Question:
Which approach most effectively mitigates the negative consequences of flawed information retrieval in RAG systems, as suggested by recent research?
A. A primary approach involves exclusively refining the initial retrieval mechanism through techniques like advanced query expansion and document re-ranking to guarantee the relevance of the provided external knowledge.
B. One compelling approach is the sophisticated integration of the language model's pre-existing internal knowledge with the retrieved external information, employing strategies to identify and resolve potential conflicts in a source-aware manner.
C. A straightforward approach to enhance RAG system resilience focuses on increasing the sheer volume of retrieved documents, assuming that the probability of encountering correct information will proportionally rise with a larger context window.
D. An alternative approach leverages a multi-stage process where the language model first generates potential answers based solely on its internal knowledge, and subsequently uses retrieved documents merely as a validation layer without actively integrating conflicting information.

Answer: BD

Astute RAG: Overcoming Imperfect
Retrieval Augmentation and Knowledge
Conflicts for Large Language Models
Fei Wang1 2 *, Xingchen Wan1, Ruoxi Sun1, Jiefeng Chen1 and Sercan Ö. Arık1
1Google Cloud AI Research, 2University of Southern California
Retrieval-Augmented Generation (RAG), while effective in integrating external knowledge to address
the limitations of large language models (LLMs), can be undermined by imperfect retrieval, which may
introduce irrelevant, misleading, or even malicious information. Despite its importance, previous studies
have rarely explored the behavior of RAG through joint analysis on how errors from imperfect retrieval
attribute and propagate, and how potential conflicts arise between the LLMs’ internal knowledge and
external sources. We find that imperfect retrieval augmentation might be inevitable and quite harmful,
through controlled analysis under realistic conditions. We identify the knowledge conflicts between LLM-
internal and external knowledge from retrieval as a bottleneck to overcome in the post-retrieval stage of
RAG. To render LLMs resilient to imperfect retrieval, we propose Astute RAG, a novel RAG approach
that adaptively elicits essential information from LLMs’ internal knowledge, iteratively consolidates
internal and external knowledge with source-awareness, and finalizes the answer according to information
reliability. Our experiments using Gemini and Claude demonstrate that Astute RAG significantly
outperforms previous robustness-enhanced RAG methods. Notably, Astute RAG is the only approach
that matches or exceeds the performance of LLMs without RAG under worst-case scenarios. Further
analysis reveals that Astute RAG effectively resolves knowledge conflicts, improving the reliability
and trustworthiness of RAG systems.
1. Introduction
Retrieval augmented generation (RAG) has become the standard approach for large language models
(LLMs) to tackle knowledge-intensive tasks (Guu et al., 2020; Lewis et al., 2020). Prior works mainly
leverage RAG to address the inherent knowledge limitations of LLMs, effectively integrating missing
information and grounding to reliable sources. However, recent research has highlighted a significant
drawback that RAG might rely on imperfect retrieval results, including irrelevant, misleading, or even
malicious information, which eventually leads to inaccurate LLM responses (Chen et al., 2024a; Xiang
et al., 2024; Zou et al., 2024). For example, when asked about the practice of eating rocks, LLMs
might cite misleading information, such as a satirical news source claiming that one should consume
at least one rock per day.1 The occurrence of imperfect retrieval augmentation is inevitable, driven by
factors such as corpus quality limitations (Shao et al., 2024), the reliability of retrievers (Dai et al.,
2024), and the complexity of the queries (Su et al., 2024). This poses a significant challenge to the
trustworthiness of RAG.
While there have been independent analyses of information retrieval and RAG in the context of
LLMs (Mallen et al., 2023; Su et al., 2024), previous studies have rarely connected the behaviors of
retrieval and subsequent generation, particularly regarding the propagation of information retrieval
errors, which may lead to knowledge conflicts (Longpre et al., 2021; Wang et al., 2023a; Xu et al., 2024b)
between LLMs and context. To this end, we conduct comprehensive analyses on the occurrence of
1https://www.bbc.com/news/articles/cd11gzejgz4o.
Corresponding author(s): fwang598@usc.edu, soarik@google.com
* This work was done while Fei was a research intern at Google Cloud AI Research.
arXiv:2410.07176v1  [cs.CL]  9 Oct 2024
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Figure 1 | Knowledge conflicts between the LLMs’ internal knowledge and retrieved knowledge from
external sources. We report the overall results with Claude under the setting in Section 4.1.
imperfect retrieval augmentation and its impact on LLM behavior under realistic conditions (Section 2).
