Large Language Models (LLMs) have achieved remarkable performance in
objective tasks such as open-domain question answering and mathematical
reasoning, which can often be solved through recalling learned factual
knowledge or chain-of-thought style reasoning. However, we find that the
performance of LLMs in subjective tasks is still unsatisfactory, such as
metaphor recognition, dark humor detection, etc. Compared to objective tasks,
subjective tasks focus more on interpretation or emotional response rather than
a universally accepted reasoning pathway. Based on the characteristics of the
tasks and the strong dialogue-generation capabilities of LLMs, we propose RiC
(Reasoning in Conversation), a method that focuses on solving subjective tasks
through dialogue simulation. The motivation of RiC is to mine useful contextual
information by simulating dialogues instead of supplying chain-of-thought style
rationales, thereby offering potential useful knowledge behind dialogues for
giving the final answers. We evaluate both API-based and open-source LLMs
including GPT-4, ChatGPT, and OpenChat across twelve tasks. Experimental
results show that RiC can yield significant improvement compared with various
baselines.

通过对话模拟，我们提出了 RiC (Reasoning in Conversation) 方法，用于解决主观任务，并提供了实验证明 RiC 可以显著改善大型语言模型在此类任务中的表现。

对话中的推理：通过大型语言模型的模拟解决主观任务

Reasoning in Conversation: Solving Subjective Tasks through Dialogue  Simulation for Large Language Models

Open Radio Access Network (O-RAN) is an emerging paradigm, whereby
virtualized network infrastructure elements from different vendors communicate
via open, standardized interfaces. A key element therein is the RAN Intelligent
Controller (RIC), an Artificial Intelligence (AI)-based controller.
Traditionally, all data available in the network has been used to train a
single AI model to be used at the RIC. This paper introduces, discusses, and
evaluates the creation of multiple AI model instances at different RICs,
leveraging information from some (or all) locations for their training. This
brings about a flexible relationship between gNBs, the AI models used to
control them, and the data such models are trained with. Experiments with
real-world traces show how using multiple AI model instances that choose
training data from specific locations improve the performance of traditional
approaches following the hoarding strategy.

本文论述了利用来自不同地点的信息来训练多个 AI 模型实例以实现更灵活的 gNB 控制模式，从而提升了传统方法的性能。