Evaluation of Attribution Bias in Generator-Informed Retrieval-Augmented Large Language Models
Authors: Amin Abolghasemi, Leif Azzopardi, Seyyed Hadi Hashemi, Maarten de Rijke, Suzan Verberne
Accepted at: acl25

Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models

Amin Abolghasemi1    Leif Azzopardi2    Seyyed Hadi Hashemi3
Maarten de Rijke4    Suzan Verberne1
1Leiden University, Netherlands    2University of Strathclyde, UK
3eBay Inc., Netherlands    4University of Amsterdam, Netherlands
{m.a.abolghasemi, s.verberne}@liacs.leidenuniv.nl
leif.azzopardi@strath.ac.uk    shashemi@ebay.com    m.derijke@uva.nl

Abstract

Attributing answers to source documents is an approach used to enhance the verifiability of a model’s output in retrieval-augmented generation (RAG). Prior work has mainly focused on improving and evaluating the attribution quality of large language models (LLMs) in RAG, but this may come at the expense of inducing biases in the attribution of answers. We define and examine two aspects in the evaluation of LLMs in RAG pipelines, namely attribution sensitivity and bias with respect to authorship information. We explicitly inform an LLM about the authors of source documents, instruct it to attribute its answers, and analyze (i) how sensitive the LLM’s output is to the author of source documents, and (ii) whether the LLM exhibits a bias towards human-written or AI-generated source documents. We design an experimental setup in which we use counterfactual evaluation to study three LLMs in terms of their attribution sensitivity and bias in RAG pipelines. Our results show that adding authorship information to source documents can significantly change the attribution quality of LLMs by 3 to 18%. We show that LLMs can have an attribution bias towards explicit human authorship, which can serve as a competing hypothesis for findings of prior work that shows that LLM-generated content may be preferred over human-written contents. Our findings indicate that metadata of source documents can influence LLMs’ trust, and how they attribute their answers. Furthermore, our research highlights attribution bias and sensitivity as a novel aspect of the brittleness of LLMs.

1 Introduction

The goal of retrieval-augmented generation (RAG) is to generate an answer to a given question using a set of top-k retrieved documents as context (Lewis et al., 2020). Large language models (LLMs) have been a crucial part of RAG pipelines, mainly as the generator component (Asai et al., 2023; Jeong et al., 2024; Lee et al., 2024; Li et al., 2024a). Although the use of LLMs offers potential benefits, it also presents considerable risks, as they are prone to generate false or hallucinated claims (Ji et al., 2023). This is important as such claims may misguide users, particularly when they are being used in critical fields such as the legal or medical domain (Augenstein et al., 2023; Malaviya et al., 2024; Xiong et al., 2024).

Enabling LLMs to attribute their answer to the source of information has been proposed as a promising direction towards reducing the likelihood of such potential harms (Li et al., 2024b,c; Patel et al., 2024). This attribution could assist users in tracing and understanding the basis of the information that LLMs are generating (Gao et al., 2023; Huang and Chang, 2024). There are many prior studies on answer attribution in RAG pipelines (Bohnet et al., 2022; Hu et al., 2024; Li

![Retrieval-augmented answer/attribution generation using two LLMs. Together with the question, retrieval results are given to the LLMs in order to generate the answer.](page_1012_670_393_312.png)

Figure 1: Retrieval-augmented answer/attribution generation using two LLMs. Together with the question, retrieval results are given to the LLMs in order to generate the answer.
et al., 2024b; Menick et al., 2022; Muller et al., 2023; Stolfo, 2024). As Figure 1 illustrates, LLMs are susceptible to making mistakes when attributing their answers to the input documents in RAG. Moreover, enabling LLMs in RAG to attribute their answer may come at the expense of inducing biases, as LLMs may carry potential biases (Esiobu et al., 2023; Ozeki et al., 2024; Xie et al.; Ziemst et al., 2024). For instance, Tan et al. (2024) show that retrieval-augmented LLMs can be biased towards selecting their own generated text when this kind of content is present in their input. Inspecting these biases is of paramount importance as they can be leveraged for both positive and negative purposes.

In this paper, we study the performance of LLMs in terms of attribution sensitivity and attribution bias w.r.t. authorship information. When we explicitly inform LLMs about the authors of input documents, and instruct them to attribute their answers to the input documents (by providing citations to these documents), how sensitive are they to the authorship information of input documents? And are they biased towards either human or LLM authorship of input documents? To address these questions, we design a simulated evaluation setup in which we measure to what extent knowing the type of author of input documents affects the quality of attribution (citation) in LLMs.

Prior work has indicated that LLM-generated content may consistently outperform human-authored content in search rankings, which, in turn, results in reducing the presence and exposure of human contributions online (Chen et al., 2024; Dai et al., 2024). Inspired by these studies, we compare human-written documents against LLM-generated documents. We follow prior work in attribution generation by prompting LLMs to generate citations to the input documents (Gao et al., 2023; Yue et al., 2023). Furthermore, we use counterfactual evaluation (Abolghasemi et al., 2024a; Goldfarb-Tarrant et al., 2023; Howard et al., 2024; Huang et al., 2020; Xie et al., 2023) to measure both authorship sensitivity and authorship bias of LLMs in RAG pipelines. This approach can be used more generally to measure algorithmic sensitivity or bias in a model or method: using counterfactual scenarios to see if changing certain characteristics leads to different outcomes.

Our experimental results show that three LLMs (Mistral, Llama3 and GPT-4) are sensitive to authorship information that is included in the input documents prior to the generation. Moreover, we show that these models carry a bias towards human authorship against LLM authorship: they are more likely to attribute their answers to documents that are explicitly labelled as having been written by humans (even if the documents are actually generated by LLMs).

We summarize our contributions as follows:

• We define and study attribution sensitivity and bias w.r.t. authorship information, as a novel aspect of trustworthiness and brittleness in retrieval-augmented LLMs.
• We propose a systematic evaluation framework for measuring attribution sensitivity and bias.
• We show that adding authorship information (as metadata) to source documents may lead to statistically significant changes in the attribution quality of retrieval-augmented LLMs.
• We show that LLMs may have an attribution bias towards explicit human authorship, which can serve as a competing hypothesis for findings of prior work that shows that LLM-generated content is preferred over human-written content by LLMs.1

2 Background

Retrieval-Augmented Generation. Given a question \( q \) and a set of top-\( k \) retrieved documents \( \mathcal{D} = \{d_1, d_2, \ldots, d_k\} \) from a collection \( \mathcal{C} \), the goal of retrieval-augmented generation (RAG) is to generate an answer for \( q \) using \( \mathcal{D} \) as context. LLMs are currently an important component of RAG pipelines, acting as the generator. The generator is given \( q \), \( \mathcal{D} \), and an instruction prompt on how to generate the answer (Jeong et al., 2024; Lee et al., 2024; Li et al., 2024a). Using top-\( k \) retrieved documents helps LLMs to be exposed to information that it might not have been trained/fine-tuned with during development. These documents are commonly retrieved using an effective sparse and/or dense retriever (Lewis et al., 2020; Rau et al., 2024).

Attributive RAG. LLMs are prone to generate hallucinated (and even factually incorrect) answers (Ji et al., 2023; Rawte et al., 2023; Yue et al., 2024). Attributing answers in RAG with LLMs is an approach taken as a step towards ensuring the veracity of the output of these models (Bohnet et al., 2022; Hu et al., 2024; Kamaloo et al., 2023; Khalifa et al.; Li et al., 2024b). Menick et al. (2022) teach

1Our code is available at https://github.com/aminenv/attrieval
language models to support answers with verified quotes. Ye et al. (2024) propose a learning-based framework in which they fine-tune LLMs to generate citations, as opposed to prompting or relying on post-hoc attribution. Stolfo (2024) analyzes whether every generated sentence in the output of LLMs is grounded in the retrieved documents or the LLM’s pre-training data.

3 Methodology

We aim to measure the attribution sensitivity and bias of LLMs in RAG settings. We investigate to what extent the attribution quality of LLMs is affected by authorship information. To this end, we use counterfactual evaluation (Bottou et al., 2013; Gardner et al., 2020; Wang and Culotta, 2021). Counterfactual evaluation has been used across various natural language processing and information retrieval tasks (Abolghasemi et al., 2024a,b; Goldfarb-Tarrant et al., 2023; Howard et al., 2024; Huang et al., 2020). This approach evaluates how a model’s predictions change when a specific feature or set of features is altered while keeping everything else constant. In our case, the change is to add authorship information to the input documents of an LLM in a RAG setting. By doing so, we can evaluate the model’s reliance on, bias towards, or sensitivity to that feature. To this end, we first generate answers with LLMs in a RAG setting using three RAG modes, as shown in Figure 2.

3.1 RAG Modes

Given a query \( q \) and a set of top-\( k \) retrieved documents \( \mathcal{D}_q \) for \( q \), we define three modes, based on authorship information of these documents that we provide to the answer generator LLM.

Vanilla RAG. In this mode, each document in \( \mathcal{D} \) is given to the LLMs without information about who the authors are. This is the plain input format for input documents as shown in the input prompt for vanilla answer/attribution generation in Figure 3.

Authorship Informed RAG. In this mode, we inform the LLM about the actual author of each document. We denote the authorship of either an LLM or a human using [LLM] and [Human] tokens as shown by Figure 7 in the Appendix.2

Counterfactual-Authorship Informed RAG. In this mode, we assign counterfactual authorship for each document. If a document is written by a human, the counterfactual authorship of this document is [LLM]. In contrast, if a document is generated by an LLM, its counterfactual authorship is [Human]. By doing so, we can investigate to what extent being written by either human or LLM affects the attribution quality of LLM. The prompt used for this mode is the same as the one for Authorship Informed RAG mode.

Figure 2 shows the three RAG modes for a setting where the relevant documents are LLM-written and the non-relevant documents are human-written.