We conduct controlled experiments on a diverse range of general, domain-specific, and long-tail
questions from NQ (Kwiatkowski et al., 2019), TriviaQA (Joshi et al., 2017), BioASQ (Tsatsaronis
et al., 2015), and PopQA (Mallen et al., 2023). We observe that imperfect retrieval augmentation is
widespread even with adept real-world search engine (such as Google Search with Web as corpus)
– roughly 70% retrieved passages do not directly contain true answers, leading to the impeded
performance of LLM with RAG augmentation.2
These findings underscore the potential severity of the imperfect retrieval issue in real-world
RAG and highlight the widespread existence of knowledge conflicts as the bottleneck to overcome
it (Figure 1). Recent studies demonstrate that LLM-internal and external knowledge offer distinct
advantages, but LLMs often struggle to consolidate conflicting information reliably, failing to respond
based on collective knowledge (Jin et al., 2024; Mallen et al., 2023; Tan et al., 2024; Xie et al., 2024).
This raises the following research question: Is there an effective method to combine internal (from LLMs’
pretrained weights) and external (from specific corpora or knowledge bases) knowledge for more reliable
RAG? Previous work has widely explored using external knowledge to enhance LLMs through RAG.
We seek to further leverage LLMs’ internal knowledge to recover from RAG failures
Motivated by these important real-world challenges, we propose Astute RAG (Section 3), a
novel RAG approach designed to be resilient to imperfect retrieval augmentation, while preserving
RAG grounding effect when RAG is reliable. To this end, Astute RAG needs effectively differentiate
the reliability of the LLM’s intrinsic knowledge and the external information retrieved in RAG, utilizing
each only when trustworthy and ensuring proper integration. Specifically, Astute RAG initially
elicits information from LLMs’ internal knowledge to explicitly complement the passages retrieved
from external sources. Then, Astute RAG conducts source-aware knowledge consolidation of
information from various internal and external sources. The desiderata is combining consistent
information, identifying conflicting information, and filtering out irrelevant information. Finally,
Astute RAG proposes answers based on each group of consistent passages and compares the an-
swers from different passage groups to determine the final answer. Our experiments involving Gemini
and Claude3 on various datasets (Section 4) demonstrate the superior performance of Astute RAG
compared to previous RAG approaches designed to be robust against retrieval corruptions. Moreover,
Astute RAG consistently outperforms baselines across different retrieval quality levels. Notably,
2Note that some passages may contain information indirectly relevant to the answer, but may unintentionally mislead or
distract LLMs.
3https://www.anthropic.com/claude
2
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Figure 2 | Imperfect retrieval (samples with low retrieval precision) is prevalent in real-world RAG.
Astute RAG is the only RAG method that achieves performance comparable to or even surpassing
conventional use of LLMs under the worst-case scenario where all retrieved passages are unhelpful.
Further analysis reveals the effectiveness of Astute RAG in resolving knowledge conflicts between
internal and external knowledge.
To conclude, our core contributions are threefold. First, we analyze RAG under realistic condi-
tions, identifying imperfect retrieval augmentation as a significant contributor to RAG failures and
pinpointing knowledge conflicts as the primary bottleneck in overcoming it. Second, we propose
Astute RAG, which explicitly addresses conflicts between LLM-internal and external knowledge,
thereby recovering from RAG failures. Third, experiments with various LLMs and datasets demonstrate
the effectiveness of Astute RAG, even in the most challenging scenarios.
2. Imperfect Retrieval: The Pitfall of RAG
To better showcase the common real-world challenges and to make better motivate for improved
methodological designs, we evaluate retrieval quality, end-to-end RAG performance, and knowledge
conflicts on a controlled set of data. The selected data encompass a diverse range of general, domain-
specific, and long-tail questions from NQ (Kwiatkowski et al., 2019), TriviaQA (Joshi et al., 2017),
BioASQ (Tsatsaronis et al., 2015), and PopQA (Mallen et al., 2023). Our analysis is based on realistic
retrieval results with Google Search4 as the retriever and the Web as the corpus. This setting allows
us to analyze the severity of imperfect retrieval in real-world RAG. Overall, we sample 1K short-form
QA instances from these datasets, and pair each instance with 10 retrieved passages.
Imperfect retrieval is common. We examine the occurrence of correct answers in retrieved passages
as an approximation of retrieval quality. Since we mainly focus on short-form QA which provides
most variants of the correct answer for each question, the approximation through string matching
can give us a rouge intuition of how precise the retrieval result is. Specifically, we define the retrieval
precision as the ratio of passages containing the correct answer for each instance:
Retrieval Precision = {number of retrieved passages containing correct answer}
{number of total retrieved passages}
As shown in Figure 2, although instances from different datasets exhibit different data distributions,
imperfect retrieval is prevalent. Specifically, ∼20% of the overall data have no mentions of the correct
answer within any retrieved passage, including 34% on NQ, 18% on TriviaQA, 24% on BioASQ, and
50% on PopQA. This finding also aligns with previous observation on information retrieval (Thakur
et al., 2024), that highlights that the number of positive passages can be very limited.
