Papers by Ze Yu Zhang
Prompting the Unknown: Understanding Response Uncertainty in Large Language Models (2026.findings-acl)
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| Challenge: | Large language models are widely used in decision-making across diverse domains. |
| Approach: | They propose a prompt-response concept model that explains the relationship between the amount of task-relevant information provided in the prompt and the LLM-generated response uncertainty by identifying four sources of response uncertainty. |
| Outcome: | The proposed model shows that the amount of information provided in the prompt influences the LLM-generated response uncertainty. |
Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graph for Retrieval-Augmented Generation (2026.eacl-long)
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| Challenge: | Standard unstructured RAG methods rely on embedding-similarity matching and lack any general mechanism to encode or exploit chronological information. |
| Approach: | They propose a retrieval-augmented generation framework that integrates a document retrieval generator with an exter-nal document retriever to enhance the model's accuracy. |
| Outcome: | The proposed framework outperforms state-of-the-art unstructured and KG-based RAG frameworks on causal and character consistency queries. |