![Three RAG modes diagram showing LLM, Human, and Authorship Informed RAG configurations](page_1016_186_420_246.png)

Figure 2: Three RAG modes (Section 3.1) for the setting with LLM actual authorship for relevant documents and Human actual authorship for non-relevant documents. The text in a rectangle denotes the actual generator (i.e., author) of each document. The text in the blue tags denotes the authorship label about which we inform the answer/attribution generator LLM.

3.2 Answer/Attribution Generation

In order to generate answers with each of the aforementioned RAG modes, we experiment with three LLMs: Mistral (Jiang et al., 2023), Llama3 (Dubey et al., 2024) and GPT-4 (OpenAI, 2023). Figure 3 shows the prompt used for vanilla answer generation. Figure 7 in the Appendix shows the prompt used for Authorship-Informed and Counterfactual-Authorship Informed answer generation. We follow prior work (Gao et al., 2023) in curating our prompts for this task.

3.3 Evaluation Metrics

Attribution Quality. We use precision and recall for evaluating the quality of attribution, i.e., how well the LLMs cite the relevant input documents. Precision of attribution for a single query is the fraction of correct citations among all cited documents

2In Section C in the Appendix, we study and provide results on replacing [Human] with a set of actual {firstname, lastname} as authors.
Instruction: Write a concise answer for the given question (query) based on the provided search result documents, and cite them properly using [0][1][2] etc.

Please take these strict considerations into account during answer generation:
1. Documents are retrieved by a search engine. As such, not all the documents are relevant to the query. Only use and cite the relevant documents that contain the answer.
2. Do not analyze irrelevant documents.

Search Results:

Document [0]({text of Document [0]})
Document [1]({text of Document [1]})
...
Document [9]({text of Document [9]})

Question: {query}.

Figure 3: Prompt used for vanilla retrieval-augmented answer generation.

in the output of an LLM. Recall is the fraction of cited relevant documents out of all relevant documents (Djeddal et al., 2024). We use the queries that have only one relevant document containing the ground-truth answer in their top-\( k \) retrieved list of documents.

Attribution Sensitivity. In order to measure the sensitivity of LLMs in RAG pipelines towards knowing authors of input documents in comparison to not knowing it, we use counterfactual evaluation and define a metric called Counterfactually-estimated Attribution Sensitivity (CAS):

\[
\mathrm{CAS}(Q) = \frac{1}{|Q|} \sum_{q \in Q} |M^q_{\text{Informed}} - M^q_{\text{Vanilla}}|.
\]

Here, \( M^q \) represents the precision and recall metrics for query \( q \), i.e., attribution quality for query \( q \). For a single query \( q \), CAS measures the difference between a base setup (the vanilla RAG mode) and a counterfactual setup (the authorship informed RAG mode) for the same set of input documents.

Attribution Bias. In order to measure the attribution bias of LLMs in RAG pipelines we use counterfactual evaluation and define a metric called Counterfactually-estimated Attribution Bias (CAB):

\[
\mathrm{CAB}(Q) = \frac{\omega}{|Q|} \sum_{q \in Q} M^q_{\text{Informed}} - M^q_{\text{CF-informed}}
\]

\[
\omega = \begin{cases}
1, & \text{if } L_f(\mathcal{R}) = [\text{Human}], L_f(\mathcal{N}) = [\text{LLM}] \\
-1, & \text{otherwise.}
\end{cases}
\]

Here, \( M^q \) represents the precision and recall metrics, i.e., attribution quality, for query \( q \), given the set of retrieved relevant documents \( \mathcal{R} \), and the set of retrieved non-relevant documents \( \mathcal{N} \). \( L_f(\mathcal{X}) \) stands for the authorship label of the set of documents \( \mathcal{X} \) in the first term of Eq. 2, i.e., corresponding to \( M^q_{\text{Informed}} \). For example, if we use human-written version of relevant documents (\( \mathcal{R} \)), and LLM-written version of non-relevant document (\( \mathcal{N} \)), and we label them with their actual generators (authors), i.e., we use authorship-informed RAG mode, then \( L_f(\mathcal{R}) \) is equal to [Human], and \( L_f(\mathcal{R}) \) is equal to [LLM]. CAB measures the difference between metric values of a base setup (the Authorship Informed RAG mode) and a counterfactual setup (the Counterfactual-authorship Informed RAG mode) for the same set of input documents consisting of \( \mathcal{R} \), and \( \mathcal{N} \). \( \omega \) determines the direction of bias towards either human or LLMs: if the set of relevant documents (\( \mathcal{R} \)) and non-relevant documents (\( \mathcal{N} \)) are respectively written by Human and LLM (i.e., \( L_f(\mathcal{R}) = [\text{Human}], L_f(\mathcal{N}) = [\text{LLM}] \)), for a single query, a positive difference (\( M_{\text{Informed}} - M_{\text{CF-informed}} \)) indicates bias towards human authorship, and a negative difference shows bias towards LLM authorship. In contrast, if the set of relevant documents (\( \mathcal{R} \)) and non-relevant documents (\( \mathcal{N} \)) are respectively written by LLM and Human (i.e., \( L_f(\mathcal{R}) = [\text{LLM}], L_f(\mathcal{N}) = [\text{Human}] \)), a negative difference (\( M_{\text{Informed}} - M_{\text{CF-informed}} \)) indicates a bias towards human authors, and a positive difference shows bias towards LLMs. We use \( \omega \) to align these two conditions of actual authorship of input documents.

Attribution Confidence. To better explore the performance of LLMs in attribution generation, we analyze whether the LLMs are more confident
when they attribute to certain types of document. To this aim, we look into the average probability of generation for attribution tokens, i.e., citation numbers (0, 1, ...):

\[
\mathrm{AC}(\mathcal{S}) = \frac{\sum_{q \in Q} \sum_{c_i \in C_q} p(c_i|q, D_q) \cdot \mathbbm{1}[c_i \in \mathcal{S}]}{\sum_{q \in Q} \sum_{c_i \in C_q} \mathbbm{1}[c_i \in \mathcal{S}]}, \tag{4}
\]

where \( q \) is a query in the set of queries \( Q \), \( D_q \) is the top-\( k \) retrieved documents for \( q \), \( C_q \) stands for all attribution numbers in the answer to \( q_i \), \( c_i \in \{0, 1, \ldots, k\} \), \( \mathcal{S} \) indicates a set of documents, e.g., the set of relevant documents for all queries, and \( p(c_i|q, D_q) \) shows the probability of generation for the attribution token \( c_i \) in the answer provided by LLM given query \( q \) and its top-\( k \) retrieved documents \( D_q \). \( \mathbbm{1}[c_i \in \mathcal{S}] \) equals 1 if \( c_i \in \mathcal{S} \).

Answer Correctness. In order to evaluate the quality of the generated answer, we follow (Gao et al., 2023; Petroni et al., 2021) and use automatic evaluation. Following (Gao et al., 2023; Stolfo, 2024), we use the normalized human-generated answer in the benchmark as the ground-truth answer and adopt Exact Match (EM) (Sirivardhana et al., 2023; Wang et al., 2023) as the evaluation metric for answer correctness (see example in Figure 16).

4 Experimental Settings

Models. We use gpt-4-0314 (OpenAI, 2023), meta-llama/Meta-Llama-3-8B-Instruct,\footnote{https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct} and mistralai/Mistral-7B-Instruct-v0.3\footnote{https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3} as answer generator LLMs in our RAG pipelines. We refer to these models as GPT-4, Llama3, and Mistral, respectively.

Benchmarks. We use two benchmarks in our experiments: Natural Questions (NQ) (Kwiatkowski et al., 2019) and MS MARCO Question Answering (Bajaj et al., 2016) (to which we refer as MS MARCO). For each benchmark, we randomly sample 500 queries. To retrieve top-\( k \) passages for each query in the NQ benchmark, we use BM25, a widely-used lexical matching retrieval model. For queries in the MS MARCO benchmark, we use passages that are extracted from relevant web documents using the state-of-the-art passage retrieval system at Bing (Bajaj et al., 2016). We note that we study the effect of different retrievers and different number of retrieved source documents in Section D and E in the Appendix, respectively.

Synthetic Collection. To construct LLM-written documents, we use Llama3 to re-write a given document from our collections without adding/removing information to/from the document. Specifically, we use a low temperature close to 0 as it makes the LLM extremely restrictive, focusing only on the most probable tokens resulting in (highly) deterministic outputs. The reason for not generating the documents from scratch is to make sure we keep the relevance/non-relevance status of documents w.r.t a query. To ensure the quality of synthetic passages, we conduct a number of annotation steps using two expert annotators. This is detailed in Section A in the Appendix. Importantly, in Section 5, we show that even without using LLM-generated documents (i.e., only designating [Human] and [LLM] as authors of documents to the original input documents) findings are the same as when we use actual LLM-generated documents.

5 Experimental Results

In this section, we explore the performance of LLMs for attributing their answer to top-\( k \) retrieved source documents using the evaluation metrics introduced in Section 3. All significance tests in the result tables are according to a paired t-test with \( p < 0.05 \).