4https://developers.google.com/custom-search/v1/overview
3
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Figure 3 | Overview of the proposed Astute RAG framework. Astute RAG is designed to better
combine the information from the external sources (e.g. web, domain-specific corpora, knowledge
bases) and internal knowledge of the LLMs by employing a consolidation mechanism to address the
information conflicts, which eventually leads to better quality generated outputs.
Imperfect retrieval leads to RAG failures. We further analyze the relation between retrieval quality
and RAG performance. We compare the performance of Claude 3.5 Sonnet, with and without RAG
and report the results by retrieval precision in Figure 4. In general, RAG is helpful when the retrieval
precision is not lower than 20%. When the retrieval precision is close to 0, the model with RAG
performs much worse than without RAG, indicating that imperfect retrieval augmentation can be the
cause of RAG failures. This finding aligns with the previous observation from Yu et al. (2024) that
adding more retrieved passages does not necessarily lead to better performance, as the additional
passages might reduce the retrieval precision.
Knowledge conflicts widely exist in RAG failures. We provide an in-depth analyses of knowledge
conflicts between LLMs’ internal knowledge and retrieved passages from external sources. With
Claude 3.5 Sonnet as the LLM, Figure 1 shows that 19.2% of the overall data exhibit knowledge
conflicts, where either the answer with or without RAG is correct. Among the conflicting cases, the
internal knowledge is correct on 47.4% of them, while the external knowledge is correct on the
remaining 52.6%. These results emphasize the importance of effectively combining the internal and
external knowledge to overcome the inherent limitation of relying solely on either source. However,
previous work (Jin et al., 2024; Tan et al., 2024; Xie et al., 2024) show that LLMs might respond based
on misleading information rather than comprehensive understanding of the conflicting knowledge in
this context.
3. Astute RAG: Overcoming the Pitfall
We begin with formulating the problem of imperfect retrieval in RAG (Section 3.1). We then provide
an overview of Astute RAG, designed to overcome this problem (Section 3.2). Subsequently,
we delve into the three major steps of Astute RAG, including adaptive generation of internal
knowledge (Section 3.3), source-aware knowledge consolidation (Section 3.4), and answer finalization
(Section 3.5).
4
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Algorithm 1 Astute RAG
Require: Query 𝑞, Retrieved Passages 𝐸= [𝑒1, . . . , 𝑒𝑛], Large Language Model M, Number of Iteration
𝑡, Max Number of Generated Passages ˆ𝑚, Prompt Templates 𝑝𝑔𝑒𝑛, 𝑝𝑐𝑜𝑛, 𝑝𝑎𝑛𝑠
1: Adaptively generate passages: 𝐼←M(𝑝𝑔𝑒𝑛, 𝑞, ˆ𝑚)
⊲Section 3.3
2: Combine internal and external passages: 𝐷0 ←𝐸⊕𝐼
3: Assign passage sources: 𝑆0 ←[1{𝑑∈𝐸}for 𝑑in 𝐷0]
4: if 𝑡> 1 then
5:
for 𝑗= 1, . . . , 𝑡−1 do
⊲Section 3.4
6:
Consolidate knowledge: ⟨𝐷𝑗+1, 𝑆𝑗+1⟩←M(𝑝𝑐𝑜𝑛, 𝑞, ⟨𝐷0, 𝑆0⟩, ⟨𝐷𝑗, 𝑆𝑗⟩)
7:
end for
8:
Finally consolidate and answer: 𝑎←M(𝑝𝑎𝑛𝑠, 𝑞, ⟨𝐷0, 𝑆0⟩, ⟨𝐷𝑡−1, 𝑆𝑡−1⟩)
⊲Section 3.5
9: else
10:
Consolidate knowledge and finalize the answer: 𝑎←M(𝑝𝑎𝑛𝑠, 𝑞, ⟨𝐷0, 𝑆0⟩)
11: end if
12: return 𝑎
3.1. Problem Formulation
Our objective is to mitigate the effects of imperfect retrieval augmentation, resolve knowledge conflicts
between the LLM’s internal knowledge and external sources (such as custom/public corpora and
knowledge bases), and ultimately produce more accurate and reliable responses from LLMs.
Given a set of retrieved passages from external sources 𝐸= [𝑒1, . . . , 𝑒𝑛], a pre-trained LLM M
(accessible through prediction-only APIs, encompassing commercial black-box ones), and a query 𝑞,
the task is to generate the corresponding correct answer 𝑎∗. Notably, this setting is orthogonal to
prior work on improving the retriever, training LLMs, or conducting adaptive retrieval, which are
mainly preliminary steps.
3.2. Overview of the Framework
Astute RAG is designed to better leverage collective knowledge from both internal knowledge
of LLMs and external corpus, for more reliable responses. As shown in Figure 3 and Algorithm 1,
Astute RAG starts from acquiring the most accurate, relevant, and thorough passage set from the
LLMs’ internal knowledge. Then, internal and external knowledge are consolidated in an iterative way,
by comparing the generated and retrieved passages. Finally, the reliability of conflicting information
is compared and the final output is generated according to the most reliable knowledge.
