Context-Enhanced Large Language Models for Generating Coherent Multi-Paper Citations

Core Concepts

Leveraging large language models and knowledge graphs to generate coherent multi-sentence citations that accurately capture the relationships between source and target research papers.

Abstract

The paper presents a method for generating multi-sentence citation text using large language models (LLMs) such as LLaMA, Alpaca, and Vicuna. The authors curated a new dataset called MCG-S2ORC from the S2ORC corpus, which contains citations referencing multiple papers in a single sentence or paragraph.
The key highlights of the work are:

The authors fine-tuned the LLM models on the MCG-S2ORC dataset for the task of multi-citation text generation. Their experiments showed that the Vicuna model outperformed the other LLMs.

To further improve the performance, the authors incorporated knowledge graphs extracted from the source and target paper abstracts, introductions, and conclusions into the prompts used for fine-tuning the LLMs. This knowledge-enhanced prompting led to significant improvements in the quality and coherence of the generated citation text.

The authors conducted extensive evaluations using standard metrics like METEOR, ROUGE-1, ROUGE-2, and ROUGE-L, demonstrating the effectiveness of their approach.

The paper highlights the advantages of using citation generation models, such as saving time, ensuring accuracy and consistency, reducing plagiarism, and serving as educational resources.

Overall, the work showcases the potential of leveraging large language models and knowledge graphs to automate the citation generation process, enabling researchers to focus more on their core work while maintaining proper source attribution.

Stats

The average number of characters in the citation texts is 227.29.
The maximum number of characters in the citation texts is 2416.
The average number of characters in the source abstracts is 1122.95.
The maximum number of characters in the source abstracts is 5516.
The average number of characters in the target abstracts is 998.48.
The maximum number of characters in the target abstracts is 93551.
The average number of target papers per sample is 2.

Quotes

"Using a CTG model offers numerous advantages. Firstly, it significantly saves time by automating the citation generation process, allowing researchers, students, and authors to focus more on their work. Secondly, these models ensure accuracy and consistency by following specific citation styles and providing correct formatting for various source types."
"To enhance citation generation, we integrated knowledge graphs into the model's prompts by extracting entity relations from the source and target paper's abstracts, introductions, and conclusions using PL-Marker. Our experiments showed that incorporating knowledge graphs significantly improved the performance and text generation capabilities of the models, enabling better comprehension of relations between source and target papers."

Key Insights Distilled From

Context-Enhanced Language Models for Generating Multi-Paper Citations

by Avinash Anan... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13865.pdf

Deeper Inquiries

How can the proposed approach be extended to handle a larger number of target papers per source paper, and what challenges might arise in doing so?

To extend the proposed approach to handle a larger number of target papers per source paper, several adjustments and considerations need to be made. One approach could involve restructuring the dataset to accommodate multiple target papers for each source paper, ensuring that the model can effectively process and generate citations for a larger set of references. Additionally, the prompts provided to the Large Language Models (LLMs) can be modified to incorporate the additional target papers' information, potentially by concatenating the abstracts, introductions, and conclusions of all target papers.
Challenges that may arise when handling a larger number of target papers include:

Increased Complexity: With more target papers, the model's task becomes more complex as it needs to consider a wider range of information and relationships between multiple sources and citations.
Token Limitations: LLMs have token limitations, so incorporating a large number of target papers may exceed these limits, requiring creative solutions to manage the input data effectively.
Training Data Quality: Ensuring the quality and relevance of the training data becomes crucial when dealing with a larger dataset to prevent biases or inaccuracies in the generated citations.
Computational Resources: Processing a larger dataset requires more computational resources, potentially leading to longer training times and increased computational costs.

Addressing these challenges would involve optimizing the data preprocessing pipeline, refining the model architecture to handle larger inputs, and potentially exploring techniques like hierarchical modeling to manage the increased complexity efficiently.

How can the proposed approach be extended to handle a larger number of target papers per source paper, and what challenges might arise in doing so?

To further enhance the coherence and accuracy of the generated multi-sentence citations, beyond leveraging knowledge graphs, several other types of contextual information can be considered:

Semantic Similarity: Utilizing semantic similarity measures to assess the relatedness between the source paper and target papers can help in generating more contextually relevant citations.
Citation Networks: Incorporating information from citation networks to understand the citation patterns and relationships between papers, which can guide the generation of citations.
Domain-Specific Knowledge: Integrating domain-specific knowledge bases or ontologies to provide additional context and domain expertise for generating citations.
Author Information: Considering information about the authors, their research interests, and affiliations can offer valuable context for generating citations that align with the authors' backgrounds and expertise.
Temporal Context: Taking into account the temporal context of the papers, such as publication dates and historical significance, can aid in generating citations that reflect the evolution of research over time.

By incorporating these additional contextual cues, the citation generation model can produce more accurate and coherent multi-sentence citations that capture the intricate relationships between scientific documents effectively.

Given the potential impact of citation generation models on academic writing and research practices, what ethical considerations should be taken into account when developing and deploying such systems?

When developing and deploying citation generation models, several ethical considerations should be prioritized to ensure responsible and ethical use of the technology:

Plagiarism Detection: Citation generation models should be equipped with robust plagiarism detection mechanisms to prevent unintentional plagiarism and ensure proper attribution of sources.
Transparency: The models should be transparent about their citation generation process, including how citations are generated, the sources of information used, and any biases in the model.
Bias Mitigation: Efforts should be made to identify and mitigate biases in the model that could impact the accuracy and fairness of generated citations, especially concerning underrepresented authors or topics.
Data Privacy: Safeguarding the privacy of researchers and authors by ensuring that sensitive information in research papers is not misused or disclosed inappropriately during the citation generation process.
Validation and Verification: Implementing mechanisms for validating and verifying the accuracy of generated citations to maintain the integrity of academic writing and research practices.
User Education: Providing clear guidelines and educational resources to users on the proper use of citation generation models, emphasizing the importance of ethical citation practices and academic integrity.

By addressing these ethical considerations, developers and users of citation generation models can promote ethical conduct, academic integrity, and responsible research practices in the scholarly community.