Opinion pieces often represent only one side of any story, which can
influence users and make them susceptible to confirmation bias and echo
chambers in society. Moreover, humans are also bad at reading long articles --
often indulging in idle reading and re-reading. To solve this, we design
ArguMentor, an end-to-end system that highlights claims in opinion pieces,
generates counter-arguments for them using an LLM, and generates a
context-based summary of the passage based on current events. It further
enhances user interaction and understanding through additional features like
Q&A bot, DebateMe and highlighting trigger windows. Our survey and results show
that users can generate more counterarguments and on an average have more
neutralized views after engaging with the system.

ArguMentor 设计了一个全套系统，它突出了意见文章中的论点，并利用 LLM 为其生成反驳观点，然后根据当前事件为文章生成基于上下文的摘要。通过额外的功能如问答机器人，辩论和突出触发窗口，该系统进一步增强了用户的交互和理解。我们的调查和结果显示，用户在使用该系统后能够产生更多的反驳观点，并且平均具有更多中立的观点。

ArguMentor: 提升用户体验与对立观点

ArguMentor: Augmenting User Experiences with Counter-Perspectives

We present a novel dataset for the controlled composition of counterarguments
designed for further applications in argument refining, mining, and evaluation.
Our dataset constitutes enriched counter-arguments to posts in the Reddit
ChangeMyView dataset that are integrated with evidence retrieved from
high-quality sources and generated based on user preferences, adjusting the
critical attributes of evidence and argument style. The resultant Counterfire
corpus comprises arguments generated from GPT-3.5 turbo, Koala, and PaLM 2
models and two of their finetuned variants (N = 32,000). Model evaluation
indicates strong paraphrasing abilities with evidence, albeit limited word
overlap, while demonstrating high style integration (0.9682 for 'reciprocity'),
showing the ability of LLM to assimilate diverse styles. Of all models, GPT-3.5
turbo showed the highest scores in argument quality evaluation, showing
consistent accuracy (score >0.8). In further analyses, reciprocity-style
counterarguments display higher counts in most categories, possibly indicating
a more creatively persuasive use of evidence. In contrast, human-written
counterarguments exhibited greater argumentative richness and diversity across
categories. Despite human-written arguments being favored as the most
persuasive in human evaluation, the 'No Style' generated text surprisingly
exhibited the highest score, prompting further exploration and investigation on
the trade-offs in generation for facts and style.

我们提出了一个新颖的数据集，用于控制组成反驳论点，旨在进一步应用于论证的改进、挖掘和评估。

审计反火：用证据和风格评估先进的反驳生成

Auditing Counterfire: Evaluating Advanced Counterargument Generation  with Evidence and Style

Argumentation is a promising model for reasoning with uncertain knowledge.
The key concept of acceptability enables to differentiate arguments and
counterarguments: The certainty of a proposition can then be evaluated through
the most acceptable arguments for that proposition. In this paper, we
investigate different complementary points of view: - an acceptability based on
the existence of direct counterarguments, - an acceptability based on the
existence of defenders. Pursuing previous work on preference-based
argumentation principles, we enforce both points of view by taking into account
preference orderings for comparing arguments. Our approach is illustrated in
the context of reasoning with stratified knowldge bases.

本文研究了基于认可度模型的不确定知识推理，通过评估命题的最可接受论点来评估命题的确定性，并探讨了基于直接反论和存在捍卫者两种认可度计算方法，并采用优先级比较方法，构建了支持这两种认可度计算方法的理论框架， 并在分层知识库推理方面进行了实证研究。