论文

研究发现，在上述多个维度上进行的指令微调可显著提升多种模型类别（PaLM、T5、U-PaLM）、多种提示设定以及多种评测基准（MMLU、BBH、TyDiQA、MGSM、开放式生成）上的表现。It is found that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups, and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).

本文进行了迄今为止对 Code LLMs 最全面的评估，结果显示 StarCoderBase 在支持多编程语言的开放 Code LLMs 中表现最优，并且能够匹敌或超越 OpenAI code-cushman-001 模型。This work performs the most comprehensive evaluation of Code LLMs to date and shows that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model.

本文显著推进了针对已对齐语言模型的对抗攻击 SOTA，并提出了关于如何防止此类系统生成不良信息的重要问题。This work significantly advances the state-of-the-art in adversarial attacks against aligned language models, raising important questions about how such systems can be prevented from producing objectionable information.

实证研究表明 AutoGen 框架在多个示例应用中有效，应用领域涵盖数学、编码、问答、运筹学、在线决策、娱乐等。Empirical studies demonstrate the effectiveness of the AutoGen framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.

PaLM 2 是一个新的 SOTA 语言模型，相比其前身 PaLM 具有更强的多语言和推理能力，并具备更高的计算效率，能够在不增加额外开销或影响其他能力的前提下在推理时控制输出毒性。PaLM 2 is a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM and enables inference-time control over toxicity without additional overhead or impact on other capabilities.

名为 PaLM 的 540 亿参数、密集激活的 Transformer 语言模型取得了突破性性能，在一系列多步推理任务上超越了微调后的 SOTA，并在最近发布的 BIG-bench 基准上超越了人类平均水平。A 540-billion parameter, densely activated, Transformer language model, which is called PaLM achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.

采用迭代的在线训练模式，按周节奏用新的人类反馈数据更新偏好模型与 RL 策略，并发现 RL 奖励与策略相对其初始化的 KL 散度平方根之间近似呈线性关系。An iterated online mode of training, where preference models and RL policies are updated on a weekly cadence with fresh human feedback data, and a roughly linear relation between the RL reward and the square root of the KL divergence between the policy and its initialization is identified.

研究表明，APE 生成的提示词既可引导模型趋向真实性和/或信息量，也可通过将其前置拼接到标准上下文学习提示词之前来提升少样本学习性能。It is shown that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts.

本文提出 Atlas，一个经过精心设计并预训练的检索增强大语言模型，能以极少训练样例学习知识密集型任务，并研究了文档索引内容的影响，表明该索引可便捷地更新。This work presents Atlas, a carefully designed and pre-trained retrieval augmented language model able to learn knowledge intensive tasks with very few training examples, and studies the impact of the content of the document index, showing that it can easily be updated.