SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
Authors: Jaehyung Kim
Jaehyun Nam
Sangwoo Mo
Jongjin Park
Sang-Woo Lee
Minjoon Seo
Jung-Woo Ha
Jinwoo Shin
Venue: ICLR 2024 poster (2024)
Keywords: question answering
large language model
retrieval
Primary area: generative models
TL;DR: We propose a simple framework to improve ODQA accuracy of LLM, by generating conditional summarizations of retrieval and evaluating them with carefully desinged prompts.

Abstract: Large language models (LLMs) have made significant advancements in various natural language processing tasks, including question answering (QA) tasks. While incorporating new information with the retrieval of relevant passages is a promising way to improve QA with LLMs, the existing methods often require additional fine-tuning which becomes infeasible with recent LLMs. Augmenting retrieved passages via prompting has the potential to address this limitation, but this direction has been limitedly explored. To this end, we design a simple yet effective framework to enhance open-domain QA (ODQA) with LLMs, based on the summarized retrieval (SuRe). SuRe helps LLMs predict more accurate answers for a given question, which are well-supported by the summarized retrieval that could be viewed as an explicit rationale extracted from the retrieved passages. Specifically, SuRe first constructs summaries of the retrieved passages for each of the multiple answer candidates. Then, SuRe confirms the most plausible answer from the candidate set by evaluating the validity and ranking of the generated summaries. Experimental results on diverse ODQA benchmarks demonstrate the superiority of SuRe, with improvements of up to 4.6\% in exact match (EM) and 4.0\% in F1 score over standard prompting approaches. SuRe also can be integrated with a broad range of retrieval methods and LLMs. Finally, the generated summaries from SuRe show additional advantages to measure the importance of retrieved passages and serve as more preferred rationales by models and humans.

Published as a conference paper at ICLR 2024
SURE: SUMMARIZING RETRIEVALS USING ANSWER CANDIDATES FOR OPEN-DOMAIN QA OF LLMs
Jachyung Kim!.* Jachyun Nam Sangwoo Mo Jongjin Park²
Sang- Woo Lee Minjoon Sco" Jung-Woo Ha Jinwoo Shin²
Carmegie Mellon University KKAIST AI University of Michigan *Naver Al Lab Naver Cloud
ja jaahynn 4 @andrew...... cmu. edu
ABSTRACT
Large language models (LLMs) have made significant advancements in vari- ous natural language processing tasks, including question answering (QA) tasks. While incorporating new information with the retrieval of relevant passages is a promising way to improve QA with LLMs, the existing methods often require additional fine-tuning which becomes infeasible with recent LLMs. Augmenting retrieved passages via prompting has the potential to address this limitation, but this direction has been limitedly explored. To this end, we design simple yet effective framework to enhance open-domain QA (ODQA) with LLMs, based on the summarized retrieval (SURE). SURE helps LLMs predict more accurate an- swers for given question, which are well-supported by the summarized retrieval that could be viewed an explicit rationale extracted from the retrieved passages. Specifically. SURE first constructs summaries of the retrieved passages for each of the multiple answer candidates. Then, SURE confirms the most plausible an- swer from the candidate set by evaluating the validity and ranking of the generated summaries. Experimental results on diverse ODQA benchmarks demonstrate the superiority of SURE, with improvements of up to 4.6% in exact match (E.)) and 4.0% in F1 score over standard prompting approaches. SURE also can be inte- grated with broad range of retrieval methods and LLMs. Finally, the generated summaries from SURE show additional advantages to measure the importance of retrieved passages and serve as more preferred rationales by models and humans.'
INTRODUCTION
Large language models (LLMs) (Brown et al., 2020; Touvron et al., 2023b) have significantly accel- erated progress in natural language processing (NLP) and have become a core technology in various real-world applications used by millions of users, such coding assistants (Chen al., 2021), search engines (Xuan-Quy et al., 2023), and chatbots (Kim et al., 2021; OpenAl, 2022). However, LLMSs often suffer from limitations, such non-factual but seemingly plausible generation, referred to as hallucinations (Welleck et al.. 2020), and difficulty in integrating up-to-date knowledge, as their learned knowledge is limited by the training corpus encoded in their parameters (Guu et al., 2020). This problem is particularly critical for question answering (QA) (Kwiatkowski et al., 2019), one of the most frequently encountered applications for LLMss
Incorporating new information through the retrieval of relevant knowledge for a given query (e.g., a question from users) widely explored to improve the accuracy of QA systems, called open- domain QA (ODQA) (Karpukhin et al., 2020), and shows promise in addressing the aforementioned limitations L.MM (Mialon et al., 2023). Constructing these retrieval- eerrieeal-uugmentedd LLMMs typically involves additional fine-tuning (Borgeaud al., 2022; Izacard et al., 2023), but it becomes infeasible due to the increase in scale and the recent nature black-box API (OpenAI, 2023). Consequently, retrieval augmentation via prompting, i.e., giving specific instruction as the input to obtain the de- sired outputs by LLM, becomes an attractive direction from its simplicity and efficiency (Shi et al., 2023). However, naïve prompting could be limited in fully exploiting the retrieved contexts, since
This work is done when Jachyung Kim was in KAIST.
'The code is available at https: //gi thub. com /buu 9/ CCI22__SURe

