We explore how iterative revising a chain of thoughts with the help of
information retrieval significantly improves large language models' reasoning
and generation ability in long-horizon generation tasks, while hugely
mitigating hallucination. In particular, the proposed method --
*retrieval-augmented thoughts* (RAT) -- revises each thought step one by one
with retrieved information relevant to the task query, the current and the past
thought steps, after the initial zero-shot CoT is generated. Applying RAT to
GPT-3.5, GPT-4, and CodeLLaMA-7b substantially improves their performances on
various long-horizon generation tasks; on average of relatively increasing
rating scores by 13.63% on code generation, 16.96% on mathematical reasoning,
19.2% on creative writing, and 42.78% on embodied task planning. The demo page
can be found at this https URL

通过信息检索迭代修订思维链能够显著提高大型语言模型在长期生成任务中的推理和生成能力，同时极大地减少错误生成。

RAT: 检索增强思维在长远生成中引发上下文感知推理

RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in  Long-Horizon Generation

Face plays an important role in human's visual perception, and reconstructing
perceived faces from brain activities is challenging because of its difficulty
in extracting high-level features and maintaining consistency of multiple face
attributes, such as expression, identity, gender, etc. In this study, we
proposed a novel reconstruction framework, which we called Double-Flow GAN,
that can enhance the capability of discriminator and handle imbalances in
images from certain domains that are too easy for generators. We also designed
a pretraining process that uses features extracted from images as conditions
for making it possible to pretrain the conditional reconstruction model from
fMRI in a larger pure image dataset. Moreover, we developed a simple pretrained
model to perform fMRI alignment to alleviate the problem of cross-subject
reconstruction due to the variations of brain structure among different
subjects. We conducted experiments by using our proposed method and
state-of-the-art reconstruction models. Our results demonstrated that our
method showed significant reconstruction performance, outperformed the previous
reconstruction models, and exhibited a good generation ability.

本研究提出了一种名为 Double-Flow GAN 的新型重构框架，通过增强判别器的能力和处理在某些领域中图像的不平衡性，提高了从脑活动中重建知觉面孔的能力，并通过使用从图像中提取的特征作为条件进行预训练，解决了在更大的纯图像数据集中从 fMRI 预训练条件重构模型的问题。此外，还开发了一个简单的预训练模型来执行 fMRI 对齐，缓解了由于不同受试者之间的脑结构变化而导致的跨被试重建问题。通过采用我们提出的方法和最先进的重建模型进行实验证明，我们的方法显示出显著的重建性能，超越了先前的重建模型，并展现了良好的生成能力。