Archivierte Veranstaltungen Detail

Vortragende Person/Vortragende Personen:
Rada Mihalcea et al.

A workshop co-located with the 62nd Annual Meeting of the Association for Computational Linguistics (ACL-2024) in Bangkok, Thailand on August 11–16, 2024.  TextGraph fosters investigation of synergies between methods for text and graph processing. This edition focuses on the fusion of LLMs with KGs.

This year, we are honored to have Rada Mihalcea as the invited speaker at the TextGraphs workshop!

We welcome presentation from papers accepted to the main/Findings of ACL conference!  Please contact textgraphsOC@gmail.com with the reference to the paper and acceptance letter from ACL. 

For the past seventeen years, the workshops in the TextGraphs series have published and promoted the synergy between the field of Graph Theory (GT) and Natural Language Processing (NLP). The mix between the two started small, with graph-theoretical frameworks providing efficient and elegant solutions for NLP applications. Graph-based solutions initially focused on single-document part-of-speech tagging, word sense disambiguation, and semantic role labeling. They became progressively larger to include ontology learning and information extraction from large text collections. Nowadays, graph-based solutions also target Web-scale applications such as information propagation in social networks, rumor proliferation, e-reputation, multiple entity detection, language dynamics learning, and future events prediction, to name a few.

We plan to encourage the description of novel NLP problems or applications that have emerged in recent years, which can be enhanced with existing and new graph-based methods. We widen the workshop topics beyond the familiar graph domain, encompassing a broader range of less examined structured data domains as well. The seventeenth edition of the TextGraphs workshop aims to extend the focus on exploring rising topics of large language models (LLMs) prompting from the unique perspective of GT. Therefore, our workshop aims to foster stronger, mutually advantageous connections between NLP and structured data, tackling key challenges inherent in each field.

TextGraphs-17 invites submissions on (but not limited to) the following topics:

1. Knowledge Graphs Meet LLMs. A proper utilization of graph-based methods for reasoning over a Knowledge Graph (KG) is a prospective way to overcome critical limitations of the existing LLMs which lack interpretability and factual knowledge and are prone to the hallucination problem. Vice versa, the incorporation of LLM knowledge learnt from large textual collections may help many graph-related tasks, such as KG completion and graph representation learning. Thus, we are highly interested in novel research on the joint use of KG and LLM for an improved processing of either the NLP or graph domain (preferably both).

2. Chain Prompting of LLMs. Recent studies show that prompting strategies like Chain-of-Thought and Graph-of-Thought enhance language understanding and generation tasks compared to the traditional few-shot methods. We welcome submissions developing advanced prompting schemes and software for LLMs and other pre-trained machine learning models.

3. Learning from Structured Data. We greet novel efforts to build a bridge between NLP and various structured data formats including relational and non-relational databases, as well as standardized data formats (such as XML, JSON, RDF, etc.)

4. Interpretability of NLP Systems. The question of interpretability poses a fundamental challenge for the practical application of NLP methods. We  invite researchers to adopt structured data and employ graph-based methods to shed light on decision-making  and logic behind modern LLMs. Any work on applying a KG or any other structured knowledge to explore and evaluate factual awareness, treating the interpretability problem from the GT perspective, or any other topic that utilizes graphs and other structured data to make LLMs more understandable, is met with appreciation.

Further information and registration