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Published as a conference paper at ICLR 2024
LLMs are simply instructed to the retrieved information, instead of being explicitly trained to use it; for example, Liu et al. (2023b) recently observed that LLMs struggle to handle long input con- texts when they naively appended. Despite its importance, how to improve strieval-augmenter LLMs via prompting has been under-explored. Therefore, to improve ODQA via LLMs, we aim to develop simple yet effective framework based on prompting, that could be easily applicable to various LLMs and retrieval methods.
Contribution. We propose framework based on Summarized Retrieval (SURE), to improve ODQA performance of retrieval-augnan aarreete--ugmentted LLMs. At a high level, SURE helps LLMs predict more grounded answers, which are well-supported by the summarization of retrieved passages that could be viewed as an explicit rationale extracted from the retrieved passages. To be specific, SURE first constructs the multiple summarizations of retrieved passages conditioned on each of a few pos- sible answer candidates. It enables LLMs to focus on the specific contexts relevant to the given candidate, and hence provides more discriminative viewpoints for the given question. Then, using the generated summarizations, SURE confirms the most plausible answer among candidates by mea- suring the corresponding summaries' validity to support the given candidate and ranking of relative informativeness answer the question. Remarkably, all the procedures of SURE are conducted via zero-shot prompting. Consequently, SURE is widely applicable when LLMs are only accessible with black-box API, even without query-relevant few-shot examples.
Through the experiments on four different QA datasets, demonstrate the effectiveness of SURE for improving the zero-shot ODQA performance of etrrevallauugmented LLMs. For example, we ob-
ChalGPT Controwee (Sufl))
40
ChaRGPT Caninweee
ChaiGPT
Exact Match (EM)
ChaiGPT IM25
CharGPT (yo retrioaal)
LLaids (vo natrivaa)
serve that the augmentation of 10 relevant passages effectively improves QA accuracy (up to 8.2% with Contriver (Izacard et al.. 2022)) of ChatGPT (Ope- nAI, 2022), and the gain is significantly enlarged with SURE (up to 12.8%), as shown in Figure 1. Overall, SURE with ChatGPT and BM25 (Robert- son et al., 2009) exhibited 4.6% exact match (EM)/F1 score improvements compared to the stan- dard prompting in average four ODQA datasets. In addition, SURE is well generalized to differ- ent configurations of various retrieval methods and LLMs. More interestingly, we observe that the generated summarization by SuRE could be fur- ther utilized to evaluate the importance of the re- trieved passages, and also verify that it has a higher model/human preference as rationale for the given prediction, compared to the generic summarization of retrieved passages. Overall, these results highlight the effectiveness of SURE, to improve ODQA systems based on LLMs, not only in terms of accuracy but also of additional advantages that can improve the user experience. We, therefore, hope that the proposed framework could be beneficial in various real-world applications.
Glabd too restevll)
30
[no
20
200
400
500
Model Parameters (B)
Figure 1: Zero-shot QA accuracy with various LLMs on Natural Question (Kwiatkowski et al., 2019). The perfor- mances of LLaMA-33B, GLaM-62B, and PaLM-540B are from the corresponding papers, respectively (Chowdhery et al., 2022: Du et al., 2022; Touvron et al., 2023a).
2 RELATED WORK
Open-domain question answering. Open-domain question answering (ODQA) (Voorhees et al., 1999) is task that requires responding to factual questions using external knowledge sources (Zhu et al., 2015; Nagel, 2016). Recently, there has been significant research interest in ODQA systems, under a framework known as the retriever-and-ren system (Chen et al., 2017). The role of re- triever is to extract the relevant pieces of information from the given knowledge sources. For the retriever, there are two different popular methods: one a lexical-based retriever, e.g., TF-IDF or BM25 (Robertson et al., 2009), and the other is sentence embedding based retriever such as DPR (Karpukhin et al., 2020) or Contriver (Izacard et al., 2022). On the other hand, the reader is respon- sible for aggregating and reasoning with the retrieved information to generate answers. Usually, recent transformer based language models (LMs) such as BERT (Kenton & Toutanova, 2019) or T5 (Raffel et al., 2020) are widely adopted for the reader after fine-tuning. In contrast, L.MSs exhibit
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comparable performance outperform in QA without fine-tuning (Kamalloo et al., 2023; Shi et al,, 2023), which indicates a potential to serve as a universal QA system (Xuan-uuy et al., 2023).
Retrieval-augmeented language models. Similar to enhancing QA systems with retriever in ODQA, augmenting LMs with relevant information retrieved from external knowledge sources has been demonstrated as an effective way to improve the performance of LMS various NLP tasks (Guu et al., 2020; Lazaridou et al., 2022; Min et al., 2022; Liu et al., 2023a), by reducing hallucination of LLMs and leveraging external knowledge which is not seen during pre-training. To construct such retrieval-augmentel LMs. the standard approach is conducting additional fine-tuning to learn how to incorporate the retrieved information (Guu et al.. 2020; Borgeaud et al., 2022: Izacard al., 2023). However, when considering the recent nature of LLMs with increasing scale and providing black-box API only, such a direction becomes less attractive. One promising direction address this challenge is investigating a better prompting (Brown et al., 2020), which incorporates the retrieved information as additional inputs in a sophisticated way. However, this direction has been only lim- itedly explored. Appending the retrieval (Si et al., 2023; Trivedi et al., 2023) is common practice for prompting, but Liu et al. (2023b) recently revealed its limitation in utilizing the retrieved infor- mation. Aggregating the predictions from each retrieved passage has been also explored (Lazaridou et al., 2022; Shi et al., 2023), but LLMs can't It see a full context of retrieved information in this case. More discussions about the summarization of retrieval in open-domain context are in Appendix G.
SUMMARIZED RETRIEVAL FOR QUESTION ANSWERING
3.1 OVERVIEW AND PROBLEM DESCRIPTION
Overview. In this section, we present our framework, coined Summarized Retrieval (SURF) to enhance ODQA performance of L.Mss, by proposing an improved way incorporate retrieved pas- sages for the prediction. Our main idea is to construct multiple summaries of the retrieved passages conditioned with each of few answer candidates, and predict the most plausible candidate as the answer after evaluating the validity and relative informativeness of summaries. In Sections 3.2 and 3.3, we present the details to generate the summarizations and evaluate them. Figure presents the specific example of QA procedure via SURE.
Problem description. Open-domain question answering (ODQA) is an extension of QA tasks that answer questions that require background knowledge by leveraging an external database. In order to answer the given question g, the ODQA system typically follows retrieve-and read framework (Chen et al., 2017; Lee et al., 2019), where the retriever finds the informative passages C from the whole corpus C, and the reader exploits the retrieved passages to decide the answer a, which can be formulated as follows:
O = Retrieverrg C,N) and à = Reader C+t),
(1)
where N is the number of retrieved passages and â is the predicted answer.
In this work, we focus on improving prompting method for an LLM-based ODQA system. Specif- ically, we adopt the existing retriever method, egg., BM25 (Robertson et al., 2009) Contriever (Izacard et al., 2022), with the dataset-specific corpus. For the reader method, we use LLMs, de- noted by M, such as ChatGPT (Sun al., 2023) or LLaMA-2 (Touvron et al., 2023b), by incorpo- rating the retrieved passages via prompting (Brown et al., 2020) without additional training. For ex- ample, with a prompt p(q, x "Reading passages C. answer to question q", the prediction à is obtained from M, i.e., ā M (p(q, )+).
3.2 CONDITIONAL SUMMARIZATION OF RETRIEVED PASSAGES
To better exploit the retrieved passages with LLMs SURE first summarizes them conditioned on each of a few potential answer candidates. This conditional summarization of retrieved passages would include the specific contexts supporting a given answer candidate, compared to the generic summarization focusing on the wide coverage for the retrieved passages. Specifically, SURE first generates answer candidates and then conducts conditional summarization.
Candidates generation. Given a question q. retrieved passages C and LLM M. we first generate K answer candidates [V1 --. (-....., УK] using a prompt Pean designed for candidate generation from
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Query
The concept of mire finanding was developed by who in 1876?
Retrieved Passages
Titl:: Innovative financing, Text: Innovative financing Innovative financing refers to first mentioned and intraduced International Finencing for Development 2002.
ACORN International, Text: micro-tinance since has reduce poverty.
volunteers that
George Brown College (Toronto), Carieton University
1. Answer Candidates Generation
Candidates: Muhammad Yunus, (b) Grameon Bank
2. Conditional Summarization
Summarizatio Microfnance began experimenting... .. Therefore, can be concluded that the concept of micro financing was developed by Muhammad Yunus in 1976.
to financial services...
Muhammad Yunus, Bangladeshi aconomist,
Bummarization (b): Grameen Bank one the ploneers of microfinance, having been founded Bengladesh 1983 by Muhammad Yunus, the concept of micro finanding was not developed by Muhammed Yunus in 1976, but rather by the Grameen Bank in 1983.
3. Verification
Validity Summanization True, Validly Summarization for (b): False, Relative informaiienees:: (b)
Prediction
Therafore, answer is "Muhammad Yunus"
Figure 2: Example of QA with the proposed SURE framework. Given query question and relevant passages retrieved by an external method, egg., BM25 (Robertson et al., 2009), a large language model. e.g., ChatGPT, needs predict the answer. To improve this, SuRE first generates multiple answer candidates via prompting, and then conditionally summarizes the retrieved passages to sup- port each candidate. By comparing the validity and relative informativeness of summaries, SURE selects the most plausible candidate final prediction.
q and +
== (g,
(2)
In Figure 2, one can observe the example of generated candidates. It noticeable that the previous works utilized stochastic decoding to generate multiple answer candidates (Lazaridou et al., 2022; Weng et al., 2022). However, we empirically observe that explicitly prompting an LLM to generate K potential candidates outputs more diverse and high-quality candidates.
Candidate-conditiooned summarization. Next, conditionally summarize the retrieved passages Cj focusing on including the relevant contexts to validate each candidate Uh E y as an answer to qr
Sk M (peun(g, Cx. yx)) for le
where Peun is prompt obtain the conditional summarization 8j: from q, CN and ÿk- We present some examples of the generated summarizations in Figure 2, and more examples
Appendix B. Remarkably, the generated summarizations effectively reduce the given passages by focusing on extracting the candidate-relevant contexts (e.g.. 1035 words of retrieved passages + 93 words of summarization). Also, we verify that the contexts of the gener- ated summarization are specialized on a given answer candidate; when we measure TF-IDF (Chowdhury, 2010) based text similarity between two candidates and two conditional summarizations from each candidate (e.g., summarization #1 is generated to support an- swer candidate #1) on Natural Question dataset (Kwiatkowski al., 2019) in Figure 3, the summa- rization exhibits a higher similarity with the corresponding candidate than the other candidate.
TF-DF Stmtarty
Figure 3: TF-IDF overlap be- tween candidates and condi- tional summarizations.
3.3 SELECTIVE PREDICTION VIA VERIFICATION OF SUMMARIZATIONS
Then, using the generated summarizations, SURE confirms the most plausible answer among the candidate set for the prediction. Our key intuition is that the quality (e.g., factuality, logicality, and
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Algorithm SORE algorithm
1: Input: Large language model M, question N retrieved passages CN candidate number K'
2: Answer Candidate Generation: y = M (Pean (x)) y E1 .i..... ......,ra ØKl
3: Conditional Summarization: 8& M (Pennq (q, CN, Wkk) for == -=...... ...... K
4: Instance-wise Validation: v(sa) < Eq. 4 with M (Peat (g. 8k))
5: Pair-wise Ranking: r(sk: Sk), [pair(*k; 82) & Eq. with M (Prank (g. 8k, 8k, 8,))
6: Output: Prediction â yk*, , K* arg max, (s*)) + r(kk, Sk)
readability) of the generated summarizations would vary depending on the plausibility of answer candidates, so as more plausible the answer, the corresponding summarization also will be more plausible. Then, LLMs find the most plausible summarization among these multiple summa- rizations if proper evaluation way is given. To this end, we propose to evaluate the generated summarizations with instance-wise validity and pair-wise ranking among them.
Instance-wise validity. First, we evaluate the validity of each summarization sy whether it is not a degenerated case the provided passages are not enough support ÿkk, or it properly supports the given answer candidate Vk. rather than the other candidate Tr, i * k. To be specific, we measure a validity ug of each summarization 8ę using prompt Pval designed for the validation:
v(k)) when M (Pvaz (q. yk: 8k)) True v(s)) 0, else.
(4)
Pair-wise ranking. In addition, we evaluate how the given summarization 8% is relatively infor- mative to answer the question q, among all summaries SK = {s To this end, we measure ranking using paie-wise ranking prompts (Qin et al., 2023; Sun et al., 2023):
1,
M (Pranz (g, Sk: 8:)) 8%:
r(8k, Sk) = Σ
pair (8k, 81), Tpair (sk, 81) k
0,
M (pranx(g, 8k; 8:)) = 8:
(5)
0.5,
else
where Pranz is a prompt to determine which is relatively more informative one to answer the question by comparing two summaries. To prevent the order bias of LLMs (Zhao al., 2021), we query the same pair of summaries twice by changing their order at the prompt Prank.
Finally, SURE makes final prediction à by incorporating both v(sk) and r(sk, Sk):
ūk* k= arg max v(8k) + r(sk; Sk),
k
i.e. both validity and ranking scores are equally contributed. Algorithm summarizes the formal procedure SURE. We also highlight that the common prompts are shared across different datasets and LLMss, and the used prompts Pean; Paun; Pval, Prank presented in Appendix A.
EXPERIMENTS
In this section, we design our experiments to investigate the following questions:
o Does SURE improve the accuracy LLMs on various ODQA datasets? (Table 1)
o Is SURE generalizable across various retrieval methods and LLMs? (Table 2)
What is the effect each component SURE? (Table 3)
SURE's summarization a good rationale for the answer? (Table Figure 4)
4.1 SETUPS
Evaluation datasets. For all experiments, we measure zero-shot QA accuracy with the four different
ODQA datasets: (1) Natural Questions (NQ) (Kwiatkowski et al., 2019), (2) WebQuestions (WebQ) (Berant et al., 2013), (3) 2WikiMulti-hopQA. (2Wiki) (Ho et al., 2020), and (4) HotpotQA (Yang et al., 2018). For NQ and WebQ, we use their original test splits and 21M English Wikipedia dump
We present such failure Appendix D.
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Table 1: EM/ / Fl for different QA methods with ChatGPT on four QA datasets. N = 10 most
relevant passages are retrieved using BM25, except no retrieval. The best and second best scores are highlighted in bold and underline, respectively.
Methods / Datasets
NQ
WebQ
2Wiki
HotporQA
Average
No retrieval
27.6/ 27.6 39.0
25.0/ / 38.8
21.4 / 24.8
22.2 / 31.9
24.1 33.6
Base
38.4
19.6 32.5
27.4/ 32.8
30.8 / 40.3
26.6 36.1
Rerank
24.8 33.9
18.8/30.6
28.0/28.4
23.6 / 32.6
RePlug
26.0/35.3
18.8/31.5
23.6/ 28.5
28.0/37.9
24.1 / 33.3
Sclection-infernnce
24.3/
28.6
29.5
30.8 /9.6
23.8 / 32.6
Chain-of-thoughts
22.3/31.4
1
19.6/
25.6/31.8
20.7 / 28.4
Sell-verification
2
1
23.2/30.5
31.6/41 41.8
24.0 34.1
SURE (Ours)
42.3
32.8/38.
33.2 43.4
31.2/ 40.1
(Karpukhin al., 2020) as the source passages for the retrieval. For 2Wiki and HotpotQA, use the subsampled splits released by Trivedi et al. (2023), along with the corresponding corpus for each data. For the experiments with LLaMA2-chat (Table 2) and more analyses (Section 4.3), we took 500 randomly subsampled examples of NQ and WebQ datasets for efficient experiments considering limited computing resources, and denoted these datasets NQ" and WebQ", respectively. As evaluation metrics, we calculate the exact match (EM) and F1 score. The EM accuracy is the ratio of correct answers the test dataset, where a given prediction is considered correct if coincides with one of the gold answers. The Fl score measures the overlap between bags of tokens in the prediction and the gold answer. We normalize the predictions and answers (i.e., case-folded, and punctuation) to compute the metries, following the implementation of Rajpurkar et al. (2016).
Baselines. We compare SURE with the following baselines. (1) No retrieval answers the question with LLMs without the retrieved passages (i.e. closed-book setup). (2) Base appends the retrieved passages as additional inputs of LLMs via prompting. (3) Line of works for better exploitation of retrieved passages with LLMs: Rerank (Lazaridou et al., 2022) and RePlug adopt ensemble strat- egy that makes predictions based on each passage and then aggregates them with specific voting methods. Specifically, Rerank and RePlug utilize TF-IDF and sentence embedding from Contriever, respectively. (4) Adapt the works that incorporate intermediate reasoning steps for improved reason- ing with LLMs, summarizing could be viewed as a specific type of reasoning: Relection-inferee (Creswell et al., 2023) measures the ranking of the passages, and conducts interactive answering by adding the passages one by one starting from higher ranked ones. Chain-of thoughts thoughttttouuttss (Kojima al., 2022): we add zero-shot Chain-of-thoughts prompting (Wei et al., 2022) into the prompt of Base. Self-verification (Weng et al., 2022) generates answer candidates based on random sampling, then selects the most plausible one by verifying its reasoning with the question from conditional masking.
Implementation details. For the experiments, we use three recent ofate-of-thh--ar L.MMs: Chat- GPT 50ttrboo-oturro001111 (OpenAI, 2022), GPT-4 ccpt-4-0611 (OpenAI, 2023), and LLaMA2-chat-70B (Touvron et al., 2023b). We use a temperature of 0.0 when calling the API or greedy decoding for LLaMA, to remove the effect of random sampling (Sun al., 2023). For the retrieval methods, we use three different approaches: BM25 (Robertson et al., 2009), DPR-mult (DPR) (Karpukhin al., 2020), and Contriever (Izacard al., 2022). We use the implementations in Elasticsearch for BM25, and BEIR for DPR and Contriever, respectively. In the case of SURE, use the same prompts across the different datasets, and they are presented in Appendix A. Also, we use a fixed value of K = 2 during the experiments since we observe that the improvements by increasing K are limited, as shown in Appendix B. When there are multiple candidates with equal plausibility (Eq. 6), then SURE selects the one generated earlier in Eq. 2.
4.2
MAIN RESULTS
Table 1 summarizes the experimental results on four different ODQA datasets, under ChatGPT with N 10 retrieved passages using B.M25. First, augmenting the retrieved passages with prompting is effective in improving ODQA accuracies of LLMs. For example, the average EMM across four ODQA datasets is increased from 24.1 to 26.6. Somewhat surprisingly, we observe that Base outperforms
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Table 2: EM with different configurations of LLMs and retrieval methods on four QA datasets.
N = 10 most relevant passages are commonly retrieved. Fl scores are reported in Table 5. For LLaMA2-chat, we conducted experiments on NQ and WebQ" and the results are indicated by
ChaiGPT
GPT4
LLaMA2-char
Datascts
BM25
SURE
DPR
SURE
Contriever
+ SURE
BM25
SURE
BM25
SURE
NQ
28.4
33.5
36.1
41.0
35.8
40.4
30.2
32.4
18.6"
30.4'
WebQ
19.6
25.1
23.2
27.3
22.5
28.7
21.5
21.7
16.0"
24.0'
2Wiki
27.4
32.8
19.2
21.4
27.2
32.6
34.8
38.2
20.2
27.8
QA
30.8
33.2
25.6
27.4
32.2
33.6
34,8
40.6
24.0
28.0
Average
26.6
31.2
26.0
29.3
29.4
33.8
30.3
33.2
19.7
27.6
Table 3: Ablation and more analyses. EM / Fl with ChatGPT are compared on four QA datasets.
N 10 most relevant passages are retrieved using BM25. The best scores are highlighted bold.
Methods / Datasets
NQ
WebQ'
2Wiki
HotpotQA
Average
Base
29.4/41.7
/9,4/22,2
2 32.8
30.8 / 40.3
26.8 / 36,8
Conditional summarizations
30.4/40.9
20.8/33,
29.2 34.5
33.0/ 43,4
28.4 / 38.1
Pair-wise ranking
30.6/41.2
21.6/34.8
36.0/36.0
30.6 40.7
28.5 / 38.2
Instance wise validity (SURE)
34.6/
23.2/36.5
32.8/38.1
33.2 43,4
31.2 40.7
MCQ prompt
35.2/45.3
22.4/35.1
30.4/36.1
31.0/41.5
29.8 / 39.5
Sum-and-pred (Gen)
26.4/37.8
/9.8
25.6/ 32.3
33.8 43.3
27.3 37.1
other sophisticated baselines overall; this inefficiency of previous methods might be a result of a more challenging yet practical experimental setup. For example, we assume the zero-shot QA rather than few-shot setups, and also consider general black-bor box APIs for LLMs which do not provide the output probability. In contrast, one can observe that SURE successfully improves QA accuracy of LLMs by effectively exploiting the retrieved passages. In particular, SURE exhibits 4.6% ab- solute EM/F1 improvements in the average, compared to naively appending the retrieved passages.
We further demonstrate the compatibility of SuRE across various LLMs and retrieval methods. Specifically, in addition to ChatGPT and BM25 considered in Table 1, we run experiments on three different LLMs (GPT-4, and LLaMA2-chat) and two different retrieval methods (DPR and Con- triever). In Table 2, we compare EM metric of SURE with the baseline that simply appends the retrieved passages. Here, ODQA performance significantly depends the retrieval methods and types of LLMs; for example, using Contriever instead of BM25 makes 2.8% average EM improve- ments, and using GPT-4 instead of ChatGPT makes 3.7% average EM improvements, respectively. Overall, one can observe that SURE consistently improves ODQA accuracy regardless of types of LLMs and retrieval methods. with 4.6% average EM improvements. More interestingly. SURE suc- cessfully improves average EM scores of I.LaMA2-chat as 7.9%, a state-of-the-an open-sourced ILM,, which further indicates the practical usefulness of SURF as a simple yet effective solution for ODQA for the open source research community. The Fl results are presented in Appendix B.I.
4.3 ADDITIONAL. ANALYSES
In this section, conduct additional analyses of SURE. We conduct experiments using ChatGPT as an LLM, BM25 as retriever, NQ" and WebQ* as datasets.
Ablation and more analysis of SURE. First, we compare the following methods for the ablation of SURE: (1) Base: appends the retrieved passages to inputs, (2) Conditional summarizations: additionally appends all the conditional summarizations, (3) Pair-wise ranking: selects the sum- marization with only ranking (Eq. 4), and (4) + Instance-wise validity: selects the summarization with both ranking and validity, i.e., SURE. In addition, we consider two different methods to further analyze where the effectiveness of SURE comes from. (5) MCQ prompt: composes Multiple Choice Questions by generating the answer candidates via prompting (Eq. 2) and using them as possible choices for prediction by appending them to input prompt (Robinson al., 2023) (more details in Appendix A.7), (6) Sum-and-pred (Gen): instead of conditional summarization, it generates generic summarization and predicts the answer based on it. We present the results in Table 3.

