| Challenge: | Large, parametric language models (LLMs) produce fluent text for many applications . hallucinations are generation of text that is factually correct and semantically plausible . |
| Approach: | They propose to use learning-tuned LLMs to infuse models with retrieval mechanisms to reduce hallucinations. |
| Outcome: | The proposed approach reduces the frequency of hallucinations by reducing the coverage of relevant facts and generating more informative responses while providing higher attribution rates. |
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| Challenge: | Large language models (LLMs) often produce factual errors due to limited internal knowledge. |
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| Challenge: | Large language models (LLMs) are susceptible to hallucinations and out-of-distribution errors when generating KG elements, such as Uniform Resource Identifiers (URIs). |
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Hallucination Detection for Grounded Instruction Generation (2023.findings-emnlp)
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| Challenge: | Existing models for generating instructions for navigation generate references to objects or actions that are inconsistent with what a human follower would perform or encounter along the path. |
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Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data (2020.findings-emnlp)
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| Challenge: | Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case. |
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Learning to Plan and Generate Text with Citations (2024.acl-long)
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Constanza Fierro, Reinald Kim Amplayo, Fantine Huot, Nicola De Cao, Joshua Maynez, Shashi Narayan, Mirella Lapata
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| Challenge: | Large language models have shown a powerful ability for text generation, but undesired behaviors such as toxicity and hallucinations can manifest. |
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| Challenge: | Large Language Models (LLMs) driven by In-Context Learning (ICL) have improved performance of text-to-SQL. |
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| Challenge: | generative large language models produce hallucinations that are not aligned with world knowledge or input context. |
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PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations (2026.acl-long)
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Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang, Yutong Zhang, Yujie Chen, Jiaming Shang, Guang Zhang, Zhuang Liu
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Enhancing Hallucination Detection via Future Context (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. |
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