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Can Large Language Models Capture Dissenting Human Voices?

, , and . EMNLP, page 4569-4585. Association for Computational Linguistics, (2023)

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Neural Databases., , , , , and . CoRR, (2020)Using species distribution models to predict new occurrences for rare plants, , , , , , and . Diversity and Distributions, (2009)Evidence-based Verification for Real World Information Needs., , , , and . CoRR, (2021)Prometheus: Inducing Fine-grained Evaluation Capability in Language Models., , , , , , , , , and 1 other author(s). CoRR, (2023)CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean., , , , , and . CoRR, (2024)FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets., , , , , , , , and . CoRR, (2023)Capturing the Relationship Between Sentence Triplets for LLM and Human-Generated Texts to Enhance Sentence Embeddings., , and . EACL (Findings), page 624-638. Association for Computational Linguistics, (2024)Generating Token-Level Explanations for Natural Language Inference., , , and . NAACL-HLT (1), page 963-969. Association for Computational Linguistics, (2019)Detrimental Contexts in Open-Domain Question Answering., and . EMNLP (Findings), page 11589-11605. Association for Computational Linguistics, (2023)Adversarial attacks against Fact Extraction and VERification., and . CoRR, (2019)