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Published as a conference paper at ICLR 2024
Table 4:
Comparison as reranking method. EM/ / Fl with ChatGPT are compared on four QA
datasets. A single most relevant passage selected among N 10 passages retrieved by BM25. The best scores are highlighted in bold.
Datasets / Methods
NQ*
WebQ
2Wiki
HotpotQA
Average
BM25
12.6 18.8
9.0/ 17.6
14.8/ 18.1
21.8/ 28.3
14.6 / 20.7
Sent-encoder (q)
18.2 26.4
11,4/21.1
14.8 / 18.1
20.2/27.
16.2 / 23.2
LLM-rerank
20.0/28.4
18.2/21.3 14.2/24.4
26.0/
19.6 / 27.1
Sent-encoder (Gen)
21.2/31.7
13.6/25.3
17.8/2111
27.0/34.3
19.9 / 28.1
Sent-encode cent-encoder (SURE)
23.2/ 32.5
/5.4/
18.0 21.5
28.8 /6.7
21.4 / 29.7
Sultes Sanralario Generil Sueruratior
Seltis Aurrarsito
GPT-4 Exal Win Rate (8c)
Exant Match (EM))
30-
38n Date
25
nor lase
Numoer Ratrissala (N)
Datasels
Evilustion Muthods
(a) Different number of retrieval
(b) GPT-4 evaluation
(c) Human preference
Figure 4: (a) EM with different numbers of retrieved passages (N) under ChatGPT and BM25, (b) Comparison between SURE's summarization and generic summarization via GPT-4 evaluation (Liu al., 2023c). (c) Human preference between SURE's summarization and generic summarization on 84 samples of NQ", along with GPT-4 evaluation. More results are in Appendix C.
First, constructing conditional summarizations improves performance as they can extract special- ized contexts for given question and its answer candidates. Next, incorporating the evaluation on the instance-wise validity of each summarization significantly improves the performance com- pared to only considering the ranking among summarizations, as it enables more precise selection by adding the assessment regarding the relevance and coherence of the summarization in relation to the given question and prediction pair. Also, simple aggregation of generated answer candidates in the prompt shows improvement, which indicates the effectiveness of our generated candidates. However, this method becomes inefficient when the given question requires more complex reason- ing to answer. Lastly, using generic summarization effective in improving ODQA with LLMs by providing concentrated and brief contexts and addressing the difficulty from the long context (Liu et al., 2023b). However, the gain is significantly limited compared to SURE, which demonstrates that the key components of SURE are conditional summarization and comparison, rather than simply providing compressed contexts.
Different number of retrieval. Next, investigate the effect of the number of retrieved passages (N). Increasing N is one of the most intuitive ways to improve the performance of retrieve-and- read system by providing more extensive information (Karpukhin et al., 2020), and hence it is natural to expect that similar positive results could be observed with retrieval- aarreval-augmented LLMs.. However, on the other hand, its effectiveness could be limited LLMs could fail to handle long input contexts (Liu et al., 2023b). To verify the effect of different N on retrieval-augmenter LLMS using prompting, we measure EM of ChatGPT and BM25 with varied N. In Figure 4(a), we present the results of Base and SURE on NQ" and WebQ". First, we observe that the accuracy of retrieval- augmented LLMs significantly depends on N: when a small number of retrieved passages only available, the performance of Base could be even behind the performance without retrieval, as it restricts the prediction within the limited contexts. As N increases, its performance is increased and takes benefit from the retrieval system. With SURE, the accuracy of LLMs could be improved even with the small number retrievals (N 5), and it achieves better accuracy with larger N.
Effectiveness for finding important passages. In previous experiments, we mainly focus on demonstrating the effectiveness of SURE for improving QA accuracy. While the accurate answer is the most important feature of the QA system, providing the proper rationale for the answer is another important feature, especially in LLM-based systems for reliable usage by users such as search engines. One of the standard approaches for this is explicitly enumerating the most relevant
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Question
Answer In Time
What's the movie called with Justin Timberiake and time?
Generic Summarization
lussin Timberiace has had a ucceessi career not only in music but in film He has appeer os several moviss Including "The Sodell Natworkk" FEreens with Benelits", and Trolls However, there is one mavie that stands out as collaboration between Tiiberiake e and the Time Relcused ed
concept of
in future w where people stop aging
must work buy themseives
more time. Timberiake plays the lead role of Will Selas, man who is falsely accused edueed of murder and must go on the run with a h hostage,
played by Am anda Seyfried. The movie received mixed reviews but was praised ed for unique concept and Timberiske's performance Therefore, 'In Time" the movie that features Justin Timberiake and the concept time.
Candidate-conddiioned Summarization (Ours)
Justin Timberlate starred in the 2011 science fiction action fim 'In Time' alongsids de Amanda Seyfried. The film is sot future where stop aging must work to buy themseives more with becoming the new currensy. Timberlake plays es, man falsely accus ed of murder who goes an the run with a wealltty hoelage, played by Seyfried. The tw must nevigate dangerous world wher time everything and corruption rempant
received mixed revievs was preised unique concept and stylish visusls.
Figure 5: Qualitative comparison of candidate-conditionel summarization from SURE (Ours) com- pared to generic summarization a rationale for the answer. More examples are in Appendix B.
retrieved passages based on the specific scoring method, which is often called Re-ranking (Nguyen et 2016; Izacard et al., 2023). To explore the advantages of SURE in this aspect, we measure QA accuracy of ChatGPT augmented with the one passage considered to be most relevant with a specific reranking method within N = 10 originally retrieved passages with BM25. To extract such a reranking method for SURF, we use the cosine similarity between the sentence embeddings (Reimers & Gurevych, 2019) of the generated summarization and the retrieved passages, denoted by Sent- centeencoder (SURE). Then, we compare it with the following baselines for reranking: (1) BM25; original retrieval score, i.e., no reranking. (2) Sent-encoder (q): centence-encodee based rerank- ing using the similarity between retrieved passages and question (Nguyen et al., 2016), (3) LLM- rerank: LLM--bsed reranking (Sun al., 2023), and (4) Sent-encoder (Gen): sentence-encoder based reranking using the similarity between retrieved passages and generic summarization, The results are presented in Table 4. Here, we observe that all the reranking methods are effective com- pared to reranking. In addition, LLM-based reranking shows higher accuracy, while SURE's similarity- based reranking outperforms all the baselines, demonstrating the superiority of SURE.
Qualitative evaluation as rationale to answer. Lastly, we explore the additional benefits of SURE, which offers rationales to support the prediction. Specifically, we compare the summarization from SURE with the generic summarization, which is also generated by LLMs but with constraint of supporting specific answer candidates. To separately consider the quality as rationale with the ac- curacy of prediction, we only compare the samples correctly predicted by both SURE and Generic summarization used in Table 3; for example, it results in 84 remaining samples in the case of NQ". We first evaluate using GPT-4, which has been demonstrated to have high correlation with humans (Liu et al., 2023c). We present the results in Figure 4(b). Here, one can observe that the summa- rization via SURE more preferred by GPT-4; for example, Generic summarization wins 30.3% while SURE wins 37. 47.4% on average. It is also worth noting that the average length of both sum- marizations is similar (Generic: 600 vs SURE's: 570 average characters on NQ), therefore the bias of GPT to prefer the longer response (Wang et al., 2023) might limitedly affect the result. Next, we ask human evaluators which summarization is more informative and plausible to support the given question-answe we also observe a higher preference for SURE'S summarization (Generic: 26.9% vs SURE: 43.4%) Overall, these results reveal the potential of SCRE toward better ODQA system by providing high-quality rationale for the answer. Details on human evaluations are presented in Appendix C.
pair on 84 samples of NQ This result is presented in Figure 4(c). Here,
CONCLUSION
In this paper, we proposed SURE, a simple yet effective framework to improve ODQA accuracy of LLMs. Our key idea is to ensure the correctness predicted answers by constructing the summaries of the retrieved passages for the potential answer candidates and evaluating their validity and rank- ing. Our experiments demonstrate that SURE significantly improves ODQA performance of various aetrivall-uugmente LLMs, and also has additional advantages for measuring the importance of pas- sages and providing the rationale for prediction. From these advantages, we believe our framework can contribute to various real-worle applications and provide better experience to users.