3.3. Adaptive Generation of Internal Knowledge
In the first step, we elicit internal knowledge from LLMs. This LLM-internal knowledge, reflecting
the consensus from extensive pre-training and instruction-tuning data, can supplement any missing
information from the limited set of retrieved passages and enable mutual confirmation between
LLM-internal and external knowledge. This is especially valuable when the majority of retrieved
passages might be irrelevant or misleading. Specifically, we prompt LLMs to generate passages based
on the given question 𝑞, following Yu et al. (2023a). While Yu et al. (2023a) primarily focused on
generating diverse internal passages, we emphasize the importance of reliability and trustworthiness
of generated passages. To achieve this goal, we enhance the original method with constitutional
principles and adaptive generation.
Inspired by Constitutional AI (Bai et al., 2022), we provide constitutional principles indicating
5
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
the desired properties of internal passages in the prompt 𝑝𝑔𝑒𝑛(see Appendix A for details) to guide their
generation, emphasizing that the generated passages should be accurate, relevant, and hallucination-
free. Moreover, we allow the LLM to perform adaptive generation of passages in its internal knowledge.
The LLM can decide how many passages to generate by itself. Rather generating a fix number of
passages, we request the LLM to generate at most ˆ𝑚passages, each covering distinct information,
and to directly indicate if no more reliable information is available. This adaptive approach allows
the LLM to generate fewer passages (or even no passages at all) when the useful information within
internal knowledge is limited and more passages when there are multiple feasible answers in the
internal knowledge. In this step, the LLM generates 𝑚≤ˆ𝑚passages based on its internal knowledge:
𝐼= [𝑖1, . . . 𝑖𝑚] = M(𝑝𝑔𝑒𝑛, 𝑞, ˆ𝑚).
3.4. Iterative Source-aware Knowledge Consolidation
In the second step, we employ the LLM to explicitly consolidate information from both passages
generated from its internal knowledge and passages retrieved from external sources. Initially, we
combine passages from both internal and external knowledge sources 𝐷0 = 𝐸⊕𝐼.
We additionally ensure source-awareness by providing the source of each passage to LLMs when
consolidating knowledge. The source information (internal or external, such as a website) is helpful in
assessing the reliability of passages. Here, we provide the passage source as 𝑆0 = [1{𝑑∈𝐸}for 𝑑in 𝐷0].
To consolidate knowledge, we prompt the LLM (with 𝑝𝑐𝑜𝑛in Appendix A) to identify consistent
information across passages, detect conflicting information between each group of consistent passages,
and filter out irrelevant information. This step would regroup the unreliable knowledge in input
passages into fewer refined passages. The regrouped passages will also attribute their source to the
corresponding one or more input passages
⟨𝐷𝑗+1, 𝑆𝑗+1⟩= M(𝑝𝑐𝑜𝑛, 𝑞, ⟨𝐷0, 𝑆0⟩, ⟨𝐷𝑗, 𝑆𝑗⟩).
We find that this is especially helpful in comparing the reliability of conflicting knowledge and
addressing knowledge conflicts. Moreover, this knowledge consolidation process can run iteratively
for 𝑡times to improve the context to be more and more useful. Users can assign a larger number of
iterations when the context is lengthy.
3.5. Answer Finalization
In the last step, we prompt the LLM (with 𝑝𝑎𝑛𝑠in Appendix A) to generate one answer based on each
group of passages (⟨𝐷𝑡, 𝑆𝑡⟩), and then compare their reliability and select the most reliable one as
the final answer. This comparison allows the LLM to comprehensively consider knowledge source,
cross-source confirmation, frequency, and information thoroughness when making the final decision.
Notably, this step can be merged into the last knowledge consolidation step to reduce the inference
complexity (the amount of prediction API calls) using a combined prompt:
𝑎= M(𝑝𝑎𝑛𝑠, 𝑞, ⟨𝐷0, 𝑆0⟩, ⟨𝐷𝑡, 𝑆𝑡⟩).
When 𝑡= 1, the initial passages will be fed into the model directly for knowledge consolidation and
subsequent answering: 𝑎= M(𝑝𝑎𝑛𝑠, 𝑞, ⟨𝐷0, 𝑆0⟩).
