Papers with knowledge utilization
Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)
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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution. |
| Approach: | They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research . |
| Outcome: | The proposed model can be used to analyze the evolution of parametric knowledge in LLMs. |
E2CL: Exploration-based Error Correction Learning for Embodied Agents (2024.findings-emnlp)
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| Challenge: | Language models are exhibiting increasing capability in knowledge utilization and reasoning, but they often suffer from misalignment between their intrinsic knowledge and environmental knowledge, leading to infeasible actions. |
| Approach: | They propose a framework that leverages exploration-induced errors and environmental feedback to enhance environment alignment for embodied agents. |
| Outcome: | The proposed framework outperforms baseline methods and exhibits superior self-correction capabilities. |
Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering (2024.findings-emnlp)
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| Challenge: | Knowledge graph question answering (KGQA) aims to provide factual answers to natural language questions by leveraging structured information stored in a knowledge graph. |
| Approach: | They propose a Question-guided Knowledge Graph Re-scoring method to eliminate noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
| Outcome: | The proposed method eliminates noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. |
FAC2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are evaluated by overall performance on various text understanding and generation tasks. |
| Approach: | They propose a framework for Fine-grAined and Cognition-grounded LLMs’ Capability Evaluation that dissociates the language-related capabilities from cognition-related ones. |
| Outcome: | The proposed framework dissociates the language-related capabilities from cognition-related ones and breaks down the process of applying a specific capability into three sub-steps: recalling relevant knowledge, utilizing knowledge, and solving problems. |
LOKA: Conflict-Aware LLM Knowledge Update with Adaptive Knowledge Memory (2026.acl-long)
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| Challenge: | Existing approaches that tackle unlearning and learning separately encounter *task conflicts* and *knowledge management issues* when applied to comprehensive knowledge updates. |
| Approach: | They propose a conflict-aware framework for Large language mOdel Knowledge updAtes that integrates updated knowledge across multiple memory units during training and integrates it with original LLM. |
| Outcome: | The proposed framework is based on theoretical analysis and empirical evidence and validates the proposed framework with empirical and theoretical evidence. |
Evaluating LLMs’ Assessment of Mixed-Context Hallucination Through the Lens of Summarization (2025.findings-acl)
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| Challenge: | Large language models generate coherent text and follow instructions across diverse tasks, but a critical challenge in scaling LLM applications is hallucination, where the generated content lacks factual grounding or deviates from the intended discourse context. |
| Approach: | They use summarization as a representative task to evaluate LLMs' capability in detecting mixed-context hallucinations, specifically distinguishing between factual and non-factual hallucinos. |
| Outcome: | The proposed model distinguishes between factual and non-factual hallucinations, and their performance bottlenecks. |