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Published as a conference paper at ICLR 2024
ETHICS STATEMENT
We strongly believe that SURE can provide a strong positive impact in real-world applications lated to QA, e,g., search engines or chatbots. Since SURE can provide the summarization that sup- ports the corresponding prediction specifically, it significantly improve the explainability (Mao et al., 2022) and reliability (Whitehead et al., 2022) of QA systems which are more important when they constructed using black-box L.Ms.. Moreover, considering the success of LLMSs in various applications more than QA (Izacard et al., 2023; Nam et al., 2023), we expect the advantages of this framework better exploit the retrieved passages with LLMs will be beneficial to them.
In contrast, there also exists some potential negative impacts when developing a system with the multiple usages of LLMs, it could be costly (Chen al., 2023) and generate sensitive (Santurkar et al., 2023) and malicious (Deshpande et al., 2023) text outputs. Since the summarization from SURE constructed based on the provided passages, one should consider their quality prevent undesirable outputs. On the other hand, incorporating the additional filtering could be strong solution (Le Bras et al., 2020; Schick al., 2021). To reduce the cost, substituting specific steps of SURE, e.g., measuring validity, with trainable small LMs could be an effective way, similar to Yang al. (2020); Lewis al. (2021); Li al. (2023).
REPRODUCIBILITY STATEMENT
We provide implementation details (e.g., design of prompts, used APIs, and retrieval methods) and experiment setups (e.g., datasets and metries) in Section and Appendix A. In addition, we will release source codes near future.
ACKNOWLEDGMENTS
This work mainly supported by Institute of Information & communications Technology Plan- ning & Evaluation (ITTP) grant funded by the Korea government (MSIT) (No.2021-0-02068, Arti- ficial Intelligence Innovation Hub; No. No.2019-0-00075. Artificial Intelligence Graduate School Pro-
gram (KAIST)). This work was partly supported by KAIST-NAVER Hypercreative Al Center.
REFERENCES
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. Semantic parsing on freebase from question-answer pairs. In Conference Empirical Methods in Natural Language Processing (EMNLP), 2013.
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Milli- can, George Bm Van Den Driessche, -ean-Bap Lespiau, Bogdan Damoc, Aidan Clark, et al. Improving language models by retrieving from trillions of tokens. In Proceedings of the Interna- tional Conference on Machine Learning (ICML). PMLR, 2022.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. In Advances Neural Information Processing Systems (NeurIPS), 2020.
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. Reading wikipedia to answer open- domain questions. In Annual Meeting of the Association for Computational Linguistics (ACL), 2017.
Lingjiao Chen, Matei Zaharia, and James Zou. Frugalgpt: How to use large language models while reducing cost and improving performance. arXiv preprint 2055 2023.
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained code. arXiv preprint IxXiv:2007.03774 2021.
10

---

Published as a conference paper at ICLR 2024
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
Gobinda G Chowdhury. Introduction to modern information retrieval. Facet publishing, 2010.
Yung-Sung Chuang, Wei Fang, Shang-Went Li, Wen-tau Yih, and James Glass. Expand, rerank, and retrieve: Query reranking for open-domain question answering. In Findings of the Association for Computational Linguistics (ACL), 2023.
Antonia Creswell, Murray Shanahan, and Irina Higgins. Selection-inference: Exploiting large lan- guage models for interpretable logical reasoning. In International Conference on Learning Rep- resentations (ICLR), 2023.
Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan. Toxicity in chatgpt: Analyzing persona-assigned language models. arXiv preprint arXiv:2304.05335, 2023.
Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, et al. Glam: Efficient scaling of language models with mixture-of-experts In Proceedings of the International Conference on Machine Learning (ICML), 2022.
Bradley Efron and Robert J Tibshirani. An introduction to the bootstrap. CRC press, 1994.
Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. Enabling large language models to gen- erate text with citations. In Conference Empirical Methods Natural Language Processing (EMNLP), 2023.
John Giorgi, Luca Soldaini, Bo Wang, Gary Bader, Kyle Lo, Lucy Lu Wang, and Arman Cohan. Open domain nulti-document summarization: A comprehensive study of model brittleness under retrieval. In Findings of the Conference on Empirical Methods Natural Language Processing (EMNLP), 2023.
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre-training. In International conference on machine learning, pp. 3929-3938. PMLR, 2020.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In International Conference on Learning Representutions (ICLR), 2021.
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. Constructing multi-hop qa dataset for comprehensive evaluation of reasoning steps. arXiv preprint 1.01060 2020.
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense information retrieval with contrastive learning. In Transactions on Machine Learning Research (TMLR), 2022.
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-YV Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. Few-shot learning with trieval augmented language models. In Journal of Machine Learning Research (JMLR), 2023.
Ehsan Kamalloo, Nouha Dziri, Charles LA Clarke, and Davood Rañiii. Evaluating open-doman question answering in the era of large language models. In Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. In Con- ference Empirical Methods in Natural Language Processing (EMNLP), 2020.

---

Published as a conference paper at ICLR 2024
Jacob Devlin Ming- Wei Chang Kenton and Lee Kristina Toutanova. Bert: Pre-training of deep bidi- rectional transformers for language understanding. In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)20019
Omar Khattab, Christopher Potts, and Matei Zaharia. Baleen: Robust multi-hop reasoning at scale via condensed retrieval. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Jeon Dong Hyeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, et al. What changes can large- scale language models bring? intensive study on hyperclova: Billions-scale korean generative pretrained transformers. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453-466 2019.
Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev. Internet- augmented language models through few-shot prompting for open-domain question answering. arXiv preprint arXiv:2203.05113 2022.
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew Peters, Ashish Sabharwal, and Yejin Choi. Adversarial filters of dataset biases. In Proceedings of the International Conference on Machine Learning (ICML), 2020.
Kenton Lee, Ming- Wei Chang, and Kristina Toutanova. Latent retrieval for weakly supervised open domain question answering. In Annual Meeting of the Association for Computational Linguistics (ACL), 2019.
Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, and Sebastian Riedel. Paq: 65 million probably-asken asked questions and what you can do with them. Transactions of the Association for Computational Linguistics, 9:1098-1115, 2021.
Liunian Harold Li. Jack Hessel, Youngjae Yu, Xiang Ren, Kai- Wei Chang, and Yejin Choi. Symbolic Kai-Wei chain-of tf-uhh distillation: Small models can also" think" step-by-step. In Anmual Meeting of the Association for Computational Linguistics (ACL), 2023.
Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, and Ji-Rong Wen. Reta-llm: A retrieval-augmented large language model toolkit. arXiv preprint arXiv:2306.08 05212, 2023a.
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts. arXiv preprint 230.0 2023b.
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu, Gpteval: Nlg evaluation using gpt-4 with better human alignment. arXiv preprint 2rXiv:2303.16634 2023c.
Jianguo Mao, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yu Xia, Yajuan Lyu, and Qiaoqiao She. Explainable question answering based on semantic graph by global differentiable learning and dy- namic adaptive reasoning. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. Reader-guided passage reranking for open-domain question answering. In Findings of the Association for Computational Linguistics (ACI.), 2021.
12

---

Published as a conference paper at ICLR 2024
Grégoire Mialon, Roberto Dessi, Maria Lomeli, Christoforos Nalmpantis, Ram Pasumuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi- Yu, Asli Celikyilmaz. et al. Augmented language models: a survey. In Transactions Machine Learning Research (TMLR), 2023.
Sewon Min, Weijia Shi, Mike Lewis, Xilun Chen, Wen-tau Yih, Hannaneh Hajishirzi, and Luke Zettlemoyer. Nonparametric masked language modeling. arXiv preprini 20tp::2200000x22222211323333 2022.
Scbastian Nagel. Common crawl news dataset. https::/ddtta..omonn..o
in/ex/ndxxx//////// html, 2016.
Jaehyun Nam, Woomin Song, Seong Hyeon Park, Jihoon Tack, Sukmin Yun, Jaehyung Kim, and Jinwoo Shin. Semi-supervised tabular classification via in-context learning of large language models. In Workshop Efficient Systems for Foundation Models@ ICML2023, 2023.
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. Ms marco: A human-generate machine reading comprehension dataset. arXiv preprint 1rX:::1611.09268 2016.
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming- Wei Chang, et al. Large dual encoders are generalizable retrievers. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
OpenAl. Introducing chatgpt. https: httpps///peeaai..oo///oo////////
2022.
OpenAl. Gpt-4 technical report. arXiv preprint arXiv:2303.08744 2023.
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. Mauve: Measuring the gap between neural text and human text using diver- gence frontiers. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, et al. Large language models are effective text rankers with pairwise ranking prompting. arXiv preprint arXiv:2306. 17563, 2023.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research (JMLR), 2020.
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Squad: 100,000+ questions for machine comprehension of text. In Conference on Empirical Methods Natural Language Processing (EMNLP), 2016.
Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert- networks. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
Stephen Robertson, Hugo Zaragoza, et al. The probabilistic relevance framework: Bm25 and be- yond. Foundations and Trends@ in Information Retrieval, 3(4):333-389, 2009.
Joshua Robinson, Christopher Michael Rytting, and David Wingate. Leveraging large language models for multiple choice question answering. International Conference on Learning Repre- sentations (ICLR), 2023.
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. Whose opinions do language models reflect? In Proceedings of the International Conference on Machine Learning (ICMI.), 2023.
Timo Schick, Sahana Udupa, and Hinrich Schütze. Self-diagnosis and self-debiasing: A proposal for reducing corpus-bases based bias in nlp. Transactions of the Association for Computational Linguisties, 9:1408-1424, 2021.
Weijia Shi. Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettle- moyer, and Wen-tau Yih. Replug: Retrieval- augmentd black-box language models. arXiv preprint 2rXiv:2301.12653 2023.
13

---

Published as a conference paper at ICLR 2024
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang. Jordan Boyd-Grabes and Lijuan Wang. Prompting gpt-3 to be reliable. In International Conference on Learning Representations (ICLR), 2023.
Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wo Wei Chang. Asqa: Factoid questions meet forg-form answers. In Conference Empirical Methods in Natural Language Processing (EMNLP), 2022.
Dan Su, Xiaoguang Li, Jindi Zhang. Lifeng Shang, Xin Jiang, Qun Liu, and Pascale Fung. Read before generate! faithful long form question answering with machine reading. In Findings of the Association for Computational Linguistics (ACI.), 2022a.
Yixuan Su. Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, and Nigel Collier. A contrastive framework for neural text generation. In Advances in Neural Information Processing Systems (NeurIPS), 2022b.
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjic Ren, Dawei Yin, and Zhaochun Ren. Is chat- gpt good at search? investigating large language models as re-ranking agent. arXiv preprint Trxii:2304.00542 2023.
Hugo Touvron,
Thibaut Lavril, Gautier Izacard, Xavier Martinet, Maric-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv: 13971, 2023a.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaci, Niko- lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open founda- tion and fine-tuned chat models. arXiv preprint arXiv:2307.09288. 2023b.
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Interleaving re- trieval with chain-of- reasoning for mhowledge-intensivy multi-step questions. In Anmual Meeting of the Association for Computational Linguistics (ACL), 2023.
Ellen M Voorhees al. The trec-8 question answering track report. In Thec, volume 99, pp. 77-82, 1999.
Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A Smith, Iz Beltagy, et al. How far can camels go? exploring the state of instruction tuning on open resources. arXiv preprint 23xvv:230600755. 2023.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. tfithugh prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems (NeurIPS). 2022.
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. Neural text generation with unlikelihood training. In International Conference Learning Rep- resentations (ICLR), 2020.
Yixuan Weng, Minjun Zhu, Shizhu He, Kang Liu, and Jun Zhao. Large language models rea- soners with self-verification. arXiv preprint a7Xiv:2212.09561, 2022.
Spencer Whitehead, Suzanne Petryk, Vedaad Shakib, Joseph Gonzalez, Trevor Darrell, Anna Rohrbach, and Marcus Rohrbach. Reliable visual question answering: Abstain rather than an- swer incorrectly. In International Conference Computer Vision (ICCV), 2022.
Dao Xuan-Quy, Le Ngoe- Biic, Phan Xuan-Dung, Ngo Bac-Bien Bien, and Vo The-Duy. Evaluation of chatgpt and microsoft bing ai chat performances physics exams of vietnamese national high school graduation examination. arXiv preprint arXiv:2306. 04538, 2023.
Yiben Yang. Chaitanya Malaviya, Jared Femanndez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, and Doug Downey. Generative data augmentation for commonsense reasoning. arXiv preprint orXiv /1546, 2020.
14