4. Experiments
We evaluate the effectiveness of Astute RAG on overcoming imperfect retrieval augmentation and
addressing knowledge conflicts. In this section, we first introduce the experiment setting in detail
6
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Method
#API Calls
NQ
TriviaQA
BioASQ
PopQA
Overall
Claude 3.5 Sonnet (20240620)
No RAG
1
47.12
81.98
50.35
29.78
54.51
RAG
1
44.41
76.68
58.04
35.96
55.47
USC (Chen et al., 2024b)
4
48.14
80.21
61.54
37.64
58.73
GenRead (Yu et al., 2023a)
2
42.03
74.20
56.99
34.27
53.55
RobustRAG (Xiang et al., 2024)
11
47.80
78.09
56.29
37.08
56.53
InstructRAG (Wei et al., 2024)
1
47.12
83.04
58.04
41.01
58.83
Self-Route (Xu et al., 2024a)
1-2
47.46
78.80
59.09
41.01
58.06
Astute RAG (t=1)
2
52.20
84.10
60.14
44.38
61.71
Astute RAG (t=2)
3
53.22
84.45
61.89
44.94
62.67
Astute RAG (t=3)
4
53.56
84.45
62.24
44.94
62.86
Table 1 | Main results on Claude under zero-shot setting, showing the accuracy of different benchmark
methods vs. Astute RAG, along with their prediction complexity, in number of prediction API calls.
Best scores are in bold.
(Section 4.1). Then, we compare the performance of Astute RAG with various baselines on diverse
datasets (Section 4.2). Finally, we provide in-depth analyses (Section 4.3).
4.1. Experimental Settings
Datasets and metrics. We conduct experiments on the data collected in Section 2 consisting of
data from NQ, TriviaQA, BioASQ, and PopQA. For each instance from these datasets, we provide 10
passages collected under a realistic retrieval setting: for each question in our benchmark, we query
Google Search to retrieve the top 30 results and select the first 10 accessible websites. From each
retrieved website, we extract the paragraph corresponding to the snippet provided in Google Search
results as the retrieved passage.. Most of the retrieval results contains natural noise with irrelevant
or misleading information. We do not consider enhancements to the retrieval side, such as query
rewriting, as such enhancements are typically already incorporated into commercial information
retrieval systems. Notably, we do not select questions or annotate answers based on the retrieval
results. This setting allows us to analyze the severity of imperfect retrieval in real-world RAG. It
distinguishes our benchmark from previous ones that employ synthetic retrieval corruptions or that
unintentionally reduce the frequency of imperfect retrieval with biased construction protocols (Chen
et al., 2024a; Yang et al., 2024). We also evaluate our method on RGB (Chen et al., 2024a), a RAG
diagnostic benchmark evaluating several crucial RAG abilities. Specifically, we choose the English
subset of RGB focusing on noise robustness. The benchmark have positive and negative passage sets
for each question. We select five negative documents per question as the context to form a worst-case
scenario. All the data in these datasets are short-form QA. Following previous work (Mallen et al.,
2023; Wei et al., 2024; Xiang et al., 2024), a model response is considered correct if it contains the
ground-truth answer. To enhance evaluation reliability, we prompt LLMs to enclose the exact answer
within special tokens, extracting them as the final responses.
General Settings of LLMs and RAG. We conduct experiments on two advanced LLMs, including Gem-
ini 1.5 Pro5 (gemini-1.5-pro-002) and Claude 3.5 Sonnet6 (claude-3-5-sonnet@20240620).
The generation temperature is set to 0 and the maximum output tokens is set to 1,024, if not
specified otherwise. By default, the passages are presented in the prompt by reversed order. All
5https://deepmind.google/technologies/gemini/pro/
6https://www.anthropic.com/news/claude-3-5-sonnet
7
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Method
#API Calls
NQ
TriviaQA
BioASQ
PopQA
Overall
Gemini 1.5 Pro (002)
No RAG
1
44.75
80.21
45.80
25.28
51.34
RAG
1
42.71
75.97
55.24
33.71
53.65
USC (Chen et al., 2024b)
4
46.44
76.68
58.39
37.64
56.43
GenRead (Yu et al., 2023a)
2
45.08
77.39
54.90
34.27
54.70
RobustRAG (Xiang et al., 2024)8
11
34.24
67.49
44.06
32.02
45.59
InstructRAG (Wei et al., 2024)
1
46.78
80.57
54.90
34.83
56.14
Self-Route (Xu et al., 2024a)
1-2
47.46
79.86
58.04
38.20
57.58
Astute RAG (t=1)
2
50.17
81.63
58.04
40.45
59.21
Astute RAG (t=2)
3
51.53
81.27
58.74
40.45
59.69
Astute RAG (t=3)
4
48.47
80.21
60.14
42.13
59.21
Table 2 | Main results on Gemini under zero-shot setting, showing the accuracy of different benchmark
methods vs. Astute RAG, along with their prediction complexity, in number of prediction API calls.
Best scores are in bold.
experiments are under the zero-shot setting for controlled evaluation, where no demonstrations for
QA or method-specific steps are provided.