Attribution Quality. Table 1 shows the results of attribution by three LLMs, Mistral, Llama3 and GPT-4, under different settings for NQ benchmark. Besides, Table 11 in the Appendix shows the same set of results for the MS MARCO benchmark. The two columns “Relevant documents” and “Non-relevant documents” indicate the actual generator (author) of these documents. The column “RAG mode” indicates how we inform the answer generator LLMs about the authorship label of relevant and non-relevant documents, as described in Section 3.1: in the “Vanilla” RAG mode, no information regarding the generator (author) of the input source documents is given to the LLM. In the “Informed” RAG mode the LLM is informed about the actual generator of the input source documents, and in the “CF-Informed” RAG mode the LLM is provided with counterfactual authorship information. As Table 1 shows, the three LLMs (Mistral, Llama3 and GPT-4) fall short of perfectly grounding their answers to the relevant documents of a given question, which is in line with the findings of
<table>
  <tr>
    <th>Answer generator</th>
    <th>Relevant documents</th>
    <th>Non-relevant documents</th>
    <th>RAG mode</th>
    <th colspan="3">Attribution quality</th>
    <th>Correctness</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th>Precision</th>
    <th>Recall</th>
    <th>EM</th>
    <th></th>
  </tr>
  <tr>
    <td>NQ</td>
    <td></td>
    <td></td>
    <td>Vanilla</td>
    <td>47.6</td>
    <td>76.6</td>
    <td>0.722</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="3">Mistral</td>
    <td>LLM</td>
    <td>Human</td>
    <td>Informed</td>
    <td>42.1</td>
    <td>68.2</td>
    <td>0.730</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>CF-informed</td>
    <td><b>52.7†</b></td>
    <td><b>77.8†</b></td>
    <td><b>0.738</b></td>
    <td></td>
  </tr>
  <tr>
    <td>Human</td>
    <td>LLM</td>
    <td>Informed</td>
    <td><b>53.4†</b></td>
    <td><b>77.8†</b></td>
    <td>0.774</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>CF-informed</td>
    <td>44.0</td>
    <td>70.2</td>
    <td>0.772</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="3">Llama3</td>
    <td>LLM</td>
    <td>Human</td>
    <td>Vanilla</td>
    <td>49.2</td>
    <td>69.2</td>
    <td>0.742</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td>45.4</td>
    <td>69.6</td>
    <td>0.730</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>CF-informed</td>
    <td><b>57.2†</b></td>
    <td><b>77.6†</b></td>
    <td><b>0.748</b></td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td>LLM</td>
    <td>Human</td>
    <td>Vanilla</td>
    <td>53.5</td>
    <td>71.0</td>
    <td>0.766</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td><b>59.9†</b></td>
    <td><b>77.8†</b></td>
    <td><b>0.790</b></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>CF-informed</td>
    <td>44.8</td>
    <td>69.2</td>
    <td>0.762</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>Human</td>
    <td>Vanilla</td>
    <td>63.3</td>
    <td>68.8</td>
    <td>0.736</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td>59.7</td>
    <td>64.6</td>
    <td>0.740</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>CF-informed</td>
    <td><b>65.9†</b></td>
    <td><b>72.2†</b></td>
    <td><b>0.742</b></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td>Vanilla</td>
    <td>64.1</td>
    <td>68.8</td>
    <td>0.760</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td><b>66.1</b></td>
    <td><b>72.2†</b></td>
    <td><b>0.776</b></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>CF-informed</td>
    <td>60.3</td>
    <td>65.0</td>
    <td>0.758</td>
    <td></td>
  </tr>
</table>

Table 1: Quality of attribution and answer correctness. The columns “Relevant Documents” and “Non-relevant Documents” refer to the actual authorship of input documents. Informed refers to the authorship-informed RAG and CF-informed refers to counterfactual-authorship informed RAG (Section 3.1). † indicates statistically significant improvements over the two other RAG Modes in each combination of “Relevant” and “Non-relevant” documents.

prior work (Djeddal et al., 2024; Gao et al., 2023; Li et al., 2024c).

<table>
  <tr>
    <th>Answer generator</th>
    <th>Relevant documents</th>
    <th>Non-relevant documents</th>
    <th>CAS</th>
    <th>ΔPrecision</th>
    <th>ΔRecall</th>
  </tr>
  <tr>
    <td>NQ</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>LLM</td>
    <td>Human</td>
    <td></td>
    <td>16.2†</td>
    <td>17.2†</td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td></td>
    <td>20.1</td>
    <td>17.0</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>LLM</td>
    <td>Human</td>
    <td></td>
    <td>13.2†</td>
    <td>14.8</td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td></td>
    <td>17.7†</td>
    <td>16.0†</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>LLM</td>
    <td>Human</td>
    <td></td>
    <td>9.7†</td>
    <td>10.2†</td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td></td>
    <td>8.7</td>
    <td>9.0†</td>
  </tr>
  <tr>
    <td>MS MARCO</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>LLM</td>
    <td>Human</td>
    <td></td>
    <td>10.9</td>
    <td>21.4†</td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td></td>
    <td>12.9†</td>
    <td>16.6</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>LLM</td>
    <td>Human</td>
    <td></td>
    <td>12.9†</td>
    <td>20.4†</td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td></td>
    <td>17.8†</td>
    <td>19.6†</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>LLM</td>
    <td>Human</td>
    <td></td>
    <td>8.2†</td>
    <td>9.6†</td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td></td>
    <td>10.9</td>
    <td>15.8†</td>
  </tr>
</table>

Table 2: Attribution sensitivity (CAS) results. Values range from 0 (minimum sensitivity) to 100 (maximum sensitivity). † indicates statistically significant values.

Attribution Sensitivity and Bias. Table 3 shows the attribution bias results in terms of CAB (Eq. 2). All three LLMs, Mistral, Llama3, and GPT-4, carry a bias towards human authorship in the input documents. Moreover, on both datasets, NQ and MS MARCO, Mistral and Llama3 have higher bias values than GPT-4. Besides, Table 2 shows the attribution sensitivity results in terms of CAS (Eq. 1). All three LLMs, Mistral, Llama3, and GPT-4, show sensitivity towards the inclusion of authorship information for the input documents of LLMs. Moreover, similar to the attribution bias values in Table 3, Mistral and Llama3 carry a higher attribution sensitivity than GPT-4. We note that we conducted experiments using different prompts and observed that the findings remained consistent across multiple runs.

Mixed RAG Mode. To better disentangle the effect of LLM generated text qualities (e.g., a potential implicit bias of LLMs towards LLM-written documents (Tan et al., 2024)) from the impact of authorship information, we now use the same set of documents in the input of LLM in the RAG, and only use different authorship labels for relevant and non-relevant documents. For this new setup, to which we refer as the Mixed RAG mode, we evaluate both a complete set of synthetic documents (i.e., for both relevant and non-relevant) and a complete set of human-written documents.
<table>
  <tr>
    <th>Answer generator</th>
    <th>Relevant documents</th>
    <th>Non-relevant documents</th>
    <th colspan="2">CAB</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th>ΔPrecision</th>
    <th>ΔRecall</th>
  </tr>
  <tr>
    <td colspan="5"><b>NQ</b></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>LLM</td>
    <td>Human</td>
    <td>+10.6<sup>†</sup></td>
    <td>+9.6<sup>†</sup></td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td>+9.4<sup>†</sup></td>
    <td>+7.6<sup>†</sup></td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>LLM</td>
    <td>Human</td>
    <td>+11.8<sup>†</sup></td>
    <td>+8.0<sup>†</sup></td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td>+15.1<sup>†</sup></td>
    <td>+8.6<sup>†</sup></td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>LLM</td>
    <td>Human</td>
    <td>+6.2<sup>†</sup></td>
    <td>+7.6<sup>†</sup></td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td>+5.8<sup>†</sup></td>
    <td>+7.2<sup>†</sup></td>
  </tr>
  <tr>
    <td colspan="5"><b>MS MARCO</b></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>LLM</td>
    <td>Human</td>
    <td>+9.5<sup>†</sup></td>
    <td>+13.8<sup>†</sup></td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td>+8.0<sup>†</sup></td>
    <td>+12.4<sup>†</sup></td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>LLM</td>
    <td>Human</td>
    <td>+15.6<sup>†</sup></td>
    <td>+18.2<sup>†</sup></td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td>+15.1<sup>†</sup></td>
    <td>+16.4<sup>†</sup></td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>LLM</td>
    <td>Human</td>
    <td>+6.1<sup>†</sup></td>
    <td>+9.0<sup>†</sup></td>
  </tr>
  <tr>
    <td></td>
    <td>Human</td>
    <td>LLM</td>
    <td>+5.4<sup>†</sup></td>
    <td>+10.8<sup>†</sup></td>
  </tr>
</table>

Table 3: Attribution Bias (CAB) results. Values range from -100 (completely biased towards LLM authorship) to +100 (completely biased towards human authorship). † indicates statistically significant bias values.

Figure 4 shows an example of Mixed RAG mode for the setting where we have human-written documents, with different authorship labels for relevant and non-relevant documents. The CAB (Eq. 2) for Mixed RAG mode is reformulated as follows:

\[
\text{CAB}(Q) = \frac{\omega}{|Q|} \sum_{q \in Q} M^q_{\text{Informed}/\text{CF-Informed}} - \frac{M^q_{\text{CF-Informed}/\text{Informed}}}{}
\]

where \( X \) and \( Y \) in \( M^q_{X/Y} \) stand for the RAG mode for the set of relevant documents and the set of non-relevant documents, respectively. The results of attribution quality for Mixed-RAG modes are shown in Table 4.\footnote{See Tables 14 and 15 (Appendix) for the complete set of results.} We see that, similar to Table 1, across different settings, when the relevant documents are labeled with human-authorship and non-relevant ones are labeled with LLM-authorship, the attribution quality is higher than the other way around. Moreover, Table 5 illustrates the attribution bias for Mixed RAG modes. Similar to the results in Table 3, there is a bias towards human authorship in all three LLMs. This indicates the existence of authorship bias regardless of the origin of the input documents, i.e., the actual author of the input documents. Furthermore, similar to the results in Table 3, Mistral and Llama3 show higher rates of attribution bias than GPT-4. Additionally, we find that when we have the same authorship label on both relevant and non-relevant documents (rows with the same RAG mode for relevant and non-relevant documents in Tables 14 and 15 in the Appendix), we do not see consistent patterns as to how LLMs attribute the answers to the input documents. Finally, we note that in Section C of the Appendix, we show additional results using real-world names as authors which further indicates the presence of attribution bias and sensitivity in LLMs towards authorship information.