---

Published as a conference paper at ICLR 2024
Zhilin Yang. Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In Conference Empirical Methods in Natural Language Processing (EMNLP), 2018.
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. Calibrate before use: Improving few-shot performance of language models. In Proceedings of the International Conference on Machine Learning (ICML), 2021.
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In European Conference Computer Vision (ECCV), 2015.
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A DESIGNED PROMPTS FOR EXPERIMENTS
In this section, we present the specific prompts used for the experiments in Section 4.
A.1 ANSWER CANDIDATES GENERATION
In Listing 1, we present the prompt Pean which is used to generate K answer candidates y [i ................................ //k] from the given question and N retrieved passages (Eq. 2). Here, we present the case of = =2
Listing Prompt answer candidates generation.
2111
Below are passages related the questien
end. After reading
the passages, provide two correct candidates for the answer the
question at the end. Each answer should be in the form: (a) xx, (b)
уу, and should not exceed words for each candidate.
Passage #1 Title: (Passage #1 Title) Passage #1 Text: (Passage HI Text)
Passage #N Title: (Passage FM Title)
Passage #N Text: (Passage IN Text)
Question: (Question)
Answer:
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A.2 CONDITIONAL SUMMARIZATION
In Listing 2, we present the prompt Paun which is used to generate conditional summarization 8 of retrieved passages validate each candidate ys as answer to the question (Eq. 3).
Listing 2 Prompt for conditional summarization.
Passage #1 Title: (Passage F1 Title)
Passage #1 Text: (Passage W1 Text
Passage FN Title: (Passage FN Title)
Passage #N Text: (Passage IN Text)
Your job is to act as professional writer. You will write good-quality passage that can support the given prediction about the question only based on the informaticn in the provided supporting passages.
Now, let's 's start. After you write, please write [DONE] to indicate you are done. Do not write a prefix (e.g., "Response:") while writing a passage.
Question: (Questien)
Choices: ((a) Choice 1 (b) Choice 2) Prediction: (a) Choice (or (b) Choice 2)
Passage:
!!!
A.3 INSTANCE- WISE VALIDATION
Listing 3, we present the prompt Pvai which used to evaluate the validity of each summarization 8t whether it is not a degenerated case the provided passages are not enough support Tk. properly supports the given answer candidate yk. rather than the other candidate Y: * (Eq. 4).
Listing 3 Prompt for instance-wise validation.
Question:
(Questien)
Prediction:
(Prediction)
Passage:
(Passage)
Does the passage correctly support the prediction? Choices: [True, + False]] Answer:
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A.4 PAIR-WISE WISE RANKING
In Listing 4, we present the prompt Prank which used to evaluate how the given summarization relatively informalive answer the question y, among all summaries Sx {sx] (Eqq. 5).
Listing 4 Prompt for pair-wise ranking.
Question:
Given the following passages, determine which one provides more informative answer to the subsequent question.
Passago 1: (Passage 1}
Passage 2: (Passage 2}
Target Question: (Question)
Your Task:
Identify which passage (Passage 1 Passage 2) is more relevant and informative to answer the question at hand, Choices: [Passage
4 Passage 2].
Answer:
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A.5 BASELINE PREDICTION
In Listing 5, we present the prompt that is used to append the retrieved passages of the question to give it inputs of LLMs. The result with this prompt is denoted by Base, in Section 4. The same prompt is used for no refrieval by assuming N 0, i.e. the only question is given to LLMs with instruction.
Listing 5 Prompt for baseline prediction.
2111
Passage #1 Title: (Passage F1 Title)
Passage #1 Text: (Passage #1 Text
Passage IN Title: (Passage IN Title)
Passage IN Text: (Passage IN Text]
Task description: predict the answer the following question. not exceed words.
Question: (Question)
Answer:
A.6 PROMPTS FOR GENERAL SUMMARIZATION
In Listing 6, present the prompt that is used construct generic summarization used in Section 4.3. One can observe that the conditioning part is removed, compared to Paux
Listing 6 Prompt for generic summarization.
Passage #1 Title: (Passage Title)
Passage #1 Text: (Passage + Text
Passage IN Title: (Passage EN Title)
Passage IN Text: (Passage IN Text)
Your job is to act as a professional writer. You will write good-quality passage that can support the prediction about the question only based on the informatien in the provided supporting passages.
Now, let's start. After you write, please write [DONE] to indicate you are done. Do not write prefix (e.g., "Response:") while writing a passage.
Question: (Question) Passage:
A.7 PROMPTS FOR MCQ PROMPT
Recently, Robinson et al. (2023) demonstrated that multiple-choice prompts generally elicit much more accurate responses than do cloze prompts, for LLMS with high multiple-choice symbol bind-
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ing ability like OpenAl Codex (Chen al., 2021). Motivated by this, we consider MCQ prompt in Listing and use it in Table 3, to evaluate the effectiveness of selecting the answer from the con- struction and verification of the conditional summarizations rather than direct prompting, under the same answer candidates from Eq. 2. One can observe that the conditioning with multiple choices part is added, compared to baseline prompting in Listing 5.
Listing 7 Prompt for MCQ prompt.
Passage #1 Title: (Passage F Title)
Passage #1 Text: (Passage [ Text
Passage #N Title: (Passage FN Title)
Passage IN Text: (Passage IN T
Task doscription: predict the answer to the following question. Do not exceed 3 words.
Question: [Question]
Choices: (aa Choice (b) Choice 2}
Answer:
A.8 DESIGN PRINCIPLES FOR PROMPT
Before finalizing the prompts used in the experiments, we examined several prompt de- signs and chose the best-performing one. Here, we'd like to share two key observations from this process. First, precise and detailed instructions are crucial. As each compo- nent of the proposed framework operates in zero-shot manner, its output greatly relies the provided instruction. For example, in answer candidate generation (Fq.. 2), the cur- rent prompt, outlined in Listing consistently outperforms the initially considered simple prompt (Task description: give two candidates for the answer to the following question (e.g., (a) xx, (b) yy)). Second, proper input arguments essential. For instance, along with the target candidate, providing all candidates additional in- put enhanced the quality of conditional summarization. This because it further specifies which contexts of retrieval should be the focus. However, including this information, or even the retrieval passages, disrupted the verification step by interrupting the focus on the summarizations.
B ADDITIONAL QUANTITATIVE RESULTS
B.1 MORE RESULTS FOR SURE UNDER DIFFERENT CONFIGURATIONS
In Table 5, we present scores with different configurations various L.LMs and retrieval meth- ods. Similar to the result in Table 2, it is observed that SURE consistently improves ODQA accuracy regardless of types of LLMs and retrieval methods, with 3.2% average F1 improvement on average.
B.2 LIMITED ACHIEVABLE IMPROVEMENT WITH MORE CANDIDATES
for all the experiments. This is due to our initial observation that the room for improvement by increasing K is not large compared to the additional costs. To investigate this, we first assume the method, denoted Oracle, which takes the maximum of EM and Fl among the multiple candidates, e.g.,, if one candidate is true and the other is wrong, then Oracle consider it as true. As one can see in Table 6, increasing K = from K = 2 limitedly improves the accuracy (e.g., 0.9% in EM), compared the remaining room for improvement by better selection with small K; for example, there is 9.0% gap between SURE and Oracle, in terms of EM. Therefore, in this work, we keep K 2 but remark that SURE can be
As we denoted in Section 4.1, we use fixed value of
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Table 5: F1 with different configurations of LLMs and retrieval methods on four QA datasets. N = 10 most relevant passages are commonly retrieved. For LLaMA2-chat, we conducted experiments on NQ and WebQ" and the results are indicated by
ChaiGPT
GPT-
LLaMA2-chal
Datasets
BM25
SURE
DPR
SURE
Contriever
+ SURE
BM25
SURE
BM25
SURE
NQ
38.8
42.3
47.4
50.8
47.6
50.4
40.9
42.4
36.4"
42.9*
WebQ
32.6
36.6
37.5
40.4
37.7
36.4
32.1
35.3"
40.5'
2Wiki
32.8
38.1
22.8
25.2
32,9
37.1
39.2
43.2
31.2
36.2
QA
40.3
43.4
34.6
35.5
42.9
43.5
44.4
50.4
39.6
38.4
Average
36.1
40.1
35.6
38.0
40.3
43.0
40.2
42.0
35.6
39.5
Table EM / Fl with different K under ChatGPT. N 10 most relevant passages are commonly retrieved with BM25.
Datasets / Methods
NQ
WebQ"
2Wiki
HotpotQA
Average
No retrieval
26.4 /
20.4 36.7
21.4/ 24.8
23.2/34.8
22.9 33.6
Base
29.4/41.7
/
27.4/32.5
30.8 00.8/40.3
27.0/ 36.9
Oracle (K 2)
43.9
29.0 43.9
47.6 54.4
41.2
40.2 51.2
Oracle (K' 3)
/5.0
29.8 47.2
48.4/ 56.2
41.0/ 54.0
41.1 53.4
extended with K > 2. Also, as there is remaining room for improvement, we hope that future work could reduce such a gap.
B.3 ADDITIONAL EVALUATION WITH LLMs
In Section 4, we considered EM/F1 scores as the common metrics for the considered ODQA datasets, following the previous works (Chowdhery et al., 2022; Touvron et al., 2023a; Izacard et al., 2023; Shi et al., 2023), to make it easy notice the significance of our results. Nevertheless, other factors like response coherence, relevance, and efficiency are important metrics to be considered.
To evaluate these aspects, we have conducted additional evaluations with L.MSs approaches. Specif- ically, we measured two additional metrics: (1) MAUVE (Pillutla et al., 2021) and (2) LLM-acc (Kamalloo et al., 2023). MAUVE is a recently proposed metric to compare the two distributions of the text generation model and human- written text using divergence frontiers. MAUVE (scale of 0 to 100, higher is better) is known for correlating highly with human judgments, and frequently used to evaluate LMs' responses (Su et al., 2022b; Gao al., 2023). LLM-acc assesses the accuracy (%) of LLMs responses to questions, using the prompting of LLMs instead of term overlap like EM/FI. We used the official code from the authors. only changing LLMs to ChatGPT. We measured this metric on NQ", WebQ". 2Wiki, and HotpotQA datasets, and the results are presented in Table 7.
Here, it is observed that the proposed method also makes significant improvements compared the baseline under these two additional evaluations with LLMs approaches. Along with the results in Section 4, this result further validates that our framework enables LLMs to provide better answers to the given question.
B.4 EXPERIMENTS ON LONG-FORE QUESTION ANSWERING
While we mainly conduct the experiments with QA datasets that have short answers in Section 4, our approach has the potential to be applicable beyond short-answer datasets. To verify this, we have conducted additional experiments on long-form question answering tasks to validate our approach's 's applicability. Specifically, we used ASQA dataset (Stelmakh et al., 2022; Gao et al., 2023) which consists of factoid questions and the corresponding long-form answers: for example, the answers of ASQA dataset have an average length of 71.8 words, while the answers of NQ dataset have 2.6 words. Following the setups in Gao et al. (2023), compared the base prompting method with retrieval and name (ours) on 948 test examples, using ChatGPT (GPT-3. 5-turbo-0333 03001) with 5 retrieved passages via GTR (Ni et al., 2022) for the experiments. For the evaluation, we measure
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Table 7: MAUVE (Pillutla et al., 2021) and LLM-evaluated accuracy (Kamalloo et al., 2023). We
use ChatGPT and = 10 most relevant passages are commonly retrieved with BM25.
MAUVE/ LLM-ace
NQ*
WebQ
2Wiki
HolpoiQA
Average
Base
81.3 / 53.2
61.3 48.8
35.1 / 36.2
52.4/516
60.0 47.5
SURE (Ours)
9 56.2
75.7 51.4
52.2
52.4
78.3 52.1
Evaluation ASQA dataset (Stelmakh et al., 2022). We ChatGPT and N most relevant passages are commonly retrieved with GTR (Ni et al., 2022), following Gao et al. (2023).
Table 8:
Methods Metrics
ROUGE-L
STR-EM
MAUVE
Base
38.00 39.83
39.81 42.63
69.83
SURE (Ours)
70.33
ROUGE-L and String Exact Match (STR-EM) for correctness, and MAUVE (Pillutla et al., 2021) for fluency and coherence, following the previous works (Stelmakh et al., 2022; Gao et al., 2023).
The results are presented in Table One can observe that our proposed framework consistently LLMs for long-form QA tasks. However, we
improves the performance of acknowledge that there is still room for improvement, particularly finding better prompt designs, given that our current designs are based on performance on short-answer datasets. We hope future research will explore this direction, extending the benefits of our framework to broader QA scenarios with LLMs.
B.5 EXPERIMENTAL WITH FEW-SHOT EXAMPLES
Here, we conduct additional experiments on NQ" and WebQ", using 1-shot and 5-shot examples from training datasets during prediction. We compare the average EM/F1 of base prompting with retrieval and SURE, across four different random seeds used for sample selection. In Listing 8, we present the prompt that used to generate K answer candidates in the case where few-shot examples are given. Here, we present the case of K 2. Note that if few-shot examples are provided, only the prompt for generating answer candidates is modified. Also, in Listing 9, we present the prompt for the base prompting. Table 9 shows that adding few-shot examples improves QA accuracy for both the baseline and name. Specifically, we observed that name's 's gain primarily results from generating more accurate answer candidates. These findings suggest that our proposed method could be effective in scenarios beyond the zero-shot setup considered. Therefore, we believe that our work could contribute broader ODQA scenarios in the future.
