Identifying user's opinions and stances in long conversation threads on
various topics can be extremely critical for enhanced personalization, market
research, political campaigns, customer service, conflict resolution, targeted
advertising, and content moderation. Hence, training language models to
automate this task is critical. However, to train such models, gathering manual
annotations has multiple challenges: 1) It is time-consuming and costly; 2)
Conversation threads could be very long, increasing chances of noisy
annotations; and 3) Interpreting instances where a user changes their opinion
within a conversation is difficult because often such transitions are subtle
and not expressed explicitly. Inspired by the recent success of large language
models (LLMs) for complex natural language processing (NLP) tasks, we leverage
Mistral Large and GPT-4 to automate the human annotation process on the
following two tasks while also providing reasoning: i) User Stance
classification, which involves labeling a user's stance of a post in a
conversation on a five-point scale; ii) User Dogmatism classification, which
deals with labeling a user's overall opinion in the conversation on a
four-point scale. The majority voting on zero-shot, one-shot, and few-shot
annotations from these two LLMs on 764 multi-user Reddit conversations helps us
curate the USDC dataset. USDC is then used to finetune and instruction-tune
multiple deployable small language models for the 5-class stance and 4-class
dogmatism classification tasks. We make the code and dataset publicly available
[this https URL].

通过利用大型语言模型 (LLMs) 自动化人工注释过程，我们训练了多个可部署的小型语言模型来进行用户态度和教条主义的分类任务。

USDC：长对话中的用户立场和教条主义的数据集

USDC: A Dataset of $\underline{U}$ser $\underline{S}$tance and  $\underline{D}$ogmatism in Long $\underline{C}$onversations

Generative Artificial Intelligence (AI) is a cutting-edge technology capable
of producing text, images, and various media content leveraging generative
models and user prompts. Between 2022 and 2023, generative AI surged in
popularity with a plethora of applications spanning from AI-powered movies to
chatbots. In this paper, we delve into the potential of generative AI within
the realm of the World Wide Web, specifically focusing on image generation. Web
developers already harness generative AI to help crafting text and images,
while Web browsers might use it in the future to locally generate images for
tasks like repairing broken webpages, conserving bandwidth, and enhancing
privacy. To explore this research area, we have developed WebDiffusion, a tool
that allows to simulate a Web powered by stable diffusion, a popular
text-to-image model, from both a client and server perspective. WebDiffusion
further supports crowdsourcing of user opinions, which we use to evaluate the
quality and accuracy of 409 AI-generated images sourced from 60 webpages. Our
findings suggest that generative AI is already capable of producing pertinent
and high-quality Web images, even without requiring Web designers to manually
input prompts, just by leveraging contextual information available within the
webpages. However, we acknowledge that direct in-browser image generation
remains a challenge, as only highly powerful GPUs, such as the A40 and A100,
can (partially) compete with classic image downloads. Nevertheless, this
approach could be valuable for a subset of the images, for example when fixing
broken webpages or handling highly private content.

通过研究发现，生成人工智能已经可以利用网页上的上下文信息实现生成相关和高质量的网页图像，而无需手动输入提示，这一方法对于修复破损的网页或处理高度私密内容可能非常有价值。

探索生成人工智能在万维网的潜力

Exploring the Potential of Generative AI for the World Wide Web

User opinions expressed in the form of ratings can influence an individual's
view of an item. However, the true quality of an item is often obfuscated by
user biases, and it is not obvious from the observed ratings the importance
different users place on different aspects of an item. We propose a
probabilistic modeling of the observed aspect ratings to infer (i) each user's
aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect
ratings as ordered discrete data and encode the dependency between different
aspects by using a latent Gaussian structure. We handle the
Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled
with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully
Bayesian inference. On two real world datasets, we demonstrate the predictive
ability of our model and its effectiveness in learning explainable user biases
to provide insights towards a more reliable product quality estimation.

通过概率建模来推断用户对产品不同方面的偏好并揭示产品内在质量，在实际数据集上展示了该方法的预测能力和学习可解释用户偏好的有效性，进而提高可靠的产品质量评估。