting, since it's been fine-tuned on instructions / examples / human preferences.\n\n2. A major advantage of retrieval augmented systems is their applicability on (1) tail distribution information; (2) generalization to information which was not present in the model's training set (like COVID for BERT). I think these are important limitations of the proposed method, and (1) is not really discussed (2 is just mentioned in the conclusion). Are most of the correct recitations cases which were seen often during training?\n\n3. Overall, the performance of closed-book models in this paper seems to significantly lag behind recent few-shot retrieval-augmented systems [5, 6]. For instance, ATLAS [5] gets 42% on NQ with 64 examples and a smaller model, while the best number in this paper is 32% (5-10x larger model). While I agree that setting up retrieval is technically cumbersome, there are very good retrieval APIs available, which were utilized in [6] without any extra LM fine-tuning. Note that I do think it's incredible that closed book LMs are doing so well, but practically (from a performance stand-point) it may be better to just retrieve some text from the web rather than ask an LM to generate it with few-shot examples. Also, retrieval augmented LMs often have lesser parameters [5], so it's unclear which is a better method from an efficiency perspective.\n\n4. I have mixed thoughts about the passage hints fine-tuning experiments, since it requires fine-tuning a large LM on Wikipedia data. Perhaps the performance gains are because of the dedicated fine-tuning on Wikipedia data for the recitation LM model (which makes it overfit to Wikipedia)? Did you remove the passages from the test set questions while doing this fine-tuning? Also I don't think enough experiments are done in the paper to justify its added complexity over vanilla LM-Recitation. I would suggest moving it to the appendix, or performing experiments on all 3 datasets / models to show its benefit.\n\n[1] - https://twitter.com/_jasonwei/status/1526589104758042624  \n[2] - https://arxiv.org/abs/2109.01652  \n[3] - https://arxiv.org/abs/2110.08207  \n[4] - https://arxiv.org/abs/2203.02155  \n[5] - https://arxiv.org/abs/2208.03299  \n[6] - https://arxiv.org/abs/2203.05115  \n[7] - https://arxiv.org/abs/2210.11416\n\n**Minor**\n\nThis paper is relevant to https://arxiv.org/abs/2004.05483 and https://arxiv.org/pdf/2110.08387.pdf, it would be great to cite them.\n\nIn Table 4 (LM-Recitation_5), why is the number for different from Table 1 (Recite and answer)? (16.34 in Table 1 vs 14.16 in Table 4)", "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": "U_DYqQ8Okrn", "reviewer": "Reviewer_rRR1", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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 authors utilize LLM to first retrieve relevant information from LLM’s own memory (by sampling), then accomplishing more accurate factual generation to produce final outcomes. The core idea is motivated by the intuition: recite-step (retrieval) that recollects relevant knowledge pieces helps answer-step (generation) better output to knowledge intensive questions. Two models are provided:\n\nFor the prompt-based model,\n(a) It first performs in-context learning for generating recitation provided with few-shot question-recitation examples.\n(b) Then it performs another in-context learning to generate the right answer provided with the corresponding recitation and question as well as the question-answer pairs\n(c) For a given question, the model samples multiple recitations (by (a)) and greedy-decode the answers (by (b)) for each sampled recitation.\n(d) The model could do multi-hop reasoning by sequentially generating recitation (by simple number prompting) till the special tokens occurs, then finding the answer based on chain-reasoning.\n\nFor the passage-hint model,\n(a) For each question-answer pair, prepare several Wikipedia pages that are ranked at the top (from a search engine) for the question query. \n(b) Create the corresponding passage hint by concatenating hierarchical title orders from the topic and section/subsection titles to the position of the paragraph in the (sub)section. \n(c) Given evidence-question pairs, perform in-context learning to sample a new question given an evidence passage.\n(d) Learn a fine-tuned LM that outputs passage hint (from (b)) as well as evidence passage (from (c)) given a question input.\n(e) Given a question, fine-tuned LM first samples multiple passage hints and passage evidence (by (d)). \n(f) Aggregate multiple passage evidences (in (e)) as a diverse recitation to the given question, then generating the answer with a few-more question-answer demonstrations in a frozen language model.