C HUMAN EVALUATION OF GENERATED SUMMARIZATION
In this section, we provide details on the human preference evaluation of generated summarizations Figure 4(c). First, we generate summarizations with a generic method (Listing 6) and with our proposed SURE (Listing 2). To separately consider the quality rationale with the accuracy of pre- diction, we only compare the samples correctly predicted by both SURE and generic summarization; it results in 84 examples from the NQ". Then, using the prompt Listing 11, conduct human evaluation. Specifically, we hired seven NLP experts off-line for our human evaluation experiment. Unlike asking GPT-4 with Listing 10, we ask human evaluators to answer as a tie if it is hard to determine.
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Table 9: Few-shot experimental results. We use ChatGPT and N 10 most relevant passages are commonly retrieved with BM25.
EM FI
0-shot
I-shot
5-shol
NQ": Base
29.4 41.77
30.1/39.
31.9 42.0 37.2 / 47.7
NQ": SURE (Ours)
35.6 / 44.9
36.3 / 46.8
WebQ": Base
/9.4/ 2
19.6/
19.9 / 33.5
WobQ": : SURE (Ours)
24.2 39.4
24.3 38.5
Listing 8 Prompt for answer candidates generation with few-shot examples.
Below are N passages related to the question at the end. We also provide the answers for various questions. After reading the passages and question-answer pairs, provide two correct candidates for the answer the question at the end. Rach answer should be in the form: (a) xx, (b) yy, and should not exceed words for each candidate.
Passage #1 Title: (Passage #1 Title)
Passage #1 Text: (Passage #1 Text)
Passage IN Title: (Passage FN Title)
Passage #N Text: (Passage IN Text)
Question: (Example question F1)
Answer: (Example answer #1)
Question: [Example quest [shot]
Answer: (Example answer #shot)
Question: (Query questien)
Provide two correct candidates for the answer:
D ADDITIONAL QUALITATIVE RESULTS
In this section, we present more qualitative results with SURE. All the examples are from NQ", and ChatGPT with BM25 (NV = 10) commonly used.
D.1 MORE EXAMPLES OF QUALITATIVE COMPARISON BETWERN SURE'S SUMMARIZATION AND GENERIC SUMMARIZATION
In Figures and 8, present examples for qualitative comparison between the candidate- conditioned summarization by SURE and generic summarization. Innecessary and tedious sentences irrelevant the answer highlighted with red.
D.2 QUALITATIVE EXAMPLES OF VERIFICATION WITH INSTANCE-WISE VALIDITY
To qualitatively show which samples are considered as invalid by LLMs, we present the examples that exhibit v(*)) = 0 as M (Pvan (g, 2h, sk)) False in Figure 9. Here, we highlight the sentences with green if they include the relevant context with the given candidate. In addition, we highlight the sentences with red if they induce different candidate as answer or do not support the candi- date. For example, the second example with a question (Who is the actor that plays Saul on GGrace and Frankie' ?), one observe that the generated summarization
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Listing 9 Base prompt with few-shot examples.
2111
Passage #1 Title: (Passage Mi Title)
Passage #1 Text: (Passage #1 Text)
Passage IN Title: (Passage HN Titlc)
Passage #N Text: (Passage HN Text)
Task description: predict the answer to the following question. not exceed words.
Question: (Example question H1)
Answer: (Example answer [1]
Question:
(Example question #k shot)
Answer: (Example answer #K shot)
Question:
(Query question)
Answer:
!!!
Listing 10 Prompt for GPT-based evaluation.
Question: Given the following summaries for the target question, determine which is more informative and plausible rationale to support given target question-answer pair.
Summary 1: (Summary 1}
Summary 2: (Summary 2}
Target Question: (Question)
Target Answer: (Answer)
Your Task:
Identify which summary (Summary 1 or Summary 2) is more informative and plausible raticnale to support given answer at hand. Choices: [Summary Summary 21.
Answor:
concludes that the given candidate (Mark Saul) is incorrect; consequently, LLMs evaluates its validity as supporting summarization for the given candidate as false.
D.3 QUALITATIVE EXAMPLES OF VERIFICATION WITH PAIR-WISE RANKING
In Figure 10, we present examples of verification by wise ranking. Here, we highlight with green for the summarization that gets higher ranking. In contrast, we highlight with red for the summarization that gets a lower ranking. We also highlight the relevant texts with the same colors, respectively.
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Listing 11 Template for human evaluation.
Given the following summaries for the target question, determine which one is more informative and plausible as rationale to support a given target question-answer pair.
Target Question: (Question)
Target Answer: (Answer)
Summary 1: (Summary 1}
Summary 2: (Summary 2}
Choices: [Summary 1, Tie, Summary 21
Your choice:
Table 10: Accuracy and cost ($) for each method. For the method in the last row, ChatGPT is used for Eq 2 and and LLaMA used for Eq and 5, respectively.
Exact Match (EM)
NQ
WebQ*
Average Cost API
Base (10 passages, ChatGPT)
29.4
19.4
1.57$
Base (50 passages, ChatGPT)
33.8
21.8
7.67$
SURE (10 passages, ChalGPT)
35.6
23.2
6.05$
SURE (10 passages, ChatGPT + LLaMA)
35.0
24.8
5.03$
E DISCUSSION ON COST AND QUALITY GAIN
While SURE significantly improves QA system of LLMs, one can be concerned about its cost as it requires multiple inferences of LLMs. However, we note that the improvement of SURE is not just simple consequence of more cost. Compared to other cost-increasing methods for accuracy improvement, SURE significantly outperforms them, i.e., SURE is an even more efficient way to increase performance. For instance, increasing the number of retrieved passages is of the most straightforward methods for this goal. But, in this case, SURE with 10 passages outperforms the base prompting with 50 passages, note that other baseline approaches such as chain-of-thougle or self seffvceiiiiattion (considered in Table 1) also require more cost than base prompting, but they fail to successfully improve the performance.
with lower total cost, as presented in Table 10. In addition,
On the other hand, one can reduce the overall cost by using cheaper LLMs for specific components, thanks to the modularity of SURE. Remarkably, SURE compatible with the recent sftteoffttheard open LLMs (see Tables and 5) and hence this advantage is more noticeable. To give an intuition, we conduct the new experiments by using ChatGPT for the answer candidate generation and sum- marization, and ILaMMA for the succeeding verification steps. As shown in the 4th row of Table 10, this hybrid approach of different L.LMs with SURF successfully reduces the cost while keeping the effectiveness for improving the accuracy; for WebQ this approach even outperforms the expensive one. This result from the effectiveness of LLaMA WebQ* and indicates the potential of such hybrid method.
Lastly, we further remark that most of SURE's cost is currently from re-reading retrieved passages for conditional summarizations (e.g., 38% of the total cost for SURE with 10 passages). This is due to current APIs not providing recycling options for previous inputs. recycling becomes available, SURE's cost could be significantly reduced.
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F LIMITATION AND FUTURE WORK
In this work, we primarily focused on zero-shot setup for the experiments, which is a commonly encountered scenario in the real world, egg, search engine. But, similar the previous works (Chowdhery et al., 2022; Touvron et al., 2023a), incorporating data-specific few-shot examples is also an interesting future direction to further improve QA accuracy of LLMs with SuRe. Another interesting direction is extending the applied task beyond QA, such as language modeling (Guu al., 2020) or language understanding tasks (Hendrycks et al., 2021).
G ADDITIONAL RELATED WORK
Summarization in open-domain. A summarization of retrieved passages has been considered in open-domain context; for example, there are recent works that propose to learn module to selec- tively use the retrieved information in sentence- (Khattab al., 2021; Su et al., 2022a) or passage- level (Mao et al., 2021; Chuang et al., 2023). In addition, Su et al. (2022a); Giorgi et al. (2023) form a new task that combines both passage retrieval and summarization for given query, and Gao et al. (2023) considers summarization of information for prompting. However, these works require a large annotated dataset to extract the information specified to answer the question or construct the generic summarization which focuses preserving the retrieved information within reduced texts.
H EXPERIMENTAL RESULTS WITH CONFIDENCE INTERVAL
In this section, we present confidence intervals for main tables (Tables 1 and 2). To achieve this, we apply bootstrapping (Efron & Tibshirani, 1994), a popular technique for statistical inference that involves random sampling with replacement. We report 95% confidence intervals obtained through 1,000 iterations of bootstrapping. The confidence intervals for the EM and FI metrics of each main table can be found in Tables 11, 12, 13, and 14.
The reliability of the results is reasonably robust, with the 95% confidence interval having only about a 10% variance from the reported value. Specifically, the EM metric of the NQ dataset, our SuRe has the lowest confidence interval value at 32.0, compared to the maximum value of 29.1 for the no retrieval baseline and 30.0 for the best competitor. This demonstrates that the advantage of SuRe over prior works is statistically significant.
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Table EM with different QA methods with ChatGPT on four QA datasets. The 95% confidence intervals are calculated via bootstrapping by 1000 iterations, and presented below the corresponding values. 10 most relevant passages are retrieved using BM25, except no retrieval. The best and second best scores are highlighted in bold and underline, respectively.
Methods Datasets
NQ
WebQ
2Wik
HotporQA
No retrieval
27.6
25.0
21.4
22.2
126.2, 29.1]
[23.2, 27.0]
[17.6, 25.2]
[18.8, 25.8)
Base
28.4
19.6
27.4
30.8
[27.0, 30.0]
[17.9, 21.3]
[23.6, 31.0]
[26.6, 35.2]
Rerank
24.8
18.8
23.0
27.8
[23.4, 26.2]
17.26,
[19.6, 26.8]
[23.8, 32.0]
RePlug
26.0
18.8
23.6
28.0
[24.6, 27.4]
11.11,
[20.0, 27.2]
[24.2, 32.0]
Selection-inference
24.3
17.3
22.6
30.8
[22.9, 25.7]
[15.7, 18.8)
[19.0, 26.0)
[26.8, 34.8)
Chain-of-thoughts
22.3
15.2
19.6
25.6
[20.8, 23.6]
[13.7, 16.6]
[16.0, 23.2]
[21.6, 29.4]
Self-verification
25.2
16.1
23.2
31.6
123.7,
[14.6, 17.7]
[19.6, 27.6]
[27.6, 35.8)
SURE (Ours)
33.5
25.1
32.8
33.2
[32.0, 35.0]
[23.1, 27.0]
[28.6, 36.8)
[29.0, 37.6]
F1 with different QA methods with ChatGPT on four QA datasets. The 95% confidence intervals are calculated via bootstrapping by 1000 iterations, and presented below the corresponding values. N most relevant passages are retrieved using BM25, except no retrieval. The best and second best scores are highlighted in bold and underline, respectively.
Table 12:
Methods / Datasets
NQ
WebQ
2Wiki
HotporQA
No retrieval
39.0
38.8
24.8
31.9
[37.7, 40.5]
[36.9, 40.7]
28.51
[28.2, 35.4]
Base
38.8
32.5
32.8
40.3
[37.3, 40.4]
[30.7, 34.3]
[28.9, 36.3)
[36.4, 44.4]
Rerank
33.9
30.6
28.4
37.4
[32.5, 35.3]
[28.8, 32.44
[25.1,
413.4.41.4
RePlug
35.3
31.5
28.5
37.9
36.8)
[29.8, 33.3]
[24.7, 32.1]
[34.2, 41.9)
Selection-inferencee
32.8
28.6
29.5
39.6
[31.5, 34.3]
127.0, 30.3]
[26.2, 33.21
[35.8, 43.71
Chain-of thoougts-
31.4
27.8
22.5
31.8
130.1,
126.1, 29.4]
118.8, 26.21
[27.8, 35.71
Self-verification
35.4
28.5
30.5
41.8
[33.9, 36.9]
[26.7, 30.2]
127.1, 34.91
[37.8, 45.6]
SURE (Ours)
42.3
36.6
38.1
43.4
[40.8, 43.7]
[34.8, 38.5]
[34.0, 42.0]
[39.4, 47.61
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Table 13: EM with different configurations of LLMs and retrieval methods on four QA datasets. The 95% confidence intervals are calculated via bootstrapping by 1000 iterations, and presented below the corresponding values. N = 10 most relevant passages are commonly retrieved. For LLaMA2- chat, we conducted experiments on NQ and WebQ" and the results are indicated by
ChaiGPT
GPT4
LLaMA2 chat
Datasets
BM25
SURE
DPR
SURB
Contriever
SURE
BM25
SURE
BM25
SURE
NQ
28.4
33,5
36.1
41.0
35.8
40.4
30.2
32.4
18.6
30.4"
(52.0.35.0]
(3.55
(2.66
(3122
(3.99.33.]
[15.0.
[26/1.444]
WebQ
19.6
25.1
23.2
27.3
22,5
28.7
21.3
21.7
16.0'
24.0"
(23.1,27.0]
(2114,5511]
[25.3,29.2]
(2.35,2133
[19.6,
[19.8,2341]
27.8]
2Wiki
27.4
32.8
19.2
21.4
27.2
32.6
34.8
38.2
20.2
27.8
[15.8]
24.8)
[23.2, 31.0]
[30.6
[34.0,42]
[166, 23.6
HotporQA
30.8
33.2
25.6
27.4
32.2
33.6
34.8
40.6
24.0
28.0
29.4)
[23.8 31.4]
[36224446]
[20.2, 27.6]
Table 14: Fl with different configurations of LLMs and retrieval methods on four QA datasets. The 95% confidence intervals are calculated via bootstrapping by 1000 iterations, and presented below the corresponding values. N = 10 most relevant passages are commonly retrieved. For LLaMA2- chat, we conducted experiments on NQ and WebQ and the results are indicated by
ChaiGPT
GPT-
LLaMA2-chal
Datasets
BM25
SLRR
DPR
SURE
Contriever
StRE
RM25
SURE
BM25
SURE
NQ
38. 8
42.3
47.4
50.8
47.6
50.4
40.9
42.4
36.4"
42,9*
48.91
[49.3,5244]
[48.8.5 51.8)
[39.5,42.]
[41.0.43.9]
[32.8, 39.6]
WebQ
32.5
36,6
37.5
40.4
37.7
40,9
36.4
32.1
35.3"
40.5"
39.3)
42.33,
[35.8, 39.6]
42.7)
[30.2.34.00
[367745 43.91
2Wiki
32.8
38.1
22.8
25.2
32.9
37.1
39.2
43,2
31.2
36.2
(28.9.
[34.0.
[1944, 26.3)
(21.6 28.8]
[29.3, 36.9)
[33.0. 41.4)
[35.2.43.0]
[39.3,47.])
[27.7, 34.9)
[32.2. 39.9)
HotpotQA
40.3
43.4
34.6
35.5
42.9
43.5
44.4
50.4
39.6
38.4
[36.4,
[39.4,47.6]
[30.8, 38.1)
38.1)
(31.6 39.6]
[36.6, 47.2]
47.33
[80.5.48.1]
[46.3, 54.3)
[34.6. 42.5]
28