Baselines. We compare Astute RAG with various RAG methods designed for enhanced robustness
and representative inference strategies designed to improve response trustworthiness. USC (Chen
et al., 2024b) is the universal self-consistency method that samples multiple LLM responses given
the same context and aggregates the answers. It provides a reference of naive improvements using
additional API calls. The temperature for sampling responses in this baseline is set to 0.7. Genread
(Yu et al., 2023a) augments retrieved passages with LLM-generated passages. It provide a reference
of presenting passages from both internal and external knowledge in the prompt without effectively
combining them. RobustRAG (Xiang et al., 2024) aggregates answers from each independent passage
to provide certifiable robustness. We use the keyword aggregation variant as it is shown to be the
best-performing variant on advanced LLMs. InstructRAG (Wei et al., 2024) instructs the LLM to
provide a rationale connecting the answer with information in passages. For a fair comparison, we
use the instructions without training or in-context learning. Self-Route (Xu et al., 2024a) adaptively
switches between LLMs with and without RAG.7 This baseline provides a reference of switching
between LLMs’ internal and external knowledge.
Implementation Details of Astute RAG. The prompt templates for Astute RAG can be found
in Appendix A. By default, we use 2 API calls per query, setting 𝑡= 1 to merge the prompt for
knowledge consolidation and answer finalization. For adaptive generation of internal knowledge, we
prompt the LLM to generate no more than one passage.
4.2. Main Results
Table 1 and Table 2 presents the results on data with realistic retrieval augmentation for each dataset.
By comparing RAG and No RAG, we find that retrieved passages might not always bring benefits to
downstream performance – on NQ and TriviaQA, RAG performance lags behind No RAG. We attribute
this to that the questions being covered by the LLM’s internal knowledge and the noise in retrieval
7The original Self-Route switches between RAG and long-context LLMs, while our implementation switches between
LLMs with and without RAG to better align with the problem formulation in this paper.
8We observe a high refusal rate in responses of RobustRAG.
8
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
results misleading the LLM. In contrast, on BioASQ and PopQA, which focus on domain-specific and
long-tail questions, RAG significantly improves LLM performance. However, due to imperfect retrieval
augmentation, the absolute performance still remains to be unsatisfactory. Among all baselines, no
single method consistently outperforms others across all datasets. This observation highlights that
these baselines are tailored to distinct settings and may not be universally applicable. For instance,
InstructRAG is more effective on TriviaQA, achieving the best performance among all baselines with
both Claude and Gemini. In contrast, Self-Route performs better than InstructRAG on both NQ and
BioASQ. Moreover, RobustRAG achieves very different performance when applied to Gemini and
Claude. Through in-depth analysis, we find that RobustRAG with Gemini exhibits a high refusal rate
(refuse to answer) in responses. We attribute this instability to the varying method designs of the
baselines, which are tailored for different scenarios, resulting in inconsistent improvement across
datasets. Overall, InstructRAG and Self-Route demonstrates the best performance among all baselines
when applied to Claude and Gemini respectively. We also note that increasing the number of API calls
does not necessarily correlate with improved performance.
Astute RAG consistently outperforms baselines across all datasets of different properties. The
overall improvement compared with the best baseline is relatively 6.85% on Claude and 4.13% on
Gemini, and the improvements in domain-specific questions are much higher. These results highlight
the effectiveness of Astute RAG in overcoming imperfect retrieval augmentation. On Claude,
adding more iteration of knowledge consolidation leads to consist improvement. The improvement
margin becomes lower when 𝑡becomes larger. This is because after each iteration, the remaining
improvement space for knowledge consolidation becomes smaller. On Gemini, increasing 𝑡primarily
benefits BioASQ and PopQA. These two datasets rely more heavily on external knowledge, and iterative
knowledge consolidation helps mitigate noise within this external information. Performance on NQ
and TriviaQA does not improve further when 𝑡reaches 3. We attribute this to the less critical role of
external knowledge in these datasets. For setting consistency and efficiency, we set the parameter ˆ𝑚
to a smaller value, limiting the influence of internal knowledge.
4.3. Analyses
Performance by retrieval precision. We compare the performance of Astute RAG and baselines
across different subsets partitioned by their retrieval precision, on our collected data with Claude as the
LLM. As shown in Figure 4, Astute RAG achieves consistently better performance than all baselines
across different retrieval precision, indicating its effectiveness in improving RAG trustworthiness in
broad scenarios. Notably, Astute RAG does not sacrifice performance gain under high retrieval
quality in exchange for improvement under low retrieval quality. When the retrieval quality is
extremely low (close to zero retrieval precision), all other RAG variants underperforms the ’No RAG’
baseline, except for the proposed Astute RAG. This observation aligns with the worst-case results
on RGB. It demonstrates the difficulty in overcoming imperfect retrieval augmentation, and verify the
effectiveness of Astute RAG in doing so.
Effectiveness in addressing knowledge conflicts. We split our collected data in to three subset
according to the answers from Claude, with and without RAG. The answers from two inference
methods can be both correct, both incorrect, or conflicting with one being correct. These three subsets
represents the three situations between internal and external knowledge. The results are shown
in Figure 4. On the conflicting subset, Astute RAG successfully chooses the correct answer in
approximately 80% of cases, being the most effective method in addressing knowledge conflicts.
