Papers by Ihab Ilyas
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to generate knowledge graphs are unable to handle non-English textual information. |
| Approach: | They propose a task of automatic Knowledge Graph Completion to bridge the gap between English and non-English textual information. |
| Outcome: | The proposed method bridges the gap between the quantity and quality of textual information between English and non-English languages. |
Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia (C18-1)
Copied to clipboard
| Challenge: | Existing datasets for question answering over knowledge graphs lack answer triples from Freebase . a defunct knowledge graph makes it difficult to build "real-world" question answering systems . |
| Approach: | They propose a benchmark dataset for simple question answering over knowledge graphs that maps SimpleQuestions entities and predicates from Freebase to DBpedia. |
| Outcome: | The proposed dataset provides simple yet strong baselines with and without neural networks. |
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge (2023.emnlp-demo)
Copied to clipboard
Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang, Anton Belyy, Samira Khorshidi, Fei Wu, Ihab Ilyas, Yunyao Li
| Challenge: | Existing large language models (LLMs) have a tendency to hallucinate and provide creative and fluent responses that are not factually accurate. |
| Approach: | They propose a tool that automatically extracts factual claims from text, gathers evidence from external knowledge sources, evaluates the factuality of each claim, and suggests revisions for identified errors. |
| Outcome: | The proposed tool detects errors in text and evaluates their factuality and suggests revisions based on the collected evidence. |
ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large Language Models (2024.emnlp-industry)
Copied to clipboard
Ronak Pradeep, Daniel Lee, Ali Mousavi, Jeffrey Pound, Yisi Sang, Jimmy Lin, Ihab Ilyas, Saloni Potdar, Mostafa Arefiyan, Yunyao Li
| Challenge: | Knowledge Graphs (KGs) are a powerful tool for capturing structured representations of the world. |
| Approach: | They propose a scalable method for generating up-to-date and configurable conversational KGQA datasets that adheres to human interaction configurations and operates at a significantly larger scale. |
| Outcome: | Qualitative psychometric analyses show that ConvKGYarn produces high-quality data comparable to popular conversational KGQA datasets across various metrics. |