---

Published as a conference paper at ICLR 2024
Question
Answer
Who British
European
Cup?
Generic Summarization
Passage Title: Brilish Teams European Cup History
Cup, any memorabi momenss echievements Howev er, there is one team that stands out the first British team to win the prestigious lourraamett,
footbll
history
Celtic, wan
European Cup.
Caltic's victory final against Italianside Inter Milan was historic moment British football. became the first team from United King style, coming back from goal down to win 2-1 in Liston.
European Cup, they did it
Since Carcc's triumph, many other British teams have Manchester United. Liverpocl, Nattingham Forest. and Villa ever, it Celtic and their achiev will always remembered
the Europsaa Cup, Incluci
pav ed the a landmark moment in football history.
British
In conclusion, Celtic the first British team to win the European Cup, paving the way for other British teams achiev e uccess tournament Their victory in the 1966-67 season always remembered historic moment foothall history.
Summarization (Ours)
anew er question of w was the first British te am win European Cup can be found in several of the provided pass ages. Acc arding to Passage 16, the first British leam to win the European Cup was Cellic, who achieved this feat in the 1966-67 season. They defeated cup.
becoming first Scottish that Celtic was the first British team to win the European Cup is supported by the information in the passages.
Internazional
the prediction
Question
Answer Spain, Taidro
belong
Generic Summarization
Passage Title: Puerto Rico's History Before U.S. Oocupation
Puerto Rico's history before the U.S. U.S. occupation dates back to the era of the Taino, the indi perigeoous people the Caribbean inhablte island they called "Boriken"
sustoms blended with thos the Sparisn, Alrican, and women from non-Hispenic European countniss during the Spanish colonizatiio Puerto was Spanish colory until end the Sparish American War in 1898, w when Spain oeded Puerto Rico, Blong with the Philippines and Guam, the United States under the Trealy Panis, Befor the U.S. occupation, Puerto Rico w as Spanish territory people Spanish sutjects.
Candidate-condttionnn Summarization (Ours)
Puerto Rico was a Spanish col any before was coded to the Urited States es under or the terms of the Trealy of Paris 1988 recorded history women in Puerto Rico can trace roats back to the era of the "Taino", the indigenous e of Caribbean. who Inhabited the Island that they called "Boriken" before the arrival Spani ards. However, was S paiin who had control av Puerto Rico before the United States. T Therefore, the predictioon on that Puerto Rico bel onged to S pain befor the U.S is supported by the information in the provided passages.
Figure 6: Qualitative comparison of candidate-connditonee summarization from SURE (Ours) com- pared generic summarization as rationale for the answer.
29

---

Published as a conference paper at ICLR 2024
Question
Answer John Quinay Admas
Who is the sixth president of the United States?
Generic Summarization
Passage Title: John Quincy Adams
John Qu Adams was the sixth President of the United States, serving fr om 1825 1829. He was the eldess son of John Adams, who also served as Pr esident from 1797 1801. John Quincy Adams had a long diplam allc and political career, serving
ambessador, United States Senator, and member of the United States House of Re presentatives. He also served as the el gigtt United States Secretary State before becoming Presi dent. espiit
acksonian Democrats, Adams was able to ancomplish several importart intiialivss during his presidency.
Including cons truction roads and canals, the establishment of nacional university, and the creation of national observatory
Candidate-condttonn Summarization (Ours)
United serving from was the eldes son of John Adams, who also served as President from 1797 to 1801. John Quincy Adams had a long piplo atic and political career, serving
Qui ney
was
1829 He
ambassador, United States Senator, member of the House Representatives He also the eighth hith United States Secretary State befor e bec aming Presi dent. Despite facing opposisi from Andrew Jacks and his preei dency through controversial election House
sup porters, of Represerne Reppresetttiives.
Question
Answer Egypt
In which country were two battles of El Alamein fought?
Generic Summarization
The two bottles of El Alamein were fought in Egypt during World War II. The First Battle of Alamein fought een forces (Germany Australia. South Africa, and New Zesland) of the Eighth Army. The battle eventtdd second adv ance by the Axis forces into Egypt. The Second Battle El ammii w as fought between the Axis and Allied force and considered turning point the Wes tern Desert Campaign. The tratrallan 9th Division fought in both battles, and the El Alamein Memorial Fountain in Sy dhey Egypt
Italy)
forces (Britain, British India.
roles.
the
war museum with conlecti bles from "the civil war and other North Africcm batt es, well
Iallaa and German military cemeteries on Tel ElsEisa Hill outside the lown
fondidate-condttiones Summarization (Ours)
The two battl Alamei fought Egypt, enti oned multiple pass ages. El Alamein town in the northern atrouh Gov ernor ale Egy pl, localed on the Mediterranean Sea, west Alexandria northwest Cairo. The
Alamein was fought Egypt between forces (Germary and Italy) and Allied forces (Britain, British India, Aus tralia,,,, South Africa, and New Zealand) of the Eighth Army. he Second Australian Division fought alongside other Allied forces. Therefore, it clear that batil of El Alarmein were fought in Egypt.
El Alamel was also fought in Egypt, where the
Figure 7: Qualitative comparison of candidate-condittone summarization from SURE (Ours) com- pared to generic summarization as a rationale for the answer.
30

---

Published as a conference paper at ICLR 2024
Question
Answer
says which call rose?
Juliet
Generic Summarization
Passage #1 Titl: Romso and Juliet
Passage #1 In Shakes peare's play Romeo Juliet, Juliet says famous
This line spoken during the balcony sc ene, hhere Rameo and Juliet ex press their love for each other despite their families' feud.
"that which call
by any other
would smell
olly sayling
name does
thing,
even
Romeo not Montague, she would a love him just much. he line has beoom loonic and ofien quoted popular culture
Candidate-conditionss Summarization (Ours)
Passage says which Therefore, the prediction that Julist says the line "that which we call a ros e" is supported by the information provided passages. is Juliet the one who utters this farouus
by any
smell
Question
Answer Ludacris
Who the rap in baby Justin Bieber?
Generic Summarization
Passage #3 Title: All Around the World (Justin Bieber song)
Passage During the rap section, Ludacris refere noes their previous collaboration an "Baby" (201 saying, "Dnce again, the dynamic duo back
JB, Ludscrist/ love evertthing boutt you You're Impertectly perfect/ Everyone's Itching for beauty/But they're just scretching
Prediction: Ludacris sings
"Baty" by Justin Bieber.
Response: As mention ed in Passage #3, Ludacris sings the rap section in "Baby" by Jus tin Bieber. concluded
previous collaboration the song Therefore,
Ludacris the one who the rap in "Baby" Justin Bieber.
conditooooo Summarization (Ours)
to the Passage ge #1 and Passage #5. Bath passages es mention that rapper Ludacris is featured in song performa rap
of who sings the Justi Bi Biebe'ss song "Baby"
found
assage Ludacria credited aco-writer of the song, while Passage #5, his verse-rap described as an anecdate of young love when he was thirteen. Therefore,
predicted
sings rap "Baby" by
Qualitative comparison of candidate-conditioneed summarization from SURE (Ours) com- pared generic summarization as rationale for the answer.
Figure 8:
31

---

Published as a conference paper at ICLR 2024
Question
Candidate Chariton Heston
Who played the king in
The Ten Commandmentt?
Summarization (Ours)
Passage #6 Title: [he Ten Commandments (1923 fim) Passage
Cecil DeMille. The film tells the story of M from his adopiion the Egypfian princess, his dellverance of the Hebrews from slavery a the hands of the Egyptians, to his recelving of the Ten Sinal. The
1923
played by Theodore Roberts. However, in the 1956 remake of the fim, @hariion Heston played the role Moses Therdfore, the prediction
Command
God
Heston played king
Commendments"
Real answer
Yul Brynner, Woodrow Stnde
Question
Candidate
Who is the actor that plays Saul
Graae and Frankle
Mark Saul
Candidateecomdittoned Summarization (Ours)
Mark Saul is the actor who played the mle of Dr. Ryan on "Grey's Anatomy" and starred commercial for Jack the Box. He also talented ariist and musician, creating for his Etsy shop and playing in the folk/rock band, The lashcards.. However there la no mention of him aying Saul on "Grace and Frankie". Ihherffore, the prediction that Mark Saul is actoo who plays Saul on "Grace and Fmankiee incorrec
Real answer
Sam Waterston
Question
Candidate
Premier league players to score gools in one game.
Edin Džžeko
Bandidate-conditions Summarization (Ours)
Edin Dãeko, Bosnian professional footballer, is known for his impressive scoring record the gue
four goals against Tottenham Hotspur, breaking another record becoming the first Manchester City player score our coals in one Premier Le eque mateil
Leaguelgame
season, Džeko
Month His tally of six lea gue goals in three games won him the for August. Džeko continued grab important goa such header against Wigan Athletic that sealed 1-0 win for City and extended their lead over points. Althaugh
Premier League Player
geal game, impressive scoring record and ability to score multiple goels in single game make him strong candidate for achieving this feat.
Real answer
Alan Shearer, Andy Cole, Dimtar Berbatov, Sergio Aguero, Jermain Defoe
Figure 9: Example summarizations that are evaluated as invalid by LLMs (Eq. 4).
32

