CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery
arXiv:2602.23075v1 Announce Type: new Abstract: Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the ethical deployment of AI assistance, including (1) the trustworthiness of AI-generated content, (2) preservation of academic integrity and intellectual property, and (3) protection of information privacy. In this work, we present CiteLLM, a specialized agentic platform designed to enable trustworthy reference discovery for grounding author-drafted claims and statements. The system introduces a novel interaction paradigm by embedding LLM utilities directly within the LaTeX editor environment, ensuring a seamless user experience and no data transmission outside the local system. To guarantee hallucination-free references, we employ dynamic discipline-aware routing to retrieve candidates exclusively from trusted web-based academic repositories, while leveraging LLMs solely for generating context-a
arXiv:2602.23075v1 Announce Type: new Abstract: Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the ethical deployment of AI assistance, including (1) the trustworthiness of AI-generated content, (2) preservation of academic integrity and intellectual property, and (3) protection of information privacy. In this work, we present CiteLLM, a specialized agentic platform designed to enable trustworthy reference discovery for grounding author-drafted claims and statements. The system introduces a novel interaction paradigm by embedding LLM utilities directly within the LaTeX editor environment, ensuring a seamless user experience and no data transmission outside the local system. To guarantee hallucination-free references, we employ dynamic discipline-aware routing to retrieve candidates exclusively from trusted web-based academic repositories, while leveraging LLMs solely for generating context-aware search queries, ranking candidates by relevance, and validating and explaining support through paragraph-level semantic matching and an integrated chatbot. Evaluation results demonstrate the superior performance of the proposed system in returning valid and highly usable references.
Executive Summary
The article introduces CiteLLM, an agentic platform designed to facilitate trustworthy scientific reference discovery. By integrating large language models (LLMs) within the LaTeX editor environment, CiteLLM aims to enhance the efficiency of scholarly activities while addressing ethical concerns such as the trustworthiness of AI-generated content, academic integrity, intellectual property, and information privacy. The system employs dynamic discipline-aware routing to retrieve references from trusted academic repositories, ensuring hallucination-free results. Evaluation results indicate that CiteLLM outperforms existing methods in returning valid and highly usable references.
Key Points
- ▸ Integration of LLMs within the LaTeX editor environment for seamless user experience.
- ▸ Dynamic discipline-aware routing to retrieve references from trusted academic repositories.
- ▸ Use of LLMs for generating context-aware search queries, ranking candidates, and validating support.
- ▸ Evaluation results demonstrate superior performance in returning valid and usable references.
Merits
Innovative Integration
The integration of LLMs within the LaTeX editor environment is a novel approach that ensures a seamless user experience and local data processing, addressing privacy concerns.
Trustworthy References
The use of dynamic discipline-aware routing to retrieve references from trusted academic repositories ensures that the references are valid and hallucination-free.
Comprehensive Evaluation
The evaluation results provide strong evidence of the system's superior performance in returning valid and highly usable references.
Demerits
Limited Scope
The system's focus on LaTeX editor integration may limit its accessibility to users who do not use LaTeX, potentially excluding a significant portion of the academic community.
Dependency on Trusted Repositories
The reliance on trusted academic repositories for reference retrieval may introduce biases or limitations if the repositories are not comprehensive or up-to-date.
Potential for Over-Reliance on AI
The use of LLMs for generating search queries and ranking candidates may lead to over-reliance on AI, potentially diminishing the critical thinking skills of researchers.
Expert Commentary
The article presents a significant advancement in the field of AI-assisted scholarly activities. The integration of LLMs within the LaTeX editor environment is a novel approach that addresses several ethical concerns related to the deployment of AI in academia. The use of dynamic discipline-aware routing to retrieve references from trusted academic repositories ensures the trustworthiness of the references, which is crucial for maintaining academic integrity. The evaluation results provide strong evidence of the system's superior performance, making it a valuable tool for researchers. However, the system's focus on LaTeX editor integration may limit its accessibility to a broader audience. Additionally, the reliance on trusted academic repositories may introduce biases or limitations. Despite these limitations, the article contributes significantly to the ongoing discussion on the ethical deployment of AI in academic settings and highlights the potential of AI to enhance the efficiency and accuracy of scholarly communication.
Recommendations
- ✓ Expand the system's compatibility to include other popular academic writing tools to increase accessibility.
- ✓ Develop mechanisms to ensure the comprehensiveness and up-to-date nature of the trusted academic repositories used for reference retrieval.
- ✓ Encourage critical thinking and human oversight in the use of AI-generated references to mitigate the potential for over-reliance on AI.