![Mixed RAG mode for the setting where we use original human-authored documents. In this example, we have “Informed” mode for relevant documents and “CF-Informed” for non-relevant documents.](page_1012_1012_420_180.png)

Figure 4: Mixed RAG mode for the setting where we use original human-authored documents. In this example, we have “Informed” mode for relevant documents and “CF-Informed” for non-relevant documents.

Attribution Confidence. Using Eq. 4, we compute the confidence of LLMs when they attribute their answer to an input document. Table 6 shows the attribution confidence of LLMs for relevant and non-relevant documents.\footnote{Table 10 in the Appendix shows the results on MS MARCO.} Across the majority of RAG modes over different origins for relevant and non-relevant documents, the confidence of all three LLMs for attributing to relevant documents is higher than for attributing to non-relevant ones. We can also see that authorship labels (i.e., using different RAG modes) do not affect this outcome. In other words, it is being relevant or not that makes the difference here. These results indicate a promising direction for improving attribution in LLMs: low confidence of LLMs in attributing to a specific document might be a useful signal for the relevance of that document to a given query.

Frequency of Attribution. In Table 1, across the majority of the settings, GPT-4 outperforms Mistral and Llama3 in terms of precision of results. In contrast, in terms of recall, it is Mistral and Llama3 which outperform GPT-4. To better explore this difference, we examine the average number of relevant citations and total citations for the three models. Figure 5 shows the average number of total citations\footnote{Tables 12 and 13 in the Appendix show both the average number of relevant citations and the total citations.} for each model. In comparison to Mistral and Llama3, GPT-4 tends to cite
<table>
  <tr>
    <th rowspan="2">Answer generator</th>
    <th colspan="2">Relevant documents</th>
    <th colspan="2">Non-relevant documents</th>
    <th colspan="2">Mixed RAG mode</th>
    <th colspan="3">Attribution quality</th>
    <th rowspan="2">Correctness</th>
  </tr>
  <tr>
    <th>Human</th>
    <th>LLM</th>
    <th>Human</th>
    <th>LLM</th>
    <th>Relevant</th>
    <th>Non-relevant</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>EM</th>
  </tr>
  <tr>
    <td>NQ</td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="3"></td>
    <td></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>Human</td>
    <td>Human</td>
    <td>CF-informed</td>
    <td>Informed</td>
    <td>44.8</td>
    <td>71.8</td>
    <td>0.772</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td>CF-informed</td>
    <td><b>52.3†</b></td>
    <td><b>77.2†</b></td>
    <td><b>0.780</b></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>CF-informed</td>
    <td>Informed</td>
    <td><b>48.7†</b></td>
    <td><b>74.6†</b></td>
    <td>0.718</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td>CF-informed</td>
    <td>42.9</td>
    <td>69.4</td>
    <td>0.742</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Human</td>
    <td>Human</td>
    <td>CF-informed</td>
    <td>Informed</td>
    <td>45.7</td>
    <td>69.6</td>
    <td>0.784</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td>CF-informed</td>
    <td><b>57.4†</b></td>
    <td><b>77.6†</b></td>
    <td><b>0.808</b></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>CF-informed</td>
    <td>Informed</td>
    <td><b>59.3†</b></td>
    <td><b>77.8†</b></td>
    <td><b>0.744</b></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td>CF-informed</td>
    <td>44.7</td>
    <td>68.4</td>
    <td>0.726</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Human</td>
    <td>Human</td>
    <td>CF-informed</td>
    <td>Informed</td>
    <td>65.8</td>
    <td>70.6</td>
    <td>0.794</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td>CF-informed</td>
    <td><b>69.1†</b></td>
    <td><b>74.0†</b></td>
    <td>0.784</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>CF-informed</td>
    <td>Informed</td>
    <td><b>66.1†</b></td>
    <td><b>71.2†</b></td>
    <td><b>0.730</b></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td>Informed</td>
    <td>CF-informed</td>
    <td>61.7</td>
    <td>66.8</td>
    <td>0.716</td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 4: Quality of attribution and answer correctness for Mixed RAG mode. The columns “Relevant Documents” and “Non-relevant Documents” refer to the actual authorship of input documents. † indicates statistically significant improvements over the other Mixed RAG mode in each combination of relevant and non-relevant documents.

<table>
  <tr>
    <th rowspan="2">Answer generator</th>
    <th colspan="2">Relevant documents</th>
    <th colspan="2">Non-relevant documents</th>
    <th colspan="2">CAB</th>
  </tr>
  <tr>
    <th>Human</th>
    <th>LLM</th>
    <th>Human</th>
    <th>LLM</th>
    <th>ΔPrecision</th>
    <th>ΔRecall</th>
  </tr>
  <tr>
    <td>NQ</td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="2"></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>Human</td>
    <td>Human</td>
    <td>+7.5†</td>
    <td>+5.4†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>+5.8†</td>
    <td>+5.2†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Human</td>
    <td>Human</td>
    <td>+11.7†</td>
    <td>+8.0†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>+14.6†</td>
    <td>+9.4†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Human</td>
    <td>Human</td>
    <td>+3.3†</td>
    <td>+3.4†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>+4.4†</td>
    <td>+4.4†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>MS MARCO</td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="2"></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>Human</td>
    <td>Human</td>
    <td>+8.6†</td>
    <td>+14.8†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>+8.7†</td>
    <td>+13.8†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Human</td>
    <td>Human</td>
    <td>+12.6†</td>
    <td>+10.4†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>+9.7†</td>
    <td>+9.8†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Human</td>
    <td>Human</td>
    <td>+7.4†</td>
    <td>+9.4†</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>+5.4†</td>
    <td>+5.2†</td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 5: Attribution Bias (CAB) results for Mixed RAG modes. Positive values indicate a bias towards human. † indicates statistically significant bias values. Values range from -100 (completely biased towards LLM authorship) to +100 (completely biased towards human authorship).

fewer documents as supporting documents for its generated answer. This is in line with the previous results, where Mistral and Llama3 score higher on recall.

Answer Correctness. Table 1 and 4 show that when the relevant documents are labeled with human-authorship and non-relevant ones are labeled with LLM-authorship, the answer correctness is higher than in the reverse case, across the majority of settings. Although this improvement is not significant and consistent across all settings, similar to attribution quality, it could indicate a bias towards human authorship. Nevertheless, we note that the automatic evaluation of answer correctness without human evaluation is not an ideal method (Bojic et al., 2023; Chiang and Lee, 2023; Zhang et al., 2019). We leave this aspect for future work as the focus of this paper is on the performance of LLMs in how frequently they tend to cite and attribute their output on documents with either human or LLM authorship.

<table>
  <tr>
    <th rowspan="2">Answer generator</th>
    <th colspan="2">Rel. Docs.</th>
    <th colspan="2">Non-rel. Docs.</th>
    <th colspan="2">RAG mode</th>
    <th colspan="2">Confidence (AC)</th>
  </tr>
  <tr>
    <th>Human</th>
    <th>LLM</th>
    <th>Human</th>
    <th>LLM</th>
    <th>Relevant</th>
    <th>Non-rel.</th>
  </tr>
  <tr>
    <td>NQ</td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="2"></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>0.9647</td>
    <td>0.9284</td>
    <td>0.9656</td>
    <td>0.9257</td>
  </tr>
  <tr>
    <td></td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>0.9737</td>
    <td>0.9401</td>
    <td>0.9678</td>
    <td>0.9355</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>0.9060</td>
    <td>0.8145</td>
    <td>0.8960</td>
    <td>0.8260</td>
  </tr>
  <tr>
    <td></td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>0.9235</td>
    <td>0.8282</td>
    <td>0.9088</td>
    <td>0.7985</td>
  </tr>
  <tr>
    <td></td>
    <td>Human LLM</td>
    <td>Human LLM</td>
    <td>Human LLM</td>
    <td>Human LLM</td>
    <td>0.9163</td>
    <td>0.8160</td>
    <td>0.9163</td>
    <td>0.8160</td>
  </tr>
  <tr>
    <td></td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>0.8908</td>
    <td>0.8238</td>
    <td>0.8908</td>
    <td>0.8238</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>Vanilla†</td>
    <td>0.9807</td>
    <td>0.9042</td>
    <td>0.9796</td>
    <td>0.9130</td>
  </tr>
  <tr>
    <td></td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>0.9834</td>
    <td>0.9094</td>
    <td>0.9776</td>
    <td>0.9346</td>
  </tr>
  <tr>
    <td></td>
    <td>Human LLM</td>
    <td>Human LLM</td>
    <td>Human LLM</td>
    <td>Human LLM</td>
    <td>0.9819</td>
    <td>0.9238</td>
    <td>0.9778</td>
    <td>0.9205</td>
  </tr>
  <tr>
    <td></td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>CF-informed†</td>
    <td>0.9776</td>
    <td>0.9346</td>
    <td>0.9776</td>
    <td>0.9346</td>
  </tr>
</table>

Table 6: The attribution confidence (AC) of LLMs in relevant and non-relevant documents for NQ dataset. † indicates a statistically significant difference between the AC values of relevant and non-relevant documents.
Figure 5: The average total number of cited documents by Mistral, Llama3, and GPT-4 across various RAG settings on NQ and MS MARCO benchmarks.

6 Conclusion and Future Work

In this paper, we have defined and studied attribution sensitivity and bias with respect to authorship information of source documents in RAG with LLMs. We have proposed a systematic evaluation framework based on counterfactual evaluation. Our results indicate that by adding authorship information to source documents, the attribution quality of LLMs may significantly change by 3% to 18%. Moreover, our results on three LLMs indicate that they have an attribution bias towards explicit human authorship, in contrast to previous studies that show that LLM-generated content may consistently be preferred over human-authored content by LLMs.

