The study investigates how large language models encode rhetorical questions by applying linear probes to two social‑media datasets. It finds that rhetorical signals appear early in the model’s representations, are most stable in last‑token embeddings, and can be distinguished from information‑seeking questions with AUROC 0.7–0.8 even across datasets. However, probes trained on different datasets rank target instances differently, revealing that multiple, distinct linear directions capture various rhetorical cues rather than a single shared representation.
By Louie Hong Yao, Vishesh Anand, Yuan Zhuang, Tianyu Jiang
arXiv:2606. 08076v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
By Esra D\"onmez, Agnieszka Falenska
arXiv:2606. 08076v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
By Esra D\"onmez, Agnieszka Falenska
The paper compares two common ways of evaluating large language models (LLMs): prompting them to answer questions directly and scoring candidate answers using likelihood-based metrics. The authors introduce a new protocol that ranks declarative statements derived from question–answer pairs, and test it across 95 decoder-only models (0.1B–104B parameters) on 10 multiple-choice QA datasets. They find that while prompted answering accuracy improves sharply with model scale and instruction tuning, statement‑likelihood ranking accuracy stays relatively stable, indicating that the two evaluation methods probe different aspects of model behavior.
By Alessandro Bondielli, Lucia Passaro, Davide Bacciu, Alessandro Lenci
arXiv:2602. 23971v4 Announce Type: replace-cross Abstract: Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an alignment failure, particularly in high-stakes advisory and social contexts.
By Magda Dubois, Cozmin Ududec, Christopher Summerfield, Lennart Luettgau
The paper introduces “Persuasio”, a multi‑agent dialogue platform that uses a formal argumentation theory to adjudicate winners in free‑text debates. Using this system, the authors generated 192 debates on a UK political topic involving humans and large language models (LLMs), and evaluated 22 interlocutors through automated adjudication and 9,702 crowdsourced pairwise judgments across 1,386 annotation instances. The results show a consistent decoupling between subjective persuasiveness—where LLMs dominate—and formal argumentative strength—where humans remain competitive, with multi‑agent and retrieval‑augmented variants widening this gap.
By Jordan Robinson, Angus R. Williams, Katie Atkinson, Anthony G. Cohn
arXiv:2606. 01736v1 Announce Type: cross Abstract: As LLMs are increasingly used to draft public-facing arguments, they may flatten public debate by repeatedly introducing the same polished, plausible arguments.
By Yekyung Kim, Yapei Chang, Chau Minh Pham, Mohit Iyyer
The paper introduces the concept of epistemic diversity for large language models (LLMs), defining it as the range of valid answers, explanations, and reasoning routes that an LLM presents to users. It argues that evaluating LLMs solely on accuracy or alignment is insufficient, especially when LLMs are used for knowledge-intensive tasks. The authors propose a preliminary framework for measuring epistemic diversity and demonstrate that leading LLMs often collapse large valid answer spaces into small canonical subsets, indicating epistemic narrowness.
By Elisabeth Kirsten, Nicole Kr\"amer, Muhammad Bilal Zafar
arXiv:2606. 05256v1 Announce Type: new Abstract: This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView.
By Kokil Jaidka, Saifuddin Ahmed
arXiv:2608.29803v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs...
By Lin Chen, Yitong Chen, Yong Li
arXiv:2608. 08445v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to ground their outputs in external knowledge.
By Xiaoyan Zhao, Yujie Cai, Yang Zhang, Grace Hui Yang, Tat-Seng Chua
arXiv:2606. 13397v1 Announce Type: cross Abstract: Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online.
By Dipto Das, Achhiya Sultana, Ankit Singh Chauhan, Saadia Binte Alam, Mohammad Shidujaman, Shion Guha, Sunandan Chakraborty, Syed Ishtiaque Ahmed