Conversational tones -- the manners and attitudes in which speakers
communicate -- are essential to effective communication. Amidst the increasing
popularization of Large Language Models (LLMs) over recent years, it becomes
necessary to characterize the divergences in their conversational tones
relative to humans. However, existing investigations of conversational
modalities rely on pre-existing taxonomies or text corpora, which suffer from
experimenter bias and may not be representative of real-world distributions for
the studies' psycholinguistic domains. Inspired by methods from cognitive
science, we propose an iterative method for simultaneously eliciting
conversational tones and sentences, where participants alternate between two
tasks: (1) one participant identifies the tone of a given sentence and (2) a
different participant generates a sentence based on that tone. We run 100
iterations of this process with human participants and GPT-4, then obtain a
dataset of sentences and frequent conversational tones. In an additional
experiment, humans and GPT-4 annotated all sentences with all tones. With data
from 1,339 human participants, 33,370 human judgments, and 29,900 GPT-4
queries, we show how our approach can be used to create an interpretable
geometric representation of relations between conversational tones in humans
and GPT-4. This work demonstrates how combining ideas from machine learning and
cognitive science can address challenges in human-computer interactions.

运用机器学习和认知科学的思想相结合的方法，通过迭代的方式，从人们和 GPT-4 中获取了一组句子和频繁对话的语气，进而创造并解释了人们和 GPT-4 之间对话语气的关系的可解释的几何表示形式，从而解决了人机交互中的挑战。

通过与人员进行抽样，揭示人类和语言模型之间对话语调的相似性和差异性

Characterizing Similarities and Divergences in Conversational Tones in  Humans and LLMs by Sampling with People

We evaluate the use of the open-source Llama-2 model for generating
well-known, high-performance computing kernels (e.g., AXPY, GEMV, GEMM) on
different parallel programming models and languages (e.g., C++: OpenMP, OpenMP
Offload, OpenACC, CUDA, HIP; Fortran: OpenMP, OpenMP Offload, OpenACC; Python:
numpy, Numba, pyCUDA, cuPy; and Julia: Threads, CUDA.jl, AMDGPU.jl). We built
upon our previous work that is based on the OpenAI Codex, which is a descendant
of GPT-3, to generate similar kernels with simple prompts via GitHub Copilot.
Our goal is to compare the accuracy of Llama-2 and our original GPT-3 baseline
by using a similar metric. Llama-2 has a simplified model that shows
competitive or even superior accuracy. We also report on the differences
between these foundational large language models as generative AI continues to
redefine human-computer interactions. Overall, Copilot generates codes that are
more reliable but less optimized, whereas codes generated by Llama-2 are less
reliable but more optimized when correct.

我们评估了开源 Llama-2 模型在不同并行编程模型和语言上生成众所周知的高性能计算内核（例如 AXPY、GEMV、GEMM）的使用情况。我们使用简单的提示通过 GitHub Copilot 基于 OpenAI Codex 生成类似的内核，以比较 Llama-2 和我们原始的 GPT-3 基准的准确性，并报告了基于这些基础的大型语言模型的区别，因为生成式人工智能继续重新定义人机交互。总体而言，Copilot 生成的代码更可靠但不太优化，而 Llama-2 生成的代码在正确时更优化但不太可靠。