As to broader implications of our work, while understanding the roots and causes of the observed sensitivity and bias requires access to the implementation, training, and fine-tuning of these models (which is beyond the scope of this paper), our findings highlight a critical aspect of how LLMs operate. Our results show the brittleness of LLMs for attributing their answers. Such brittleness can be used for both constructive and harmful purposes, e.g., one can bias the output of an LLM towards their own content by incorporating authorship information in their documents.

While we only focused on human versus LLM authorship as metadata in this work, in future work our systematic evaluation method can be used to investigate the sensitivity and bias towards other metadata of source documents (e.g., gender and race of authors). Furthermore, our evaluation methodology can be incorporated in trustworthiness benchmarks used for the evaluation of LLMs such as DecodingTrust (Wang et al., 2024). Finally, our proposed methodology for the evaluation of sensitivity and bias is adaptable to other metrics for measuring the quality of attribution, i.e., metrics other than precision and recall can be used as \( M \) in Eq. 1, 2, and 5.

Limitations

In this work we do not propose or explore solutions for mitigating the observed bias as our focus is on uncovering the brittleness of LLMs when being used for retrieval-augmented generation. Besides, we have evaluated three LLMs in our experimental setup, two of which are open-source and the other closed-source. Consequently, investigating the same attribution sensitivity and bias on other LLMs is of interest for future studies. Furthermore, in our experiments, we used queries that have only one relevant document containing the ground-truth answer in their top-\( k \) retrieved list of documents. We do this to ensure the traceability of the correct attribution. However, we acknowledge the limitation of this evaluation method in capturing the fine-grained attribution support of input documents. Finally, it is important to mention that our current research is limited to datasets and prompts in English. Therefore, we point out the need to expand our evaluation and analysis to include datasets in other languages.

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Appendix

A  Synthetic Document Generation

Prompt. Figure 6 shows the prompt used for re-writing passages for the two benchmarks of NQ and MS MARCO.

<table>
  <tr>
    <th>Instruction:</th>
    <td>Please write a high-quality paraphrase for the given passage.<br>Keep the length approximately the same. Do not add any new information.</td>
  </tr>
  <tr>
    <th>Passage:</th>
    <td>{input passage}</td>
  </tr>
</table>

Figure 6: Prompt used for generating synthetic documents.

Data Quality. In order to ensure the quality of synthetic passages, we conduct the following annotation steps using two expert annotators: (i) for each of the queries in our two benchmarks, we provide the annotators the quadruple of (query \( q \), original relevant passage \( p_r \), synthetic relevant \( \hat{p}_r \) passage, answer \( a \)). We then ask the annotators to determine whether the synthetic passage \( \hat{p}_r \) is still relevant to the query and includes the answer \( a \) to the query \( q \). (ii) In order to ensure that non-relevant passages are still non-relevant after being rewritten by an LLM, for each query, we provide the annotators the quadruple of (query \( q \), original non-relevant passage \( p_n \), synthetic non-relevant \( \hat{p}_n \) passage, answer \( a \)). We then ask the annotators to determine whether the synthetic non-relevant passage \( \hat{p}_n \) is still non-relevant to the query and does not include the answer \( a \) to the query \( q \). Due to the large number of non-relevant passages for each query, we randomly select 10% of queries, i.e., 50 queries out of 500 queries. Our annotation results indicate a perfect performance in keeping the relevance and non-relevance status of synthetic documents with respect to their corresponding queries.

B  Authorship Informed Answer/Attribution Generation Prompt

Figure 7 shows the prompt used for authorship-informed answer/Attribution generation with all three LLMs, Mistral, Llama3, and GPT-4.

C  Extended Set of Authorship Labels

So far, we have used [LLM] and [Human] as the authorship labels for the source documents. In this section, we discuss and provide results using an extended set of authorship labels. Specifically, we use [AI] as the label for denoting the synthetic (LLM) authorship. For human authorship, on the other hand, we analyze the use of real-world names to indicate the authors of documents. This reflects a more realistic setting of authorship indication on documents. To create this set of names, we prompt GPT-4 to randomly generate a pool of 100 (first name, last name) pairs. Figure 8 shows the prompt we use for this task. We then randomly sample one instance of (first name, last name) from this pool when labeling human authorship for each document in the list of top-\( k \) source documents of a query (instead of using [Human] as the authorship label).

Table 7 shows the attribution sensitivity results using the extended set of authorship labels. As we can see, all three LLMs Mistral, Llama, and GPT-4 are sensitive to adding the authorship information similar to the attribution sensitivity results with [Human] and [LLM] authorship labels (Table 2). In addition, we see that GPT-4 shows a lower level of sensitivity than Mistral and Llama. Moreover, Table 8 shows the attribution bias results using the extended set of authorship labels. Similar to the attribution bias results with [Human] and [LLM] authorship labels (Table 3), all three LLMs Mistral, Llama, and GPT-4 show an attribution bias towards human authorship, i.e., they are biased towards documents that are labeled with human author names. This indicates the robustness of our analysis against changes in labels.

D  Effect of the Number of Source Documents

To study the effect of the number of source documents, i.e., the length of the retrieved ranked list of documents given to the answer generator LLM, we evaluate the attribution sensitivity and bias using varying number of source documents. To this end, we use 4 ranking cut-offs for the ranked list of source documents (\( k \)): 2, 5, 8, 10. To ensure the existence of the relevant document as the input, we randomly put the relevant document in the top-\( k \) (\( k \in \{2, 5, 8, 10\} \)). For this set of experiments we use human-generated versions of both relevant and non-relevant documents. Furthermore, we use the
Instruction: Write a concise answer for the given question (query) based on the provided search result documents, and cite them properly using [0][1][2] etc.

Please take these strict considerations into account, during answer generation:
1. Documents are retrieved by a search engine. As such, not all the documents are relevant to the query. Only use and cite the relevant documents that contain the answer.
2. Do not analyze irrelevant documents.
3. Some documents are authored by humans, while others are generated by a Large Language Model (LLM). We denote this distinction for each document.

Search Results:

Document [0]({text of Document [0]}) (written by [Author])
Document [1]({text of Document [1]}) (written by [Author])
...
Document [9]({text of Document [9]}) (written by [Author])

Question: {query}.

Figure 7: Prompt used for authorship-informed answer/citation generation with LLM. [Author] is filled with one instance from either {Human, Person, Individual} or {AI, LLM, Machine}, depending on the source of the document and the RAG setting.

<table>
  <tr>
    <th>Answer generator</th>
    <th>Relevant documents</th>
    <th>Non-relevant documents</th>
    <th>CAS</th>
    <th>ΔPrecision</th>
    <th>ΔRecall</th>
  </tr>
  <tr>
    <th colspan="6">NQ</th>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>Human</td>
    <td>Human</td>
    <td>27.5</td>
    <td>26.8</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>13.3</td>
    <td>14.4</td>
    <td></td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Human</td>
    <td>Human</td>
    <td>15.0</td>
    <td>12.4</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>15.6</td>
    <td>14.4</td>
    <td></td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Human</td>
    <td>Human</td>
    <td>7.4</td>
    <td>7.0</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>7.5</td>
    <td>6.8</td>
    <td></td>
  </tr>
  <tr>
    <th colspan="6">MS MARCO</th>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>Human</td>
    <td>Human</td>
    <td>11.0</td>
    <td>17.2</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>9.4</td>
    <td>14.0</td>
    <td></td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Human</td>
    <td>Human</td>
    <td>13.9</td>
    <td>18.6</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>13.3</td>
    <td>17.4</td>
    <td></td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Human</td>
    <td>Human</td>
    <td>10.8</td>
    <td>13.2</td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td>LLM</td>
    <td>LLM</td>
    <td>9.2</td>
    <td>10.8</td>
    <td></td>
  </tr>
</table>

Table 7: Attribution sensitivity (CAS) results for the RAG setting with extended set of authorship labels. Values range from 0 (minimum sensitivity) to 100 (maximum sensitivity). † indicates statistically significant values.

extended set of labels (i.e., authors with first names and last names). Figure 9 shows the results of attribution sensitivity (CAS) and attribution bias (CAB) for the three LLMs on the NQ and MS MARCO benchmarks. All three LLMs show both attribution sensitivity and bias across varying number of source documents (\( k \)). Moreover, we can see that no conclusion can be inferred for the effect of \( k \) on the *degree* of sensitivity and bias in these LLMs.

E Effect of the Retriever

In our experiments, we have used two different retrievers for NQ and MS MARCO benchmarks: the list of source documents for NQ are retrieved using BM25, and for MS MARCO we used the ranked list of documents in the benchmark which are retrieved using the Bing search engine (see Section 4).

In order to better disentangle the effect of retrievers on the attribution sensitivity and bias, we use two more commonly-used retrievers for our experiments:
• uniCOIL (Lin and Ma, 2021): a retrieval model built upon COIL (Gao et al., 2021), which works based on sparse learned representation of documents.
• TCT-ColBERT (Lin et al., 2020): a dense retrieval model trained with knowledge distillation using ColBERT (Khattab and Zaharia, 2020) as the teacher model.

For this set of experiments we use the extended set of labels. Besides, we use original (human-generated) documents. Table 9 shows the results of attribution sensitivity and bias on NQ benchmark using uniCOIL and TCT-ColBERT. As the results on uniCOIL and TCT-ColBERT show, the three LLMs {Mistral, Llama, GPT-4} have attribution sensitivity and bias with respect to the authorship informa-
Figure 9: Attribution Sensitivity and Bias in Mistral, Llama3, and GPT-4, across varying number of retrieved documents (top-\( k \) values) on NQ (top) and MS MARCO benchmarks (bottom).

