Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models (2025)

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 vulnerability of LLMs.

## 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 @xcite . large language models (llms) have been a crucial part of rag pipelines, mainly as the generator component @xcite question retrieval ... ... ## 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 @xcite @xcite . counterfactual evaluation has been used across various natural language processing and information retrieval tasks @xcite @xcite . 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 . ## experimental settings models. we use gpt-4-0314 (openai, 2023), meta-llama/meta-llama-3-8b-instruct, 3 and mistralai/mistral-7b-instruct-v0.3 4 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) @xcite and ms marco question answering @xcite (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 @xcite . we note that we study the effect of different retrievers and different 3 https://huggingface.co/meta-llama/ meta-llama-3-8b-instruct 4 https://huggingface.co/mistralai/ mistral-7b-instruct-v0.3 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. ## 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 @xmath0.05. attribution quality. attribution sensitivity and bias. ## 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 @xcite . 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.