Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of
large language models (LLMs), establishing itself as a primary approach to
solving complex reasoning tasks. Existing CoT synthesis approaches usually
focus on simpler reasoning tasks and thus result in low-quality and
inconsistent CoT prompts. In response to this challenge, we present an
empirical investigation of CoT prompting and introduce CoTGenius, a novel
framework designed for the automatic generation of superior CoT prompts.
CoTGenius is developed based on three major evolution strategies, i.e.,
complicate, diversify, and specify-alongside two filtering mechanisms:
evolutionary success judgement and correctness verification. We further employ
CoTGenius to create an extensive CoT dataset, and subsequently fine-tune the
Llama 2-Chat 7B and 13B models on this dataset. We call the resulting model
ChainLM. To deal with the cumulative error issue in reasoning steps, we propose
a step-level debating method, wherein multiple debaters discuss each reasoning
step to arrive at the correct answer. Extensive experiments demonstrate that
our ChainLM models exhibit enhanced proficiency in addressing a spectrum of
complex reasoning problems compared to existing models. In addition, we conduct
an in-depth analysis of the impact of data categories within CoTGenius on the
model performance. We release our dataset and code at
this https URL

Chain-of-Thought 提示可以增强大型语言模型（LLMs）的推理能力，现有的 CoT 综合方法针对简单推理任务，导致 CoT 提示的质量低且不一致。为了应对这一挑战，我们进行了 CoT 提示的实证研究并引入了 CoTGenius，一个用于自动生成优质 CoT 提示的新框架。我们进一步采用 CoTGenius 创建了一个广泛的 CoT 数据集，并在该数据集上对 Llama 2-Chat 7B 和 13B 模型进行了微调，得到了 ChainLM 模型。为了解决推理步骤中的累积误差问题，我们提出了一种步骤级辩论方法，其中多个辩论者讨论每个推理步骤，得出正确答案。大量实验证明，与现有模型相比，我们的 ChainLM 模型在解决一系列复杂推理问题方面表现出更高的熟练度。此外，我们对 CoTGenius 中的数据类别对模型性能的影响进行了深入分析。我们在此 https URL 上发布了我们的数据集和代码。

ChainLM: 通过改进思维连贯性促进大型语言模型的能力

ChainLM: Empowering Large Language Models with Improved Chain-of-Thought  Prompting

Llama 2-Chat is a collection of large language models that Meta developed and
released to the public. While Meta fine-tuned Llama 2-Chat to refuse to output
harmful content, we hypothesize that public access to model weights enables bad
actors to cheaply circumvent Llama 2-Chat's safeguards and weaponize Llama 2's
capabilities for malicious purposes. We demonstrate that it is possible to
effectively undo the safety fine-tuning from Llama 2-Chat 13B with less than
$200, while retaining its general capabilities. Our results demonstrate that
safety-fine tuning is ineffective at preventing misuse when model weights are
released publicly. Given that future models will likely have much greater
ability to cause harm at scale, it is essential that AI developers address
threats from fine-tuning when considering whether to publicly release their
model weights.

Llama 2-Chat 的模型权重的公开释放可能导致忽略其安全调整，从而使其能力被恶意利用，并且为了预防未来模型造成的伤害，AI 开发人员应该解决模型权重公开释放带来的威胁。