SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities

Pearson correlation coefficient

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A general language assistant as a laboratory for alignment

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Program synthesis with large language models

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Evaluating large language models trained on code

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Training verifiers to solve math word problems

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Large language models are zero-shot reasoners

Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022

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Training language models to follow instructions with human feedback

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Chain-of-thought prompting elicits reasoning in large language models

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Alphazero-like tree-search can guide large language model decoding and training

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Xidong Feng, Ziyu Wan, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, and Jun Wang. 2023 · 2023

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Llama guard: Llm-based input-output safeguard for human-ai conversations

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Let’s verify step by step

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Llama 2: Open foundation and fine-tuned chat models

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Jailbroken: How does LLM safety training fail?

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Judging llm-as-a-judge with mt-bench and chatbot arena

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Least-to-most prompting enables complex reasoning in large language models

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Universal and transferable adversarial attacks on aligned language models

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Mle-bench: Evaluating machine learning agents on machine learning engineering

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Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, et al. 2024 · 2024

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Deliberative alignment: Reasoning enables safer language models

Qwq: Reflect deeply on the boundaries of the unknown

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Badchain: Backdoor chain-of-thought prompting for large language models

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SafeDecoding: Defending against jailbreak attacks via safety-aware decoding

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Tree of thoughts: Deliberate problem solving with large language models

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Llamafactory: Unified efficient fine-tuning of 100+ language models

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Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. 2024 · 2024

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