---

Published as a conference paper at ICLR 2024
Question
Who & the CEO Crateand Bamel?
Fandidate-condtiooees Summaridanmaaamaa Summanization (Candidate: Neela Montgomery!
Nesia Montgomery the current CEO of Crate Bars) having assumed role after Doug Diemaz departed in April 2017. Monigomery brings wealth of experience the position, having Board Member Mull-Channel company of Grate Barrel. Under her leadership, the company is expected to continue its exp ansion into new markets, both domestically
previbusly served
Group, the parent
internationally. Montgomrery's focus innovation and customer experience is drive growth and success for Crate Barrel the years come.
Candidate-condillonee conditioned Summarization 2 (Candidate: Sascha Bopp)
Sascha Bopp was named the CratfCrate Barrel after Barbara Turfretired in 2012 How ever, 20 haliday season
gr awing disenc employees, Bopp removes ed from his position and terminated from the company. nienee then, there have been sev eral other CEOs, including Doug Diemaz and currently Neela Montzomery. information pvovdded, itcan predicted that Sasc Bopp the CEO Crate & Barrel at some point in time.
poor performance during
However, bas
Real
Noela Monigamery
Question
The concept of micro financing develaped by who 19767
Candidate-condtionn
Summmnization (Candidate: Muhammad Y Yunus]
Microfinacce is term used desoribe financial services, provided to low-income individuals or those who do not have access to traditional banking rvices. The concept of microfinance has been ar ound for deca des, but was not until the 1970s that gained widespread dieniion. 1976, Muhammad Yunus, Bangladeshi experimenting with lending small amounts money poor women in ruml areas. He founded the Grameen Bank in 1983, which becams poor without ut re equiiig collateral. Yunus and the Grame en Bank wes awarted the Nobel Peace Prize 2006 their efforts to create economic and social development through microfinance. Therefore, Yunus 1976.
as loans, savings, and insurance,
began
microfinance institttion provide small
concluded that the soncept
financing developed by
denoiiiiinddddd Summaniationn (Candidate: Grameen Bank)
Microfinance is termthat has evolved over time, from micro-credit to miaro-iinncee is way to promote economic development, mployment, and growth th rough the support of micro- nntrssreneurs and small businesses. The Grameen Bank one the pioneers microfinance, having been founded Bangladesh in provide smal loans to poor peopls who did not have access to traditinall banking servioes. The bark's success reducing poverty through h microfinance world. Whie microfihance has its aitics, it has undoubtedly he lped many people lift themselves out
by Muhammad Yunus. The bark's mission
inspined
iniliatives
of poverty. [herefore, can udd that concept micro financing was not daveloped by
Yunus 1976, rather
Bank 1983
Real answer
Muhammad Yunus
Figure 10: Example summarizations with pair-wise rank evaluation (Eq. 5).
33


Reviews:
[{"review_id": "WDuH1ZKp8Q", "reviewer": "Reviewer_mMA8", "summary": "This work studies retrieval augmentation via prompting, for the task of open-domain question answering. They propose a method based on \"summarized retrieval\" (SuRe). SuRe proceeds in a few steps. First, it retrieves top-k results with an off-the-shelf retriever. Second, it generates multiple candidate answers directly (not via decoding multiple completions). Third, it generates a conditional summary (of the retrievals) for each candidate answer. Lastly, it validates each summary (as faithful or not) and uses a pairwise comparison approach to select the most informative answer. The pairwise scoring approach is applied across all pairs and averaged.\n\nOn the Open-Domain QA tasks NQ, WebQ, 2Wiki, and HotpotQA, the authors report improvements of up to 4.4% in exact match over baselines. The authors conduct a human evaluation of the SuRe summaries (whose defining characteristic is being centered around a candidate answer, derived from GPT-4) against general-purpose summaries (derived from GPT-4 without answer candidates). Generic summarization wins 30.3% while SURE wins 37.4%. They ask human evaluators which summaries are more informative and better support the question-answer pairs, and observe higher preference for SuRe (Generic: 26.9% vs SuRe: 43.4%).", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed pipeline is relatively rich and well-executed.\n\n2. The authors develop a number of thoughtful baselines and report extensive comparisons. While there's very limited comparisons to prior work directly, I do find that there's a lot of value in the set of curated baselines they develop, which can be compared apples to apples to the proposed method.\n\n3. The results are consistently solid across several tasks, LMs, and retrievers. This is the hallmark of a solid idea. The results are never that strong overall (in isolation), but perhaps SuRe can probably be combined into a really strong 'sota' system in principle.", "weaknesses": "1. The authors assert in the abstract that retrieval augmentation via prompting \"has been limitedly explored\". While much more work is required to improve RAG methods that use prompting (or otherwise), few areas of modern NLP that have received more attention than RAG prompting. As a case in point, the authors build a method for \"summarized retrieval\", but I don't see citations to much prior work on considering summarization in the context of open-domain QA and prompting. For example, \"Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval\" is the title of a paper at NeurIPS 2021, where the notion of _condensed retrieval_ seems fundamentally connected to _summarized retrieval_. For another example, \"Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval\" is a recent task proposal. These are certainly different formulations of summarization at scale, but they are just two examples of a rich space considering summarization in open-domain contexts.\n\n\n2. The authors focus on 'zero-shot prompting', but I do not find a convincing justification for presenting this limitation as a 'remarkable' feature. Zero-shot prompts are not necessarily indicative of generality (if anything, a decent few-shot prompt specifies the task more precisely and is empirically not unlikely to be more robust across LMs, counter to the assertion by the authors). While I'm not opposed to the need to eliminate some angles from a large experimental endeavor, I do wonder how useful SuRe is if the QA component had access to a few examples of the task. (This overall may explain, for instance, why chain of thought performs so poorly in the evaluations.)", "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": "", "extra_scores": {}}, {"review_id": "PyyQD4lhhR", "reviewer": "Reviewer_Qrw6", "summary": "This paper proposes a method to improve open-domain QA by making a summary of the retrieved passages. To create good summaries, candidates are first generated by an LLM, which are then used to condition the generation of summarties. The method is compared with a naive augmentation with all the retrieved passages and a generic summarization, as well as several existing approaches such as reranking, CoT, etc. The proposed method is shown to perform better on several datasets.", "questions": "See comments in Weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The idea of creating a summary of the retrieved passages centered around the possible answers is very interesting. This may solve the problem of noise information contained in the passages and help dealing with long passages. This idea has not been explored previously.\nThe approach relies on prompts to LLM, so it can be used with any LLM without fine-tuning it. This may be a generally feasible approach in many application contexts.\nThe experimental results are convincing. It demonstrates the a answer-oriented summary is better than a generic summary, and better than no summarization. The advantage of the approach is properly shown. In addition, the method is also shown to outperform the existing methods.", "weaknesses": "The performance of the method may strongly depend on the prompts used. While the paper demonstrates that appropriate prompts can help create a good summary for improving QA, there are still questions about what prompts should be used. I wonder if the authors have tested several alternative prompts before choosing the ones used.\nFig 3 is unclear. What are \"the corresponding two conditional summarizations\"?", "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": "", "extra_scores": {}}, {"review_id": "PJO81aWK6g", "reviewer": "Reviewer_9TBy", "summary": "This work introduces Summarized Retrieval (SURE) to enhance the performance of Open-Domain Question Answering (ODQA) using retrieval-augmented Language Models (LLMs). The goal is to provide more well-grounded answers with LLMs by generating summarizations of retrieved passages, which serve as explicit rationales for the answers. By constructing multiple summarizations for each possible answer candidate, LLMs can then focus on context relevant to the candidate and provide more discriminative viewpoints for the question. Experiments are conducted on multiple QA datasets showing that SURE improves across all of them.", "questions": "- Why limit the evaluation to only EM/F1 and not consider LLMs approaches for automatic evaluation?\n- Why only short-answer datasets? Have you considered long-answers? What would change in that case?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The idea of constructing the summaries of the retrieved passages for the potential answer candidates is somehow simple yet effective. The paper shows significant improvements across various datasets.", "weaknesses": "While SURE shows interesting results, there are some points that in my opinion should be clarified/improved before publication:\n- It is unclear how this approach can scale. SURE may work well in experiments, but in real-world applications, the number of relevant passages can vary greatly and the various steps in SURE can become extremely costly, limiting the usefulness of this approach.\n- The evaluation metrics are based on term overlaps and they might not capture all dimensions of model performance. Other factors like response coherence, relevance, and efficiency should also be considered, especially in the case of LLMs.", "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": "", "extra_scores": {}}, {"review_id": "ORELRQZeWG", "reviewer": "Reviewer_AgBt", "summary": "This paper proposes Summarized Retrieval (SURE) for open-domain QA. First, it generates answer candidates from retrieved passages  with LLMs. Then, for each candidate answer, it conditionally summarizes the retrieved passages in order to focus on extracting the candidate-relevant contexts. Then those answers are ranked by a weighted score of instance-wise validity score and pairwise informativeness score. Experimental results show that SURE significantly outperforms the baselines on multiple datasets and LLM configurations. Detailed ablation studies are also performed.", "questions": "The paper is clearly written. No more questions from me.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- A novel framework is proposed to enhance open-domain QA with LLMs where the candidate answer can be better grounded on retrieved passages.\n- The experiments are well-conducted and the performance improvement is significant and consistent.\n- The paper is very well-written.", "weaknesses": "- The proposed method could be expensive considering eq 4-6.", "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": "", "extra_scores": {}}, {"review_id": "TEL1Jhg3ZQ", "reviewer": "Reviewer_MmoS", "summary": "The paper presented a new design, named SuRE, of combining retrieval and prompting to improve LLM's Open Book QA quality. It starts with retrieving relevant documents about the question, then generates a list of candidate answers with vanilla retrieval-augmented generation (RAG) prompting. The key contributions then follow. It prompts LLMs to produce a per-candidate summary of the retrieved documents to support the candidate. Then, LLM-based point-wise and pair-wise critiques are used to assess the quality of the per-candidate supporting summary. The candidate with the most sound supporting summary is chosen as the final answer. This is based on an intuition that the supporting summary for a correct candidate is usually of higher quality.\n\nThe paper designed experiments to show the quality gain of SuRe, which surpasses vanilla RAG and other recent algorithms of improvement. It also showed that the improvement is consistent with different retrieval algorithms and LLMs. Ablation study is run to understand the contribution of different part of SuRE. The paper thus concludes that SuRe is an effective way to improve retrieval-based open book QA without the need to finetune the underlying LLM.", "questions": "- Section 1 paragraph 2: \"implicitly forced to use the retrieved information without additional training\". The Reviewer didn't get it. Do you mean use or not use?\n\n- Numbers in the experiment section: It's better to give some confidence interval of the EM/F1 numbers since they are obtained on a smaller sample (500, if the Reviewer recalls correctly).\n\n- Section 4.3: Expand \"MCQ\" and explain what Robinson et al (2023) did. Readers should not have to read a reference paper if they don't want to dive deeper.\n\n- Figure 4 (a): The shape of the point is hard to read, and only using the red-blue color to distinguish lines could be a problem for color-blind people. (Reviewer appreciates the effort of adding patterns to (b) and (c) so color is not the only discriminator). \n\n- Last paragraph of Section 4. Reviewer suggest adding the number of human-preference samples (84) here, saving readers a trip to the Appendix.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Writing and clarity. The paper is mostly well-written, easy to follow, and free of grammar / formatting errors.\n- Comprehensive experiment design. The paper tested SuRe with different datasets, retrieval algorithms and LLMs as those factors could have a big impact on the outcome.\n- Reviewer particularly likes the way the paper listing all research questions explicitly at the beginning of the experiment section with reference to corresponding tables and figures: clear and easy to follow.", "weaknesses": "Here the Reviewer tries to order the weakness by their priority.\n\n- The title and initial claim in the abstract are too broad. They almost sound like a claim of re-inventing RAG. Abstract called out of hallucination and grounding, but it's not specifically studied in the paper, at least not more than just exact-match rate and F1 scores. It would be better if the authors make the title and abstract more specific to the contribution.\n\n- Efficiency and cost, which is an important shortcoming of SuRe, is not discussed. During the process, SuRe makes a significant amount of calls to the underlying LLM. They are both slow and costly (in direct money terms in the case of calling commercial APIs). It would be useful to show the comparison and let any potential users know the cost of the quality gain.\n\n- Ablation study is poorly designed. Reviewer is expecting a study where each of the key component of SuRe  is removed (ablated). However the paper showed the results of each one individually added. The subcomponents are not 1 to 1 mapped to SuRe either. For example what is MCQ and how it maps back to SuRe? Some readers may figure it out eventually but it's hard for the Reviewer to get it in a short time.   \n\n- Need to be specific about \"limitedly explored\" when talking about previous work. A potential reader should not need to guess or read the whole reference paper to understand where the limits of the previous exploration are in the author's view.\n\nSee more trivial comments related to weakness in the Questions section.", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)