The study explores the effectiveness of the Chain-of-Thought approach, known
for its proficiency in language tasks by breaking them down into sub-tasks and
intermediate steps, in improving vision-language tasks that demand
sophisticated perception and reasoning. We present the "Description then
Decision" strategy, which is inspired by how humans process signals. This
strategy significantly improves probing task performance by 50%, establishing
the groundwork for future research on reasoning paradigms in complex
vision-language tasks.

该研究通过将复杂的视觉语言任务拆分为子任务和中间步骤的思维链方法，探究其在提高需要复杂感知和推理的视觉语言任务中的有效性。我们提出了 “先描述再决策” 的策略，该策略受人类信号处理方式启发，显著提高探索任务性能 50%，为进一步研究复杂视觉语言任务中的推理范式奠定了基础。

复杂视觉语言推理任务中的思维链路作用

The Role of Chain-of-Thought in Complex Vision-Language Reasoning Task

Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application.

我们提出了《Algorithm of Thoughts》，这是一种新的通过算法推动大型语言模型的算法推理路径的策略，以在上下文学习中扩展其思路探索，并超越了早期单查询方法和最近的多查询策略。

思维算法：增强大型语言模型中的想法探索

Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language  Models

Stance detection predicts attitudes towards targets in texts and has gained
attention with the rise of social media. Traditional approaches include
conventional machine learning, early deep neural networks, and pre-trained
fine-tuning models. However, with the evolution of very large pre-trained
language models (VLPLMs) like ChatGPT (GPT-3.5), traditional methods face
deployment challenges. The parameter-free Chain-of-Thought (CoT) approach, not
requiring backpropagation training, has emerged as a promising alternative.
This paper examines CoT's effectiveness in stance detection tasks,
demonstrating its superior accuracy and discussing associated challenges.

本文研究了 Chain-of-Thought 方法在 stance detection 任务中的有效性，并展示了其卓越的准确性和相关挑战。