<table>
  <tr>
    <th rowspan="2">Answer generator</th>
    <th colspan="2">Relevant documents</th>
    <th colspan="2">Non-relevant documents</th>
    <th colspan="2">CAB</th>
  </tr>
  <tr>
    <th>Human</th>
    <th>LLM</th>
    <th>Human</th>
    <th>LLM</th>
    <th>\( \Delta \)Precision</th>
    <th>\( \Delta \)Recall</th>
  </tr>
  <tr>
    <td colspan="7"><b>NQ</b></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>Human</td>
    <td>LLM</td>
    <td>Human</td>
    <td>LLM</td>
    <td>+13.1</td>
    <td>+3.6</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td>+4.4</td>
    <td>+2.4</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Human</td>
    <td>LLM</td>
    <td>Human</td>
    <td>LLM</td>
    <td>+6.9</td>
    <td>+1.6</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td>+9.8</td>
    <td>+8.4</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Human</td>
    <td>LLM</td>
    <td>Human</td>
    <td>LLM</td>
    <td>+2.8</td>
    <td>+3.0</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td>+3.9</td>
    <td>+2.4</td>
  </tr>
  <tr>
    <td colspan="7"><b>MS MARCO</b></td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>Human</td>
    <td>LLM</td>
    <td>Human</td>
    <td>LLM</td>
    <td>+6.6</td>
    <td>+6.0</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td>+4.3</td>
    <td>+3.6</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Human</td>
    <td>LLM</td>
    <td>Human</td>
    <td>LLM</td>
    <td>+9.8</td>
    <td>+12.2</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td>+8.0</td>
    <td>+8.2</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Human</td>
    <td>LLM</td>
    <td>Human</td>
    <td>LLM</td>
    <td>+5.1</td>
    <td>+4.0</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
    <td>+6.9</td>
    <td>+6.8</td>
  </tr>
</table>

Table 8: Attribution Bias (CAB) results for the RAG setting with extended set of authorship labels. Positive values indicate a bias towards human. \( \dagger \) indicates statistically significant bias values.

tion regardless of the retriever that is being used to retrieve their top-\( k \) source documents. Moreover, we see that the sensitivity and bias values across all models are lower for the answer generation upon the source documents from uniCOIL than when TCT-ColBERT is being used as the retriever. This finding is specifically important as it shows that the quality of retrieved source documents can affect the quality of attribution by LLMs.

F Attribution Quality Results

Table 11 shows the results of attribution by Mistral, Llama3, and GPT-4, under different settings for the MS MARCO benchmark.

<table>
  <tr>
    <th>Answer generator</th>
    <th>Retriever</th>
    <th>\( \Delta \)Precision</th>
    <th>\( \Delta \)Recall</th>
  </tr>
  <tr>
    <th colspan="4"><b>CAS</b></th>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>uniCOIL</td>
    <td>16.8</td>
    <td>15.0</td>
  </tr>
  <tr>
    <td></td>
    <td>TCT-ColBERT</td>
    <td>18.2</td>
    <td>17.0</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>uniCOIL</td>
    <td>14.5</td>
    <td>13.0</td>
  </tr>
  <tr>
    <td></td>
    <td>TCT-ColBERT</td>
    <td>18.0</td>
    <td>13.6</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>uniCOIL</td>
    <td>6.6</td>
    <td>6.6</td>
  </tr>
  <tr>
    <td></td>
    <td>TCT-ColBERT</td>
    <td>8.7</td>
    <td>8.2</td>
  </tr>
  <tr>
    <th colspan="4"><b>CAB</b></th>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>uniCOIL</td>
    <td>+6.6</td>
    <td>+3.4</td>
  </tr>
  <tr>
    <td></td>
    <td>TCT-ColBERT</td>
    <td>+7.9</td>
    <td>+5.8</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>uniCOIL</td>
    <td>+8.2</td>
    <td>+4.6</td>
  </tr>
  <tr>
    <td></td>
    <td>TCT-ColBERT</td>
    <td>+12.7</td>
    <td>+8.8</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>uniCOIL</td>
    <td>+3.9</td>
    <td>+3.8</td>
  </tr>
  <tr>
    <td></td>
    <td>TCT-ColBERT</td>
    <td>+5.2</td>
    <td>+4.6</td>
  </tr>
</table>

Table 9: Attribution sensitivity (CAS) and Bias (CAB) results across different retrievers. Positive values of CAB indicate a bias towards human authorship.

G Confidence Results

Table 10 shows the confidence results of Mistral, Llama3, and GPT-4 on MS MARCO benchmark.

H Average Number of Cited Documents

Tables 12 and 13 show Relevant and Total number of cited documents for each model on both benchmarks.

I Mixed RAG Mode Results

Tables 14 and 15 show the results for Mixed RAG mode as described in Section 5.
<table>
  <tr>
    <th rowspan="2">Answer generator documents</th>
    <th colspan="2">Non-relevant RAG mode</th>
    <th colspan="2">Confidence</th>
  </tr>
  <tr>
    <th>Relevant documents</th>
    <th>Non-relevant documents</th>
    <th>Relevant</th>
    <th>Non-relevant</th>
  </tr>
  <tr>
    <td colspan="5"><b>MS MARCO</b></td>
  </tr>
  <tr>
    <td>LLM Human</td>
    <td>Vanilla</td>
    <td></td>
    <td>0.9620</td>
    <td>0.9527</td>
  </tr>
  <tr>
    <td>LLM Human</td>
    <td>Informed†</td>
    <td></td>
    <td>0.9511</td>
    <td>0.9470</td>
  </tr>
  <tr>
    <td>Mistral</td>
    <td>CF-informed†</td>
    <td></td>
    <td>0.9746</td>
    <td>0.9456</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>Vanilla†</td>
    <td></td>
    <td>0.9616</td>
    <td>0.9446</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>Informed†</td>
    <td></td>
    <td>0.9650</td>
    <td>0.9521</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>CF-Informed†</td>
    <td></td>
    <td>0.9484</td>
    <td>0.9516</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Vanilla†</td>
    <td></td>
    <td>0.9267</td>
    <td>0.8878</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>Informed†</td>
    <td></td>
    <td>0.9104</td>
    <td>0.8918</td>
  </tr>
  <tr>
    <td>Llama3</td>
    <td>CF-informed†</td>
    <td></td>
    <td>0.9332</td>
    <td>0.8622</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>Vanilla</td>
    <td></td>
    <td>0.8888</td>
    <td>0.8941</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>Informed†</td>
    <td></td>
    <td>0.9441</td>
    <td>0.8736</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>CF-Informed†</td>
    <td></td>
    <td>0.906</td>
    <td>0.889</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Vanilla†</td>
    <td></td>
    <td>0.9749</td>
    <td>0.9038</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>Informed†</td>
    <td></td>
    <td>0.9714</td>
    <td>0.9165</td>
  </tr>
  <tr>
    <td>GPT-4</td>
    <td>CF-informed†</td>
    <td></td>
    <td>0.9757</td>
    <td>0.9173</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>Vanilla</td>
    <td></td>
    <td>0.9506</td>
    <td>0.9395</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>Informed†</td>
    <td></td>
    <td>0.9657</td>
    <td>0.9171</td>
  </tr>
  <tr>
    <td>Human LLM</td>
    <td>CF-informed†</td>
    <td></td>
    <td>0.9556</td>
    <td>0.936</td>
  </tr>
</table>

Table 10: The attribution confidence (AC) of LLMs in attributing answers to relevant and non-relevant documents for the MS MARCO QA benchmark. † stands for statistically significant difference between the AC values of relevant and non-relevant documents.

J Examples

Table 16 shows the results of Authorship-Informed retrieval-augmented generation with Mistral, Llama3, and GPT-4 for the query “where was the new pete’s dragon filmed.” Both Llama3 and GPT-4 generate the correct answer and accurately attribute their answers to the ground-truth document [5]. However, despite providing the correct answer and the correct attribution, Mistral attributes its generated answer to an additional source document, i.e., document [2]. Table 17 shows the results of three RAG modes with GPT-4 for the query “who won the men’s single title of australia open on 1 february 2015.” This result corresponds to the combination of “human-written” relevant documents and LLM-written non-relevant ones. As we see, in all RAG models, this model makes a mistake in attributing to document [2], which does not contain the answer. However, in the Authorship Informed RAG mode (where we inform the LLM that document [8] has human authorship), in addition to document [2], the model also refers to the ground-truth document [8].
<table>
  <tr>
    <th>Answer generator</th>
    <th>Relevant documents</th>
    <th>Non-relevant documents</th>
    <th>RAG mode</th>
    <th colspan="2">Attribution quality</th>
    <th>Correctness</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th>Precision</th>
    <th>Recall</th>
    <th>EM</th>
  </tr>
  <tr>
    <td colspan="7"><b>MS MARCO</b></td>
  </tr>
  <tr>
    <td rowspan="3">Mistral</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>23.1</td>
    <td>76.4</td>
    <td><b>0.316</b></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>22.2</td>
    <td>65.8</td>
    <td>0.306</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td><b>31.7†</b></td>
    <td><b>79.6†</b></td>
    <td>0.312</td>
  </tr>
  <tr>
    <td rowspan="3">Llama3</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>22.8</td>
    <td>72.8</td>
    <td>0.342</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td><b>28.0†</b></td>
    <td><b>72.6†</b></td>
    <td><b>0.384</b></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>20.1</td>
    <td>60.2</td>
    <td>0.334</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>29.3</td>
    <td>66.0</td>
    <td>0.334</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>22.8</td>
    <td>58.0</td>
    <td>0.330</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td><b>38.4†</b></td>
    <td><b>76.2†</b></td>
    <td><b>0.352</b></td>
  </tr>
  <tr>
    <td rowspan="3">Llama3</td>
    <td rowspan="3">Human</td>
    <td rowspan="3">LLM</td>
    <td>Vanilla</td>
    <td>30.5</td>
    <td>64.8</td>
    <td>0.416</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td><b>42.6†</b></td>
    <td><b>78.0†</b></td>
    <td><b>0.474</b></td>
  </tr>
  <tr>
    <td>CF-Informed</td>
    <td>27.5</td>
    <td>61.6</td>
    <td>0.422</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>38.1</td>
    <td>55.6</td>
    <td>0.312</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>35.4</td>
    <td>52.0</td>
    <td>0.310</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td><b>41.5†</b></td>
    <td><b>61.0†</b></td>
    <td><b>0.324</b></td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">Human</td>
    <td rowspan="3">LLM</td>
    <td>Vanilla</td>
    <td>37.0</td>
    <td>53.0</td>
    <td>0.380</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td><b>38.5</b></td>
    <td><b>59.2</b></td>
    <td>0.378</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>33.1</td>
    <td>48.4</td>
    <td>0.362</td>
  </tr>
</table>

Table 11: Quality of attribution and answer correctness for MS MARCO. The columns “Relevant Documents” and “Non-relevant Documents” refer to the actual authorship of input documents. Informed refers to the authorship-informed RAG and CF-informed refers to counterfactual-authorship informed RAG (Section 3.1). † indicates statistically significant improvements over the two other RAG Modes in each combination of “Relevant” and “Non-relevant” documents.