Notably, Astute RAG even brings performance improvement on the subset where neither internal
nor external knowledge alone leads to the correct answer. This indicates that Astute RAG can
effectively combine partially-correct information from LLM-internal and external knowledge, to
9
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
(a) Performance by Retrieval Precision (b) Performance by Knowledge Conflicts
(c) Worst-case Performance on RGB
Figure 4 | Analysis results. (a) Performance of Claude across different buckets ranked by retrieval
precision. (b) Performance of Claude on conflicting and consistent instances between No RAG
and RAG. (c) Worst-case performance of Claude on RGB, where all retrieved passages are negative.
Astute RAG reaches a performance close to No RAG, while other RAG systems are far behind.
achieve the correct answer through collective information across them.
Worst-case performance on RGB.. Figure 4 presents the results under the worst-case setting on RGB
where all retrieved documents are negative. It demonstrates the noise robustness of Astute RAG
and baseline RAG methods. The performance gap between RAG and No RAG exceeds 50 points,
highlighting the detrimental impact of imperfect retrieval results and emphasizing the importance of
providing robust safeguards against worst-case scenarios. While the baseline RAG methods outperform
the original RAG, they still obviously fall behind No RAG. Astute RAG is the only RAG method
that reaches a performance close to No RAG under the worst-case scenario, further supporting its
effectiveness in addressing imperfect retrieval augmentation.
Qualitative study. In Figure 5, we present two representative examples showing the intermediate
outputs of Astute RAG. In the first example, LLM without RAG generates a wrong answer, while
RAG returns a correct answer. Astute RAG successfully identified the incorrect information in
its generated passage and an external passage, avoiding confirmation bias Tan et al. (2024). In
the second example, LLM alone is correct, while RAG is incorrect due to the noisy retrieval results.
Astute RAG detected the correct answer from noisy retrieved information by checking with its
internal knowledge.
5. Related Work
Retrieval augmented generation (RAG) seeks to address the inherent knowledge limitation of LLMs
with passages retrieved from external sources of information such as private corpora or public
knowledge bases (Borgeaud et al., 2022; Guu et al., 2020; Lewis et al., 2020). Given the widespread
adoption of RAG in various real-world applications, including risk-sensitive domains, the negative
impact of noisy information within retrieved passages has garnered increasing attention (Cuconasu
et al., 2024). Recent work has sought to enhance the robustness of RAG systems against noise from
various perspectives, including training LLMs with noisy context (Fang et al., 2024; Pan et al., 2024;
Yoran et al., 2024; Yu et al., 2023b), training small models to filter out irrelevant passages (Wang
et al., 2023c; Xu et al., 2023), passage reranking (Glass et al., 2022; Yu et al., 2024), dynamic and
iterative retrieval (Asai et al., 2023; Jiang et al., 2023; Yan et al., 2024), query rewriting (Ma et al.,
10
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Figure 5 | Qualitative examples. Top: Astute RAG identified the error in internal knowledge (i.e.,
generated passage) by confirming with external sources. Bottom: Astute RAG detected the correct
answer from noisy retrieved information by checking with its internal knowledge. Standard RAG does
not provide an answer because the retrieved passages are too noisy.
2023), and speculative drafting (Wang et al., 2024). These studies focus on distinct modules or stages
of RAG systems and are orthogonal to our work.
Our work focuses on enhancing RAG robustness at the post-retrieval stage, after retrieved passages
have been provided. On this topic, RobustRAG (Xiang et al., 2024) aggregates answers from each
independent passage to provide certifiable robustness. InstructRAG (Wei et al., 2024) instructs the
LLM to provide a rationale connecting the answer with information in passages. MADRA (Wang
et al., 2023b) applies multi-agent debate to select helpful evidence. However, these works do not
explicitly incorporate internal knowledge to recover from RAG failures and may therefore collapse
when the majority of retrieved passages are negative. In terms of emphasizing internal knowledge of
LLMs in RAG, recent work has explored using LLM-generated passage as context (Yu et al., 2023a;
Zhang et al., 2023), adaptively switching between LLMs with and without RAG (Jeong et al., 2024;
Mallen et al., 2023; Xu et al., 2024a), and combining answers from internal and external knowledge
11
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
through contrastive decoding (Jin et al., 2024; Zhao et al., 2024). We focus on a black-box setting
where no further training is required, directly addressing knowledge conflicts to combine the helpful
information from both sides and achieve more reliable answers.
