Workshop on Knowledge Graph and Large Language Models (KaLLM)

Large Language Models (LLMs) have revolutionised the field of Natural Language Processing (NLP) by improving contextual understanding, zero-shot and few-shot learning, text generation, and question answering. However, LLMs have limitations such as accuracy, bias, interpretability, and context. They often produce factually inaccurate information, struggle to understand complex contexts, and may unintentionally produce biased or stereotyped information. KGs, on the other hand, have high-quality explicit knowledge, symbolic reasoning potential, and the ability to evolve with new knowledge, making them essential for various applications. The intersection of LLMs and KGs has sparked significant interest and innovation in NLP. LLM-enhanced KGs can be introduced into pre-training and inference stages to provide external knowledge and assess interpretability. LLM-augmented KGs are designed to improve coverage and ease the use of KGs in various tasks, including embedding learning, completion, construction, KG-to-text generation, and KGQA. Combining the benefits of LLMs and KGs can also improve performance in knowledge representation.

The workshop intends to provide a platform for researchers, practitioners, and industry professionals to explore the synergies between LLMs and KGs. We aim to provide a space for the LLM community and the community of KG researchers to interact and explore how these two communities could collaborate and support one another. The goal of the workshop is to seize on the tremendous opportunities arising from investigating cutting-edge approaches, addressing challenges and limitations, and applications in different domains.

Call for Papers

The submissions should focus on the interaction between LLMs and KGs in the context of NLP. The workshop will cover a diverse range of topics related to the integration of LLMs and KGs, including but not limited to:

Knowledge-enhanced language generation
KG-based question answering using LLMs
Fact validation and bias mitigation
KG creation and completion using LLMs
Privacy considerations in LLM-KG integration
Interpretability and explainability
Cross-domain applications
KG-based text summarisation with LLMs
Ethical implications of LLM-KG technologies
Multimodality of KGs and LLMs
Multilingual LLMs for KGs and vice-versa

Submission Guidelines

We invite the following types of submissions:

Long papers must be original unpublished work, with concrete evaluation and analysis. They can be up to 8 (eight) pages, plus unlimited pages of references and appendix.
Short papers should be original unpublished work, including a small focused contribution, a negative result, an opinion piece, or an interesting application. They can be of up to 4 (four) pages of content with unlimited pages of references and appendix.
Resource papers must include new dataset(s) or other resource(s) directly relevant to KGs and LLMs, including the publication of that resource. The resource papers can be up to 8 (eight) pages, plus unlimited pages of references and appendix.

All submissions should be anonymous and must follow ACL formatting guidelines. The ACL 2024 templates must be used; these are provided in LaTeX and also Microsoft Word format. Submissions will only be accepted in PDF format. The peer review will be double-blind. The workshop will not accept work that is under review or has already been published in or accepted for publication in any other peer-reviewed venue. However, papers published in arXiv are allowed to be submitted to the workshop.

All submissions have to be made through the following Openreview link.

Authors should be aware of OpenReview's moderation policy for newly created profiles:

New profiles created without an institutional email will go through a moderation process that can take up to two weeks.
New profiles created with an institutional email will be activated automatically.

Best Paper Award

We will recognise the best paper with an award. Reviewers will be asked to flag papers they deem worthy of a prize. The general chairs will set up a small panel that will determine the winner. The award will be sponsored by Bloomberg.