Papers by Tugba Temizel
HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs (2024.eacl-srw)
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| Challenge: | Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs), limiting their widespread acceptance beyond chatbot applications. |
| Approach: | They propose a framework that combines benchmarking LLMs’ hallucination tendencies with efficient hallucinian detection. |
| Outcome: | The proposed framework provides opportunities to test and improve LLMs and can generate benchmarking datasets tailored to specific domains. |