Quantifying how meaning propagates through communicative exchanges remains underdeveloped in computational linguistics. Here we introduce an information-theoretic framework that quantifies the directed flow of semantic content between interlocutors and decomposes multi-source contributions into redundant, unique, and synergistic components.
arXiv:2408.14575v5 Announce Type: replace Abstract: EVINCE (Entropy and Variation IN Conditional Exchanges) is a novel framework for optimizing multi-LLM dialogues using conditional statistics and in...
arXiv:2609.39225v1 Announce Type: new Abstract: Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations. Whil...
The paper introduces a structured framework for claim-level discourse analysis in dense health narratives, addressing the limitations of existing topic- or sentiment-based representations. It identifies an average of 13.22 atomic claims per minute in social media health videos and proposes tuples that link each claim to thematic aspects, stance, and multidimensional pragmatic attributes. A benchmark of 1,191 manually annotated claims from 60 videos across four health domains is created, and experiments show that large language models perform well on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, indicating a need for task-specific inference strategies.
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.
arXiv:2606. 12332v1 Announce Type: cross Abstract: Evaluating multi-turn dialogue is challenging because quality emerges across turns rather than within individual responses.
The paper introduces the Persuasion Index (PI), a taxonomy of 15 persuasion dimensions grounded in psychological and communication theories, implemented with 55 lexicon- and rule-based sub-features. PI is modular, allowing individual detectors to be swapped while preserving its theoretical framework. Evaluations on four English argumentative datasets show that PI provides a shared, lightweight feature space that captures meaningful predictive signals and reveals consistent dimension-level associations with persuasion outcomes, with variations across topics and stances.
arXiv:2606. 05330v1 Announce Type: cross Abstract: Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change.
The paper investigates how the way users phrase advice‑seeking requests—termed articulation—creates stable, measurable patterns distinct from the topics of the requests. By analyzing 16,447 prompts from public chat corpora, the authors identify a small set of latent articulation factors that consistently appear across datasets and splits. One key finding is a long‑form, information‑poor style that leads language models to give shorter, vaguer answers without seeking clarification, a pattern that persists across topics and prompt lengths.
The paper introduces ContraTalk, a benchmark that tests whether dialogue models truly use acoustic cues or rely on transcript shortcuts. It formalizes cross‑modal disagreement, creates conflict and consistent QA examples, and proposes an Audio Twin representation to expose acoustic evidence to models. Experiments show that while text‑only LLMs perform well on consistent cases, they falter on conflict cases, and AudioLLMs only partially mitigate this issue.
arXiv:2607. 14769v1 Announce Type: cross Abstract: Existing text summarization research has focused much on monologic information (e.
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.
arXiv:2608. 14630v1 Announce Type: cross Abstract: Human decision-making is often shaped by a range of well-documented cognitive biases.