An Investigation of LLMs’ Inefficacy in Understanding Converse Relations (2023.emnlp-main)
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| Challenge: | Existing benchmarks for Large Language Models (LLMs) follow the data distribution of pre-training data. |
| Approach: | They propose a benchmark ConvRe focusing on converse relations which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets. |
| Outcome: | The proposed benchmark focuses on converse relations, which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets. |
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| Challenge: | Existing benchmarks of social language are lacking for large language models. |
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| Challenge: | Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. |
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| Challenge: | Recent studies focus on performance benchmarks without fully comparing LLMs to graph learning models. |
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Can Large Language Models Understand DL-Lite Ontologies? An Empirical Study (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown remarkable proficiency in understanding textual data and revolutionizing the field of natural language processing. |
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| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
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LLM4RE: A Data-centric Feasibility Study for Relation Extraction (2025.coling-main)
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LLMs meet Bloom’s Taxonomy: A Cognitive View on Large Language Model Evaluations (2025.coling-main)
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| Challenge: | Existing evaluation approaches for Large Language Models lack a structured approach that reflects the underlying cognitive abilities required for solving the tasks. |
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CUTE: Measuring LLMs’ Understanding of Their Tokens (2024.emnlp-main)
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Lexical Semantics with Large Language Models: A Case Study of English “break” (2023.findings-eacl)
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| Challenge: | Large neural language models (LLMs) can be powerful tools for research in lexical semantics. |
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