<table>
  <tr>
    <th>Answer generator</th>
    <th>Relevant documents</th>
    <th>Non-relevant documents</th>
    <th>RAG mode</th>
    <th>#Cited docs.</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th>Relevant Total</th>
  </tr>
  <tr>
    <td colspan="5"><b>NQ</b></td>
  </tr>
  <tr>
    <td rowspan="3">Mistral</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>0.766 2.190</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.682 2.280</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.778 2.050</td>
  </tr>
  <tr>
    <td rowspan="3">Llama3</td>
    <td rowspan="3">Human</td>
    <td rowspan="3">LLM</td>
    <td>Vanilla</td>
    <td>0.784 2.114</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.778 2.080</td>
  </tr>
  <tr>
    <td>CF-Informed</td>
    <td>0.702 2.202</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>0.692 1.718</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.696 1.906</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.776 1.682</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">Human</td>
    <td rowspan="3">LLM</td>
    <td>Vanilla</td>
    <td>0.710 1.656</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.778 1.624</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.692 1.952</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>0.688 1.166</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.646 1.152</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.722 1.162</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">Human</td>
    <td rowspan="3">LLM</td>
    <td>Vanilla</td>
    <td>0.688 1.122</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.722 1.168</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.650 1.138</td>
  </tr>
</table>

Table 12: The average number of cited relevant documents and in total (relevant plus non-relevant documents).

<table>
  <tr>
    <th>Answer generator</th>
    <th>Relevant documents</th>
    <th>Non-relevant documents</th>
    <th>RAG mode</th>
    <th>#Cited docs.</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th>Relevant Total</th>
  </tr>
  <tr>
    <td colspan="5"><b>MS MARCO</b></td>
  </tr>
  <tr>
    <td rowspan="3">Mistral</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>0.764 4.266</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.658 3.960</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.796 3.586</td>
  </tr>
  <tr>
    <td rowspan="3">Llama3</td>
    <td rowspan="3">Human</td>
    <td rowspan="3">LLM</td>
    <td>Vanilla</td>
    <td>0.728 4.084</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.726 3.560</td>
  </tr>
  <tr>
    <td>CF-Informed</td>
    <td>0.602 3.826</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>0.66 2.91</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.58 3.274</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.762 2.77</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">Human</td>
    <td rowspan="3">LLM</td>
    <td>Vanilla</td>
    <td>0.648 2.838</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.78 2.614</td>
  </tr>
  <tr>
    <td>CF-Informed</td>
    <td>0.616 3.038</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">LLM</td>
    <td rowspan="3">Human</td>
    <td>Vanilla</td>
    <td>0.556 1.724</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.52 1.774</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.61 1.744</td>
  </tr>
  <tr>
    <td rowspan="3">GPT-4</td>
    <td rowspan="3">Human</td>
    <td rowspan="3">LLM</td>
    <td>Vanilla</td>
    <td>0.53 1.772</td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>0.592 1.848</td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>0.484 1.776</td>
  </tr>
</table>

Table 13: The average number of cited relevant documents and in total (relevant plus non-relevant documents).
<table>
  <tr>
    <th rowspan="2">Answer generator</th>
    <th rowspan="2">Relevant documents</th>
    <th rowspan="2">Non-relevant documents</th>
    <th colspan="2">Mixed RAG mode</th>
    <th colspan="3">Attribution quality</th>
    <th rowspan="2">Correctness</th>
  </tr>
  <tr>
    <th>Relevant</th>
    <th>Non-relevant</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>EM</th>
  </tr>
  <tr>
    <td colspan="9">NQ</td>
  </tr>
  <tr>
    <td rowspan="4">Human</td>
    <td rowspan="4">Human</td>
    <td>Vanilla</td>
    <td>Vanilla</td>
    <td>50.4</td>
    <td><b>77.6</b></td>
    <td><b>0.784</b></td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>Informed</td>
    <td>45.5</td>
    <td>74.6</td>
    <td>0.772</td>
    <td></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>Informed</td>
    <td>44.8</td>
    <td>71.8</td>
    <td>0.772</td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>CF-informed</td>
    <td><b>52.3</b></td>
    <td>77.2</td>
    <td>0.780</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="4">Mistral</td>
    <td rowspan="4">LLM</td>
    <td>Vanilla</td>
    <td>Vanilla</td>
    <td>47.0</td>
    <td><b>76.8</b></td>
    <td>0.724</td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>Informed</td>
    <td>48.4</td>
    <td>74.6</td>
    <td>0.726</td>
    <td></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>Informed</td>
    <td><b>48.7</b></td>
    <td>74.6</td>
    <td>0.718</td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>CF-informed</td>
    <td>42.9</td>
    <td>69.4</td>
    <td><b>0.742</b></td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="4">Human</td>
    <td rowspan="4">Human</td>
    <td>Vanilla</td>
    <td>Vanilla</td>
    <td>50.4</td>
    <td>72.0</td>
    <td>0.798</td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>Informed</td>
    <td>46.6</td>
    <td>71.0</td>
    <td>0.796</td>
    <td></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>Informed</td>
    <td>45.7</td>
    <td>69.6</td>
    <td>0.784</td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>CF-informed</td>
    <td><b>57.4</b></td>
    <td><b>77.6</b></td>
    <td><b>0.808</b></td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="4">Llama3</td>
    <td rowspan="4">LLM</td>
    <td>Vanilla</td>
    <td>Vanilla</td>
    <td>53.1</td>
    <td>71.4</td>
    <td>0.742</td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>Informed</td>
    <td>50.4</td>
    <td>68.8</td>
    <td>0.732</td>
    <td></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>Informed</td>
    <td><b>59.3</b></td>
    <td><b>77.8</b></td>
    <td><b>0.744</b></td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>CF-informed</td>
    <td>44.7</td>
    <td>68.4</td>
    <td>0.726</td>
    <td></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>CF-informed</td>
    <td>50.8</td>
    <td>75.8</td>
    <td>0.732</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="4">Human</td>
    <td rowspan="4">Human</td>
    <td>Vanilla</td>
    <td>Vanilla</td>
    <td>65.9</td>
    <td>71.2</td>
    <td>0.778</td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>Informed</td>
    <td>68.1</td>
    <td>73.2</td>
    <td>0.786</td>
    <td></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>Informed</td>
    <td>65.8</td>
    <td>70.6</td>
    <td><b>0.794</b></td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>CF-informed</td>
    <td><b>69.1</b></td>
    <td><b>74.0</b></td>
    <td>0.784</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="4">GPT-4</td>
    <td rowspan="4">LLM</td>
    <td>Vanilla</td>
    <td>Vanilla</td>
    <td>65.9</td>
    <td>70.4</td>
    <td>0.718</td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>Informed</td>
    <td>65.2</td>
    <td>69.8</td>
    <td>0.726</td>
    <td></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>Informed</td>
    <td><b>66.1</b></td>
    <td><b>71.2</b></td>
    <td><b>0.730</b></td>
    <td></td>
  </tr>
  <tr>
    <td>Informed</td>
    <td>CF-informed</td>
    <td>61.7</td>
    <td>66.8</td>
    <td>0.716</td>
    <td></td>
  </tr>
  <tr>
    <td>CF-informed</td>
    <td>CF-informed</td>
    <td>63.8</td>
    <td>68.8</td>
    <td>0.724</td>
    <td></td>
  </tr>
</table>

