Citation practices are crucial in shaping the structure of scientific
knowledge, yet they are often influenced by contemporary norms and biases. The
emergence of Large Language Models (LLMs) like GPT-4 introduces a new dynamic
to these practices. Interestingly, the characteristics and potential biases of
references recommended by LLMs that entirely rely on their parametric
knowledge, and not on search or retrieval-augmented generation, remain
unexplored. Here, we analyze these characteristics in an experiment using a
dataset of 166 papers from AAAI, NeurIPS, ICML, and ICLR, published after
GPT-4's knowledge cut-off date, encompassing 3,066 references in total. In our
experiment, GPT-4 was tasked with suggesting scholarly references for the
anonymized in-text citations within these papers. Our findings reveal a
remarkable similarity between human and LLM citation patterns, but with a more
pronounced high citation bias in GPT-4, which persists even after controlling
for publication year, title length, number of authors, and venue. Additionally,
we observe a large consistency between the characteristics of GPT-4's existing
and non-existent generated references, indicating the model's internalization
of citation patterns. By analyzing citation graphs, we show that the references
recommended by GPT-4 are embedded in the relevant citation context, suggesting
an even deeper conceptual internalization of the citation networks. While LLMs
can aid in citation generation, they may also amplify existing biases and
introduce new ones, potentially skewing scientific knowledge dissemination. Our
results underscore the need for identifying the model's biases and for
developing balanced methods to interact with LLMs in general.

大型语言模型（LLMs）如 GPT-4 在引用实践中引入新的动态，并且可能放大现有的偏见和引入新的偏见，从而可能扭曲科学知识的传播。

大型语言模型反映人类引用模式，具有较高的引用偏好

Large Language Models Reflect Human Citation Patterns with a Heightened  Citation Bias

Recent lay language generation systems have used Transformer models trained
on a parallel corpus to increase health information accessibility. However, the
applicability of these models is constrained by the limited size and topical
breadth of available corpora. We introduce CELLS, the largest (63k pairs) and
broadest-ranging (12 journals) parallel corpus for lay language generation. The
abstract and the corresponding lay language summary are written by domain
experts, assuring the quality of our dataset. Furthermore, qualitative
evaluation of expert-authored plain language summaries has revealed background
explanation as a key strategy to increase accessibility. Such explanation is
challenging for neural models to generate because it goes beyond simplification
by adding content absent from the source. We derive two specialized paired
corpora from CELLS to address key challenges in lay language generation:
generating background explanations and simplifying the original abstract. We
adopt retrieval-augmented models as an intuitive fit for the task of background
explanation generation, and show improvements in summary quality and simplicity
while maintaining factual correctness. Taken together, this work presents the
first comprehensive study of background explanation for lay language
generation, paving the path for disseminating scientific knowledge to a broader
audience. CELLS is publicly available at:
this https URL.

介绍了 CELLS，这是最大（63k 对）和领域最广泛（12 个期刊）的平行语料库，旨在提高健康信息可访问性。该研究通过派生两个特殊对的平行语料库，采用检索增强模型来解决简化原始摘要和生成背景解释的关键挑战，同时保持事实正确性，并通过定性评估显示了显示了质量的提高和简化的优点，为推广科学知识到更广泛的受众铺平了道路。