Leveraging Human Computation Gaming to Enhance Knowledge Graphs for Accuracy-Critical Generative AI Applications

المفاهيم الأساسية

A framework called GAME-KG that leverages human computation gaming to modify and validate knowledge graphs, enabling their use to augment large language models in accuracy-critical domains.

الملخص

The GAME-KG framework is a six-step approach that leverages human computation gaming (HCG) to modify and validate knowledge graphs (KGs) for use in accuracy-critical generative AI applications.
The key steps are:

Discover Data: Identify and collect the documents that will be used to develop the domain model.
Parse Text: Extract entities and their relationships from the documents.
Build KG and Generate Narrative: Construct the KG from the parsed text, and use a language model to generate a fictionalized narrative based on the KG.
Identify Graph: Determine which parts of the KG may benefit most from human feedback, e.g. based on connection strength.
Inject Graph and Narrative: Incorporate the KG and narrative into the HCG.
Collect Player Feedback: Players provide feedback to modify and validate the KG, which is then used to update the graph.

The framework is demonstrated through two examples:

A Unity-based video game called "Dark Shadows" that collects player feedback to modify KGs parsed from human trafficking press releases.
An experiment where GPT-4 is prompted to answer questions based on the original KG and a human-modified KG, showing how the modified KG can provide more explainable responses.

The initial results suggest that GAME-KG can be an effective way to enhance KGs with human feedback, enabling their use to augment large language models in accuracy-critical domains like human trafficking data analysis.

الإحصائيات

Kizer trafficked victims across state borders.
Villaman was an accomplice to Kizer.

اقتباسات

"Providing LLMs with external, structured representations of facts in the form of knowledge graphs (KGs) has proven useful for addressing this limitation [of explainability]."
"Crowdsourcing has been used to collect knowledge that may be obvious to a human but not a machine."

الرؤى الأساسية المستخلصة من

A Framework for Leveraging Human Computation Gaming to Enhance Knowledge Graphs for Accuracy Critical Generative AI Applications

by Steph Buongi... في arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19729.pdf

استفسارات أعمق

How can the GAME-KG framework be extended to other accuracy-critical domains beyond human trafficking?

The GAME-KG framework can be extended to other accuracy-critical domains by adapting the approach to suit the specific requirements and nuances of those domains. For instance, in domains like healthcare or finance where accuracy and explainability are crucial, the framework can be tailored to parse relevant documents and create knowledge graphs that capture the domain-specific information. By leveraging Human Computation Gaming (HCG) to collect feedback through engaging video game mechanics, the framework can involve domain experts or stakeholders to validate and modify the knowledge graphs. This process ensures that the information represented in the graphs is accurate and comprehensive, enhancing the trustworthiness of generative AI applications in those domains. Additionally, by incorporating diverse datasets and scenarios from different accuracy-critical domains, the framework can be generalized and applied to a wide range of fields beyond human trafficking.

What potential biases or limitations could be introduced by relying on human feedback to modify knowledge graphs?

Relying on human feedback to modify knowledge graphs introduces the potential for biases and limitations that need to be carefully addressed. One potential bias is the subjective interpretation of information by human annotators, which can lead to inconsistencies in how connections are made in the knowledge graphs. This subjectivity can introduce errors or inaccuracies, especially when dealing with implicit knowledge that may vary based on individual perspectives. Moreover, there is a risk of introducing human errors or oversights during the feedback collection process, which can impact the quality and reliability of the modified knowledge graphs. Additionally, the scalability of collecting human feedback through HCG may pose limitations in terms of the volume of data that can be processed and the time required for validation. Ensuring diversity in the pool of human annotators and implementing robust validation mechanisms are essential to mitigate biases and limitations when relying on human feedback to modify knowledge graphs.

How might the GAME-KG framework be adapted to collect feedback on the quality and reliability of the generated narratives, in addition to the knowledge graphs?

To collect feedback on the quality and reliability of the generated narratives in addition to the knowledge graphs, the GAME-KG framework can incorporate interactive mechanisms within the video game environment. Players can be prompted to provide feedback on the coherence, consistency, and relevance of the narratives generated by the AI models. This feedback can be collected through in-game surveys, quizzes, or interactive storytelling elements that allow players to rate the narratives based on their understanding and engagement. Additionally, the framework can implement a validation system where players can flag inconsistencies or inaccuracies in the narratives, similar to how modifications are made to the knowledge graphs. By integrating feedback mechanisms specifically focused on narrative quality, the framework can ensure that the generated narratives align with the intended domain knowledge and are reliable for downstream generative AI applications.