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Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings.
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Still no lie detector for language models: Probing empirical and conceptual roadblocks
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A holistic approach to undesired content detection in the real world
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Microsoft’s politically correct chatbot is even worse than its racist one
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Detoxifying language models risks marginalizing minority voices
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Confronting reward model overoptimization with constrained RLHF
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Language artificial intelligences’ communicative performance quantified through the Gricean conversation theory
Yunju Nam, Hyenyeong Chung, and Upyong Hong. 2023 · 2023
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AI deception: A survey of examples, risks, and potential solutions
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Fine-tuning aligned language models compromises safety, even when users do not intend to!
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Simple synthetic data reduces sycophancy in large language models
Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, and Quoc V Le. 2023 · 2023
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Rongwu Xu, Brian S Lin, Shujian Yang, Tianqi Zhang, Weiyan Shi, Tianwei Zhang, Zhixuan Fang, Wei Xu, and Han Qiu. 2023 · 2023
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Uncertainty-penalized reinforcement learning from human feedback with diverse reward LoRA ensembles
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Siren’s song in the AI ocean: A survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023 · 2023
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Detectors for safe and reliable llms: Implementations, uses, and limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, et al. 2024 · 2024
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