Table 14: Quality of attribution and answer correctness with Mixed RAG modes for NQ benchmark. The columns “Relevant Documents” and “Non-relevant Documents” refer to the actual authorship of input documents.
<table>
  <tr>
    <th rowspan="2">Answer generator</th>
    <th rowspan="2">Relevant documents</th>
    <th rowspan="2">Non-relevant documents</th>
    <th colspan="2">Mixed RAG mode</th>
    <th colspan="3">Attribution quality</th>
    <th rowspan="2">Correctness</th>
  </tr>
  <tr>
    <th>Relevant</th>
    <th>Non-relevant</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>EM</th>
  </tr>
  <tr>
    <td rowspan="5">MS MARCO QA</td>
    <td colspan="8">Vanilla Vanilla 22.7 75.6 0.370</td>
  </tr>
  <tr>
    <td colspan="8">Informed Informed 22.7 71.6 0.368</td>
  </tr>
  <tr>
    <td colspan="8">Human Human CF-informed Informed 19.8 62.4 0.370</td>
  </tr>
  <tr>
    <td colspan="8">Informed CF-informed <b>28.4</b> <b>77.2</b> <b>0.389</b></td>
  </tr>
  <tr>
    <td colspan="8">CF-informed CF-informed 24.4 71.6 0.380</td>
  </tr>
  <tr>
    <td rowspan="5">Mistral</td>
    <td colspan="8">Vanilla Vanilla 24.0 73.6 0.298</td>
  </tr>
  <tr>
    <td colspan="8">Informed Informed 23.6 61.8 0.298</td>
  </tr>
  <tr>
    <td colspan="8">LLM LLM CF-informed Informed <b>28.9</b> <b>75.6</b> 0.296</td>
  </tr>
  <tr>
    <td colspan="8">Informed CF-informed 20.2 61.8 0.278</td>
  </tr>
  <tr>
    <td colspan="8">CF-informed CF-informed 23.3 70.8 0.276</td>
  </tr>
  <tr>
    <td rowspan="5">Llama3</td>
    <td colspan="8">Vanilla Vanilla 30.4 70.0 0.436</td>
  </tr>
  <tr>
    <td colspan="8">Informed Informed 29.9 74.4 0.430</td>
  </tr>
  <tr>
    <td colspan="8">Human Human CF-informed Informed 24.9 70.0 0.432</td>
  </tr>
  <tr>
    <td colspan="8">Informed CF-informed <b>37.5</b> <b>80.4</b> <b>0.476</b></td>
  </tr>
  <tr>
    <td colspan="8">CF-informed CF-informed 28.8 66.8 0.424</td>
  </tr>
  <tr>
    <td rowspan="5">GPT-4</td>
    <td colspan="8">Vanilla Vanilla 30.1 65.2 0.326</td>
  </tr>
  <tr>
    <td colspan="8">Informed Informed 31.5 65.6 0.330</td>
  </tr>
  <tr>
    <td colspan="8">LLM LLM CF-informed Informed <b>35.4</b> <b>75.0</b> 0.344</td>
  </tr>
  <tr>
    <td colspan="8">Informed CF-informed 25.7 65.2 0.338</td>
  </tr>
  <tr>
    <td colspan="8">CF-informed CF-informed 30.0 69.2 0.414</td>
  </tr>
  <tr>
    <td colspan="8">Vanilla Vanilla 35.9 52.2 0.382</td>
  </tr>
  <tr>
    <td colspan="8">Informed Informed 38.1 57.0 0.392</td>
  </tr>
  <tr>
    <td colspan="8">Human Human CF-informed Informed 35.2 52.0 0.370</td>
  </tr>
  <tr>
    <td colspan="8">Informed CF-informed <b>42.5</b> <b>61.4</b> <b>0.394</b></td>
  </tr>
  <tr>
    <td colspan="8">CF-informed CF-informed 36.8 55.8 0.382</td>
  </tr>
  <tr>
    <td colspan="8">Vanilla Vanilla 37.8 54.2 0.304</td>
  </tr>
  <tr>
    <td colspan="8">Informed Informed 36.3 53.0 0.296</td>
  </tr>
  <tr>
    <td colspan="8">LLM LLM CF-informed Informed <b>40.5</b> <b>58.4</b> 0.298</td>
  </tr>
  <tr>
    <td colspan="8">Informed CF-informed 35.2 53.2 0.294</td>
  </tr>
  <tr>
    <td colspan="8">CF-informed CF-informed 37.1 55.4 0.294</td>
  </tr>
</table>

Table 15: Quality of attribution and answer correctness with Mixed RAG modes for the MS MARCO benchmark. The columns “Relevant Documents” and “Non-relevant Documents” refer to the actual authorship of input documents.
Query
where was the new pete’s dragon filmed
Ground Truth Answer:
New Zealand
Groundtruth Document: [5]
Pete’s Dragon (2016 film). Principal photography commenced in January 2015 in New Zealand, with Lowery directing. Rehearsals began early January 2015, while news reported the beginning of the shooting on January 26, 2015, set to last through April. Live-action filming locations included Bay of Plenty, Taupo and Wellington, while CGI was done at Stone Street Studios. On February 10, 2015, a press release confirmed that principal photography had commenced. Shooting took place in and around Wellington and Rotorua, before transferring to Tapanui, where the old Blue Mountain Lumber mill was used as the mill in the film, and the main street became Millhaven for two weeks. Production concluded on April 30, 2015.

Mistral Output
"The new Pete’s Dragon (2016) was filmed in various locations in New Zealand, including the Bay of Plenty, Taupo, and Wellington [2] [5]. The production also used the old Blue Mountain Lumber mill in Tapanui as the film's mill and transformed the main street into Millhaven for two weeks [5]."

Llama3 Output
According to Document [5], the live-action scenes of the 2016 film "Pete’s Dragon" were shot in various locations in New Zealand, including the Bay of Plenty, Taupo, Wellington, and Tapanui.

GPT-4 Output
The new Pete’s Dragon (2016) was filmed in various locations in New Zealand, including the Bay of Plenty, Taupo, Wellington, Rotorua, and Tapanui [5].

Document [2] (wrongly cited in Mistral output)
Pete’s Dragon (2016 film). In March 2013, Walt Disney Pictures announced it would remake Pete’s Dragon, as non-musical, dramatic film to be written by David Lowery and Toby Halbrooks, and directed by Lowery. Disney intended to rework the core story as a dramatic story, rather than a musical. It set the new work in the Pacific Northwest of the early 1980s, rather than the Northern New England of the early 1900s in the 1977 film.

Table 16: Answer/Attribution generated by Mistral, Llama3, and GPT-4 in Authorship Informed RAG mode with synthetic relevant and human-written (original) non-relevant documents.

Query
who won the men’s single title of australia open on 1 february 2015
Ground Truth Answer:
'Novak Djokovic', 'Djokovic'
Groundtruth Document: [8]
2015 Australian Open – Men’s Singles. Djokovic went on to win his fifth Australian Open title by defeating Murray once again, 7–6(7–5), 6–7(4–7), 6–3, 6–0. The defeat saw Murray lose the Australian Open final for the fourth time in his career, becoming the first man in the Open Era to achieve four runner-up finishes in Melbourne.

Vanilla
Stan Wawrinka won the men's singles title at the 2015 Australian Open [2].

Informed
Stan Wawrinka won the men’s singles title at the 2014 Australian Open, defeating the world’s top two players [2]. However, the 2015 Australian Open men's singles title was won by Novak Djokovic, who defeated Andy Murray in the final [8].

CF-Informed
Stan Wawrinka won the men’s singles title at the 2015 Australian Open [2].

Document [2] (wrongly cited in Vanilla and CF-Informed RAG Modes)
2015 French Open – Men’s Singles. Wawrinka defeated Djokovic in the final, 4–6, 6–4, 6–3, 6–4, to win his first French Open and second Grand Slam title. As he did when he won the 2014 Australian Open, Wawrinka defeated the world No. 1 and world No. 2 on his way to the title.

Table 17: Answer/Attribution generated by GPT-4 in Vanilla, Authorship Informed, and Counterfactual-Authorship Informed RAG modes, with human-written (original) relevant and synthetic non-relevant documents. Reminding LLMs about the authors (Authorship Informed RAG mode) has resulted in a correct answer and attribution.

Reviewer paper summaries: This paper investigates the sensitivity and bias of LLMs in RAG pipelines towards authorship metadata of source documents. Specifically, it examines whether explicitly providing authorship information (human vs. LLM-generated) influences LLMs' attribution quality when generating answers with citations. Employing a novel counterfactual evaluation setup, the authors study attribution sensitivity (how attribution quality changes when LLMs are informed about authorship) and attribution bias (LLMs' tendency to prefer human-authored documents over LLM-generated ones or vice versa).
Summary of strengths: 1. This paper proposed authorship attribution sensitivity and attribution bias, which are novel and unexplored vulnerabilities.

2. The experiment settings (vanilla, authorship-informed, counterfactual) and metrics are well-designed,  clearly demonstrating the presence of attribution sensitivity and bias. The observation that LLMs consistently prefer human-authored metadata, even when content is identical, is compelling and counterintuitive to some prior research suggesting LLM-generated content might be preferred.

3. The paper is well-written and easy to follow.
Summary of weaknesses: 1. I appreciate the authors raised this interesting vulnerability about authorship attribution sensitivity and bias, and the comprehensive empirical analysis about it. However, how exactly will this phenomenon affect RAG systems in practice? What's the benefit of potential malicious users to manually mislabel human or LLM generated contents to pollute the retrieval result?

2. Continuing with Q1, perhaps there are also other metadata types beyond authorship, such as publication dates, publication sources/institutions/nationality, etc.? I think maybe pretty interesting conclusions could be drawn from these factors, showing some kind of systematic preference/bias that is widely existing in current LLMs, due to their training data/training pipeline. This could be very useful for both researchers and normal users to better understand the global bias/limitations of current LLM/RAG systems.

3. Also, it would be beneficial if the authors could propose some initial solutions or provide insights about how to mitigate this phenomenon.
Comments, suggestions and typos: No
Overall assessment scores: 3
Metareview score: 3.5
Metareview: This paper presents experiments that suggest that LLMs exhibit a bias towards human-attributed sources, even when the content is identical. This finding challenges prior work suggesting a preference for LLM-generated content.

There were three reviews for the paper with scores from 2 to 4. There was an active discussion thread on all reviews between authors and reviewers.

Reasons to accept:
Well-written and easy to follow
Thoughtfully-designed experimental setup 
Timely topic with exciting future directions 

Work to be addressed before publication:
Authors should analyze potential confounding factors impacting the results. This could either be extra analysis/experimentation, or a discussion of potential factors.
Terminology could be clearer, especially the metrics
Some missing methodological details could be added to the final manuscript
Summary of reasons to publish: All reviewers appreciated the clear writing of the paper and were excited about the topic and direction of the work. All were supportive of the methodological decisions made by the authors, though there were various nuances discussed.