6. Conclusion
Our paper investigates the impact of imperfect retrieval on the performance of RAG systems and
identifies knowledge conflicts as a key challenge. To address this, we introduce Astute RAG, a
novel approach that leverages the internal knowledge of LLMs and iteratively refines the generated
responses by consolidating internal and external knowledge in a source way. Our empirical results
demonstrate the effectiveness of Astute RAG in mitigating the negative effects of imperfect retrieval
and improving the robustness of RAG systems, particularly in challenging scenarios with unreliable
external sources.
Among the limitations, Astute RAG’s effectiveness hinges on the capabilities of advanced LLMs
with strong instruction-following and reasoning abilities, hence potentially more limited applicability
with less sophisticated LLMs. As an important future direction, extending the experimental setup to
include longer outputs would be important, where the challenges of imperfect retrieval and knowledge
conflicts may be even more pronounced. Furthermore, a comprehensive analysis of the impact of
various context types (Balachandran et al., 2024) would enhance the understanding of the proposed
method’s effectiveness. Future work can also extend our method beyond LLMs and RAG, such as
addressing knowledge conflicts in multimodal settings (Zhu et al., 2024).
Acknowledgement
We would like to thank Jinsung Yoon for valuable discussions and insights that helped to improve
this paper. We would also like to thank all other colleagues from Google Cloud AI Research for their
valuable feedback.
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Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
A. Prompt Template for Astute RAG
Prompt for Adaptive Passage Generation (𝑝𝑔𝑒𝑛)
Generate a document that provides accurate and relevant information to answer the given
question. If the information is unclear or uncertain, explicitly state ’I don’t know’ to avoid any
hallucinations.
Question: {question} Document:
Prompt for Iterative Knowledge Consolidation (𝑝𝑐𝑜𝑛)
Task: Consolidate information from both your own memorized documents and externally
retrieved documents in response to the given question.
* For documents that provide consistent information, cluster them together and sum-
marize the key details into a single, concise document.
* For documents with conflicting information, separate them into distinct documents, ensuring
each captures the unique perspective or data.
* Exclude any information irrelevant to the query.
For each new document created, clearly indicate:
* Whether the source was from memory or an external retrieval.
* The original document numbers for transparency.
Initial Context: {context}
Last Context: {context}
Question: {question}
New Context:
16
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Prompt for Knowledge Consolidation and Answer Finalization (𝑝𝑎𝑛𝑠)
Task: Answer a given question using the consolidated information from both your own
memorized documents and externally retrieved documents.
Step 1: Consolidate information
* For documents that provide consistent information, cluster them together and summarize
the key details into a single, concise document.
* For documents with conflicting information, separate them into distinct documents, ensuring
each captures the unique perspective or data.
* Exclude any information irrelevant to the query.
For each new document created, clearly indicate:
* Whether the source was from memory or an external retrieval.
* The original document numbers for transparency.
Step 2: Propose Answers and Assign Confidence
For each group of documents, propose a possible answer and assign a confidence score based
on the credibility and agreement of the information.
Step 3: Select the Final Answer
After evaluating all groups, select the most accurate and well-supported answer.
Highlight your exact answer within <ANSWER> your answer </ANSWER>.
Initial Context: {context_init}
[Consolidated Context: {context}] # optional
Question: {question}
Answer:
B. Data Collection
Encompassing a diverse range of natural questions, our benchmark consists of realistic retrieval results
with Google Search9 as the retriever and the Web as the corpus. Notably, we do not select questions
or annotate answers based on the retrieval results. This setting allows us to analyze the severity of
imperfect retrieval in real-world RAG. It distinguishes our benchmark from previous ones that employ
synthetic retrieval corruptions or that unintentionally reduce the frequency of imperfect retrieval
with biased construction protocols (Chen et al., 2024a; Yang et al., 2024). Overall, our benchmark
contains 1,042 short-form question-answer pairs, each paired with 10 retrieved passages.
Question-answer pairs. We consider question-answer pairs from four datasets of different
properties spanning across general questions, domain-specific questions, and long-tail questions. NQ
(Kwiatkowski et al., 2019) and TriviaQA (Joshi et al., 2017) are two widely-studied question-answering
(QA) datasets in general domains. BioASQ (Tsatsaronis et al., 2015) is from biomedical domain
that has demonstrated significant benefits from RAG when general-purpose LLMs are considered.
PopQA (Mallen et al., 2023) focuses on long-tail knowledge and has been shown to be challenging
for even advanced LLMs to solve without external knowledge. All these datasets contain questions
with short-form answers and most of them list all valid answer variants. This format can support
automatic verification of answer appearance in retrieved passages and model responses, leading to
more precise evaluations.
9https://developers.google.com/custom-search/v1/overview
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Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Retrieval process. For each question in our benchmark, we query Google Search to retrieve the
top 30 results and select the first 10 accessible websites. From each retrieved website, we extract the
paragraph corresponding to the snippet provided in Google Search results as the retrieved passage.
We do not consider enhancements to the retrieval side, such as query rewriting, as such enhancements
are typically already incorporated into commercial information retrieval systems.
18