Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have a tendency to hallucinate false or misleading information, limiting their reliability. |
| Approach: | They examine how architecture-based inductive biases affect the propensity to hallucinate . they find that the models are more reliable and more reliable than traditional models . |
| Outcome: | The proposed models can be used to train and train large language models that are factual or able to explain themselves through their knowledge. |
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| Challenge: | Grasping the intricacies of hallucination in LLMs can be daunting, especially for those new to the field. |
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| Challenge: | Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. |
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| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
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| Challenge: | Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI) |
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| Challenge: | Large language models (LLMs) have been shown to possess impressive capabilities, but they are not problem-free. |
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An Audit on the Perspectives and Challenges of Hallucinations in NLP (2024.emnlp-main)
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Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs, Mukund Srinath, Koustava Goswami, Sarah Rajtmajer, Shomir Wilson
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Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models generate hallucinated text when confronted with false premise questions . authors propose a method to mitigate false premises hallucinosity . |
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