\n\nThe authors verify that the recite-and-answer method is effective for Closed-Book Question-Answering tasks being compatible with other useful approach that improve few-shot in-context learning capability.", "summary_of_the_review": "(1) As the passage-hint model is a corpus-specific treatment that additionally utilizes title hierarchies, the prompt-based model must have great added benefits. The core concern is that the model performs in-context learning based on the model-generated recitations. No matter how you tune the temperature parameter, there must be a pathological trade-off between sacrificing diversity and sacrificing factuality. The current draft does not address impacts of less-accurate or incorrect recitations. \n\n(2) It will be great to have the performance graph (given two metrics) over increasing number of few-shot prompts. If you could approximately measure the correctness of generated recitations, another figure that demonstrate the performance dependency with respect to the number of correct shots will benefit readers.\n\n(3) Another major concern is the computational trade-off from using many shots. Table 1 shows that the proposed recite-and-answer model (with 5 shots) does not have significant impacts against standard-prompting (with more shots). Can you address the claim that using the standard-models with more shots is more expensive than running your prompt-based model with less-shots?\n\n(4) It is less clear that how you decide the number of shots you would use for standard-prompting model and your recite-and-answer model. For example, UL2-20B is tested with 16 shots, whereas the in-house version used 64 shots. Justify the rationale of picking the number of shots and the reason not to test on more shots for OPT-30B.\n\n(5) Excluding UL2-20B (mostly encoder-decoder T5 structure except the mixture of denoiser variations with multiple special tokens), is your In-house model of decoder-only? If so, why In-house has significant performance gain on HotpotQA but no visible boost on TriviaQA, which is the entirely opposite to OPT-30B? Any specific example-based explanation than a conjecture would be beneficial.\n\n(6) The passage-hint model consists of significantly more steps. It will be useful to see overall time-cost trade-off between your model and standard models.\n\n(7) Is the passage-hint model flexible if the evidence documents do not have a clear and fine-grained title/subtitle structures? What if some of title components from this hierarchy is missing? Should we expect performance degradation or performance boost because the passage hints are also generated being possibly incorrectly if the information structure is too granular.\n\n(8) Overall, not much qualitative examples are provided. Having some successful and failure cases by examples will be highly \n", "strength_and_weaknesses": "(Strengths)\n\n(1) Intuitive ideas\n\n(2) No need to retrieve documents.\n\n(3) Compatible with other approaches like chain-of-thought prompting.\n\n\n\n(Weaknesses)\n \n(1) Major concerns that will be listed below.\n\n(2) Unclear cost for running the proposed methods against the standard-prompting models.\n\n(3) Insufficient rationale to support gains and no gains ", "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": "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": "1QIXsrDvxJ_", "reviewer": "Reviewer_m3Mw", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "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 authors presents RECITE, a novel prompting Language Model (LM) paradigm to improve know knowledge-intensive NLP tasks. The main idea is to first let the language model to generate knowledge via prompting (knowledge-recitation) and then to let the language model solve the task (task-execution). The author evaluate the performance of the proposed approach with three close book QA tasks (Natural Questions, TriviaQA, and HotpotQA) and three LMs (In-house LM (62B), UL2 (20B), and OPT (30B)). The results shows better performance than direct prompting and chain-of-thoughts, and comparable performance to retrieval methods (BM25).", "summary_of_the_review": "The paper propose a two step approach to improve knowledge-intensive NLP tasks by using only LMs (no external retriever).  However the model performance are a bit lagging compare to simple BM25 + LMs, plus the clear weakness of updated knowledge.", "strength_and_weaknesses": "Strength\n- Retrieval free QA model are an important topic to study, and RECITE enable midsize LMs to achieve competitive performance in the close-book QA. \n\nWeaknesses\n- As also mentioned is Limitation, the main drawback is the the updated of the model with new knowledge. This is even more evident when compared to simple DB retrieval in Table 5, where BM25 performs better that LM-Recitation 5-shot with 20-path (the best proposed model). \n\nQuestions/Suggestion:\n- What is the size of the index + docs (compressed) of BM25? is the size larger or smaller of the model parameter? if the size of the DB is larger of the model parameter, why not constrain it (e.g., randomly removing articles) and check if the performance in Table 5 changes? \n- What is the inference speed of LM-Recitation K-shot with t-path? How's compared to BM25? \n- Does scale matter? what's the performance of larger LM (PaLM-500B or GPT-3/Instruct-GPT3) or smaller (GPT-J 6B)?  \n- Figure 4 shows the performance difference by sampling more self-consistency paths, but what append if the model samples more responses for the direct method (or chain-of-thoughts)? ", "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": "Not applicable"}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster