Argumentation and Persuasion

Source papers 109

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

• (QA)2: Question Answering with Questionable Assumptions
  For instance, the question When did Marie Curie discover Uranium? cannot be answered as a typical when question without addressing the false assumption Marie Curie discovered Uranium. In this work, we…
• A Hybrid Human-AI Approach for Argument Map Creation From Transcripts
  Deliberation processes are important mechanisms for collaborative decision-making, fostering informed choices across a wide array of domains (Vaculín et al., 2013; Owen, 2015). Traditionally, these pr…
• A Hybrid Intelligence Method for Argument Mining
  Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly a…
• A Robustness Evaluation Framework for Argument Mining
  Standard practice for evaluating the performance of machine learning models for argument mining is to report different metrics such as accuracy or F1. However, little is usually known about the model’…
• A meta-analysis of the persuasive power of large language models
  Large language models (LLMs) are increasingly used for persuasion, such as in political communication and marketing, where they affect how people think, choose, and act. Yet, empirical findings on the…
• AI Argues Differently: Distinct Argumentative and Linguistic Patterns of LLMs in Persuasive Contexts
  Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, particularly in high-stake settings such as persuasive online discourse. While recent work focuses on …
• AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
  For Large Language Models (LLMs) to be reliably deployed in both everyday and high-stakes domains, knowing when not to answer is equally critical as answering correctly. Real-world user queries, which…
• Argument Quality Assessment in the Age of Instruction-Following Large Language Models
  Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve arg…
• Argument Summarization and its Evaluation in the Era of Large Language Models
  Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining (AM). This paper investig…
• Argumentative Large Language Models for Explainable and Contestable Decision-Making
  The diversity of knowledge encoded in large language models (LLMs) and their ability to apply this knowledge zero-shot in a range of settings makes them a promising candidate for use in decisionmaking…
• Argunauts: Open LLMs that Master Argument Analysis with Argdown
  [Community Article](https://huggingface.co/blog/community)Published February 14, 2025 [](https://huggingfac…
• Attention, Intentions, And The Structure Of Discourse
  In this paper we explore a new theory of discourse structure that stresses the role of purpose and processing in discourse. In this theory, discourse structure is composed of three separate but interr…
• Attentive Reasoning Queries: A Systematic Method for Optimizing Instruction-Following in Large Language Models
  We present Attentive Reasoning Queries (ARQs), a novel structured reasoning approach that significantly improves instruction-following in Large Language Models through domain-specialized reasoning blu…
• Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
  Large language models (LLMs) have recently shown impressive performance on tasks involving reasoning, leading to a lively debate on whether these models possess reasoning capabilities similar to human…
• Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate
  Abstract: Large Language Models (LLMs) have demonstrated significant capabilities in understanding and generating human language, contributing to more natural interactions with complex systems. Howeve…
• Can AI Explanations Make You Change Your Mind?
  In the context of AI-based decision support systems, explanations can help users to judge when to trust the AI’s suggestion, and when to question it. In this way, human oversight can prevent AI errors…
• Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions
  Communication among humans relies on conversational grounding, allowing interlocutors to reach mutual understanding even when they do not have perfect knowledge and must resolve discrepancies in each …
• Can Language Models Recognize Convincing Arguments?
  propose tasks measuring LLMs’ ability to (1) distinguish between strong and weak arguments, (2) predict stances based on beliefs and demographic characteristics, and (3) determine the appeal of an arg…
• Can Large Language Models Capture Human Annotator Disagreements?
  Human annotation variation (i.e., annotation disagreements) is common in NLP and often reflects important information such as task subjectivity and sample ambiguity. While Large Language Models (LLMs)…
• Can Large Language Models Understand Argument Schemes?
  Argument schemes represent stereotypical patterns of reasoning that occur in everyday arguments. However, despite their usefulness, argument scheme classification — that is, classifying natural langua…
• Can Large Language Models do Analytical Reasoning?
  Our divide-and-conquer approach breaks down play-by-play data into smaller, more manageable segments, solves each piece individually, and then aggregates them together. Besides the divide-and-conquer …
• Can Large Language Models perform Relation-based Argument Mining?
  The general AM problem can be split into three main tasks: 1) argument identification, involving segmenting text into units and determining which are argumentative; 2) identification of argumentative …
• Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
  Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) …
• Chain of Stance: Stance Detection with Large Language Models
  Stance detection is an active task in natural language processing (NLP) that aims to identify the author’s stance towards a particular target within a text. Given the remarkable language understanding…
• ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
  Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given th…
• Complex Logical Instruction Generation
  Instruction following has catalyzed the recent era of Large Language Models (LLMs) and is the foundational skill underpinning more advanced capabilities such as reasoning and agentic behaviors. As tas…
• Computational Modelling of Undercuts in Real-world Arguments
  Argument Mining (AM) is the task of automatically analysing arguments, such that the unstructured information contained in them is converted into structured representations. Undercut is a unique struc…
• Constructing a Periodic Table of Arguments
  The existing classifications of arguments are unsatisfying in a number of ways. This paper proposes an alternative in the form of a Periodic Table of Arguments. The newly developed table can be used a…
• Critical-Questions-of-Thought: Steering LLM reasoning with Argumentative Querying
  Studies have underscored how, regardless of the recent breakthrough and swift advances in AI research, even state-of-the-art Large Language models (LLMs) continue to struggle when performing logical a…
• DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations
  Those models take a contrastive learning approach, where they build binary classifiers to differentiate positive, or coherent examples from negative, or incoherent dialogues. Those classifiers are usu…
• DEEM: Dynamic Experienced Expert Modeling for Stance Detection
  Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results. However, considering that stance detection usually require…
• DR-HAI: Argumentation-based Dialectical Reconciliation in Human-AI Interactions
  “The significance of creating AI agents that can establish trust and accountability by interacting with human users is constantly increasing. This is the central idea behind the field of explainable A…
• Debating with More Persuasive LLMs Leads to More Truthful Answers
  Common methods for aligning large language models (LLMs) with desired behaviour heavily rely on human-labelled data. However, as models grow increasingly sophisticated, they will surpass human experti…
• DeepCT-enhanced Lexical Argument Retrieval
  The recent Touché lab’s argument retrieval task focuses on controversial topics like ‘Should bottled water be banned?’ and asks to retrieve relevant pro/con arguments. Interestingly, the most effectiv…
• Determinants of LLM-assisted Decision-Making
  Decision-making is a fundamental capability in everyday life. Large Language Models (LLMs) provide multifaceted support in enhancing human decision-making processes. However, understanding the influen…
• Do LLMs Truly Understand When a Precedent Is Overruled?
  Large language models (LLMs) with extended context windows show promise for complex legal reasoning tasks, yet their ability to understand long legal documents remains insufficiently evaluated. Develo…
• Durably reducing conspiracy beliefs through dialogues with AI
  “Conspiracy theories are a paradigmatic example of beliefs that, once adopted, are extremely difficult to dispel. Influential psychological theories propose that conspiracy beliefs are uniquely resist…
• EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory
  EVINCE (Entropy and Variation IN Conditional Exchanges) is a novel framework for optimizing multi-LLM dialogues using conditional statistics and information theory. It addresses limitations in multi-a…
• Eliciting Reasoning in Language Models with Cognitive Tools
  The recent advent of reasoning models like OpenAI’s o1 was met with excited speculation by the AI community about the mechanisms underlying these capabilities in closed models, followed by a rush of r…
• Explicit Inductive Inference using Large Language Models
  However, recently McKenna et al. (2023a) has pointed out that LLMs are severely affected by an attestation bias when performing inference tasks. Given the question of whether premise P entails hypothe…
• Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates
  Argumentative Relation Classification is the task of determining the relationship between two contributions in the context of an argumentative dialogue. Existing models in the literature rely on a com…
• Exploring the Potential of Large Language Models in Computational Argumentation
  Computational argumentation has become an essential tool in various domains, including law, public policy, and artificial intelligence. It is an emerging research field in natural language processing …
• Exploring the Role of Prior Beliefs for Argument Persuasion
  Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the langu…
• Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
  Decision conferences are structured, collaborative meetings that bring together experts from various fields to address complex issues and reach a consensus on recommendations for future actions or pol…
• Fine-grained Hallucination Detection and Editing for Language Models
  Several recent work studies automatic hallucination detection (Min et al., 2023) or editing outputs (Gao et al., 2022) to address such LM hallucinations. These systems typically categorize hallucinati…
• Fine-tuning Pre-trained Language Models for Dialogical Argument Mining with Inference Anchoring Theory
  In this paper, we present our framework for DialAM-2024 Task A: Identification of Propositional Relations and Task B: Identification of Illocutionary Relations. The goal of Task A is to detect argumen…
• GenAI as a Power Persuader: How Professionals Get Persuasion Bombed When They Attempt to Validate LLMs
  When diligent professionals make decisions, they validate their analyses. Increasingly, professionals across domains use generative AI (GenAI) for analytic knowledge work and therefore validate the AI…
• How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
  Most traditional AI safety research views models as machines and centers on algorithm focused attacks developed by security experts. As large language models (LLMs) become increasingly common and comp…
• How susceptible are LLMs to Logical Fallacies?
  “This paper investigates the rational thinking capability of Large Language Models (LLMs) in multi-round argumentative debates by exploring the impact of fallacious arguments on their logical reasonin…
• HowProjective is Projective Content? Gradience in Projectivity and At-issueness
  Projective content is utterance content that a speaker may be taken to be committed to even when the expression associated with the content occurs embedded under an entailment-canceling operator (e.g.…
• Identification of Propositional and Illocutionary Relations
  In this paper we tackle the shared task DialAM- 2024 aiming to annotate dialogue based on the inference anchoring theory (IAT). The task can be split into two parts, identification of propositional re…
• Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions
  However, explanations generated via CoT are susceptible to content biases that negatively affect their robustness and faithfulness. To mitigate existing limitations, recent work has proposed the use o…
• LLM Augmentations to support Analytical Reasoning over Multiple Documents
  Our key contributions are: 1) We conduct the first investigation of the feasibility of using LLMs in intelligence analysis where both evidencebased reasoning and analytical creativity is of utmost …
• LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory
  What does it truly mean for a language model to “reason” strategically, and can scaling up alone guarantee intelligent, context-aware decisions? Strategic decision-making requires adaptive reasoning, …
• LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback
  Ensuring that online discussions are civil and productive is a major challenge for social media platforms. Such platforms usually rely both on users and on automated detection tools to flag inappropri…
• LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
  Large reasoning models (LRMs) tackle complex reasoning problems by following long chain-ofthoughts (Long CoT) that incorporate reflection, backtracking, and self-validation. However, the training tech…
• LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High
  These implicit assumptions, known as presuppositions, refer to background knowledge or shared beliefs assumed to be part of the common ground between interlocutors (Stalnaker, 1973). Presuppositions a…
• Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments
  Abstract. Large Language Models (LLMs) are already as persuasive as humans. However, we know very little about how they do it. This paper investigates the persuasion strategies of LLMs, comparing them…
• Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
  Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, LOGICLM, which integrates LLMs with sy…
• Modeling Appropriate Language in Argumentation
  Online discussion moderators must make adhoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offens…
• Modeling the Quality of Dialogical Explanations
  Abstract Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an explainer discusses a concept or phenomenon of interest with an explainee. Leaving the explainee with a…
• Multi-Agent Collaborative Intelligence: Dual-Dial Control for Reliable LLM Reasoning
  Multi-agent debate often wastes compute by using a fixed adversarial stance, aggregating without deliberation, or stopping on heuristics. We introduce MACI, an active controller with two independent d…
• On the Adaptive Psychological Persuasion of Large Language Models
  However, systematic exploration of their dual capabilities to autonomously persuade and resist persuasion, particularly in contexts involving psychological rhetoric, remains unexplored. In this paper,…
• On the Conversational Basis of Some Presuppositions
  The current literature on presupposition focuses almost exclusively on the projection problem: the question of how and why the presuppositions of atomic clauses are projected to complex sentences whic…
• Overview of DialAM-2024: Argument Mining in Natural Language Dialogues
  Argumentation is the process by which humans rationally elaborate their thoughts and opinions in written (e.g., essays) or spoken (e.g., debates) contexts. Argument Mining research, however, has been …
• Persuasive presuppositions
  A recurrent claim, coming from different approaches to pragmatics, argumentation theory and related disciplines, is that informative presuppositions have a special persuasive force. My aim in this pap…
• Premise Order Matters in Reasoning with Large Language Models
   Large language models (LLMs) have accomplished remarkable reasoning performance in various domains. However, in th…
• Presuppositions are more persuasive than assertions if addressees accommodate them: Experimental evidence for philosophical reasoning
  Best practice and descriptive research claim that presuppositions, such as the “too” in “,” increase the persuasiveness of arguments. Surprisingly, there is hardly any causal evidence for this claim. …
• Prompting Large Language Models With the Socratic Method
  Abstract—This paper presents a systematic approach to using the Socratic method in developing prompt templates that effectively interact with large language models, including GPT- 3. Various methods a…
• Propositional Interpretability in Artificial Intelligence
  David Chalmers I will argue for the importance of a special sort of interpretability, which I call propositional interpretability. This involves interpreting a system’s mechanisms and behavior in ter…
• Query Understanding in the Age of Large Language Models
  “The central problem of IR systems, also referred to as the “holy grail” of IR, is to overcome the vocabulary mismatch between the user and the system [75]. This leads to the challenge of matching the…
• RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems
  Abstract: Reasoning requires going beyond pattern matching or memorization of solutions to identify and implement “algorithmic procedures” that can be used to deduce answers to hard problems. Doing so…
• ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
  Large Language Models (LLMs) still struggle with natural language reasoning tasks. Motivated by the society of minds (Minsky, 1988), we propose RECONCILE, a multi-model multiagent framework designed a…
• Reasoning Can Hurt the Inductive Abilities of Large Language Models
  Large Language Models (LLMs) have shown remarkable progress across domains, yet their ability to perform inductive reasoning—inferring latent rules from sparse examples—remains limited. It is often as…
• Reasoning Models Are More Easily Gaslighted Than You Think
  Recent advances in reasoning-centric models promise improved robustness through mechanisms such as chain-of-thought prompting and test-time scaling. However, their ability to withstand misleading user…
• Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
  Human reasoning involves different strategies, each suited to specific problems. Prior work shows that large language model (LLMs) tend to favor a single reasoning strategy, potentially limiting their…
• Reranking-based Generation for Unbiased Perspective Summarization
  Generating unbiased summaries in real-world settings such as political perspective summarization remains a crucial application of Large Language Models (LLMs). Yet, existing evaluation frameworks rely…
• Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing
  Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argum…
• Rhetorical XAI: Explaining AI’s Benefits as well as its Use via Rhetorical Design
  Modern AI systems are notoriously opaque, limiting efforts to understand or audit their behaviors [42, 188]. In response, Explainable Artificial Intelligence (XAI) aims to foster trust and accountabil…
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval
  To address these issues, in this paper, we propose SAILER, a new Structure-Aware pre-traIned language model for LEgal case Retrieval. It is highlighted in the following three aspects: (1) SAILER fully…
• SPICE: Self-Play In Corpus Environments Improves Reasoning
  Self-improving systems require environmental interaction for continuous adaptation. We introduce SPICE (Self-Play In Corpus Environments), a reinforcement learning framework where a single model acts …
• SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLM
  Abstract—Topic discovery in scientific literature provides valuable insights for researchers to identify emerging trends and explore new avenues for investigation, facilitating easier scientific infor…
• Self-reflecting Large Language Models: A Hegelian Dialectical Approach
  Investigating NLP through a philosophical lens has recently caught researcher’s eyes as it connects computational methods with classical schools of philosophy. This paper introduces a philosophical ap…
• Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making
  Large language models (LLMs) have demonstrated strong potential in clinical question answering, with recent multi-agent frameworks further improving diagnostic accuracy via collaborative reasoning. Ho…
• Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds
  We evaluate LLMs’ language understanding capacities on simple inference tasks that most humans find trivial. Specifically, we target (i) grammatically-specified entailments, (ii) premises with evident…
• SocraSynth: Multi-LLM Reasoning with Conditional Statistics
  Large language models (LLMs), while promising, face criticisms for biases, hallucinations, and a lack of reasoning capability. This paper introduces SocraSynth, a multi-LLM agent reasoning platform de…
• Sources of Hallucination by Large Language Models on Inference Tasks
  We establish two biases originating from pretraining which predict much of their behavior, and show that these are major sources of hallucination in generative LLMs. First, memorization at the level o…
• Stance Detection on Social Media with Fine-Tuned Large Language Models
  The implementation of prompting strategies represents a significant departure from traditional NLP model training methods. By employing these strategies, LLMs can generate predictions without the exte…
• Strategic Reasoning with Language Models
  This paper introduces an approach that uses pretrained LLMs with few-shot chain-of-thought examples to enable strategic reasoning for AI agents. Our approach uses systematically generated demonstratio…
• Teaching Probabilistic Logical Reasoning to Transformers
  We propose a novel end-to-end fine-tuning approach, Probabilistic Constraint Training (PCT), that utilizes probabilistic logical rules as constraints in the fine-tuning phase without relying on these …
• The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
  Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly …
• The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
  Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-tur…
• The Levers of Political Persuasion with Conversational AI
  There are widespread fears that conversational AI could soon exert unprecedented influence over human beliefs. Here, in three large-scale experiments (N=76,977), we deployed 19 LLMs—including some pos…
• The Missing Layer of AGI: From Pattern Alchemy to Coordination Physics
  Influential critiques argue that Large Language Models (LLMs) are a dead end for AGI: “mere pattern matchers” structurally incapable of reasoning or planning. We argue this conclusion misidentifies th…
• The Model Says Walk: How Surface Heuristics Override Implicit Constraints in LLM Reasoning
  Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through a diagnose–measure–bridge–treat framework. Causal-behavior…
• The Place of Emotion in Argument
  Douglas N. Walton Abstract Appeals to emotion—pity, fear, popular sentiment, and _ad hominem_ attacks—are commonly used in argumentation. Instead of dismissing these appeals as fallacious wherever t…
• The Thin Line Between Comprehension and Persuasion in LLMs
  Large language models (LLMs) are excellent at maintaining high-level, convincing dialogue, but it remains unclear whether their persuasive success reflects genuine understanding of the discourse. We e…
• The social component of the projection behavior of clausal complement contents
  Abstract. Some accounts of presupposition projection predict that content’s consistency with the Common Ground influences whether it projects (e.g., Heim 1983; Gazdar 1979a,b). I conducted an experime…
• Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models
  Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret. Prompting techniques like C…
• Thinking LLMs: General Instruction Following with Thought Generation
  in the standard alignment framework they lack the basic ability of explicit thinking before answering. Thinking is important for complex questions that require reasoning and planning – but can be appl…
• Thought Anchors: Which LLM Reasoning Steps Matter?
  We argue that analyzing reasoning traces at the sentence level is a promising approach to understanding reasoning processes. We present three complementary attribution methods: (1) a black-box method …
• Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design
  we advocate for the development of conversational technology that is inherently designed to support and facilitate argumentative processes. We argue that, at present, large language models (LLMs) are …
• Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers
  Representing discourse as argument graphs facilitates robust analysis. Although computational frameworks for constructing graphs from monologues exist, there is a lack of frameworks for parsing dialog…
• Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy
  The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of so…
• Unlocking Varied Perspectives: A Persona-Based Multi-Agent Framework with Debate-Driven Text Planning for Argument Generation
  Writing persuasive arguments is a challenging task for both humans and machines. It entails incorporating high-level beliefs from various perspectives on the topic, along with deliberate reasoning and…
• Using LLMs to Discover Legal Factors
  Abstract. Factors are a foundational component of legal analysis and computational models of legal reasoning. These factor-based representations enable lawyers, judges, and AI and Law researchers to r…
• Virtuous Machines: Towards Artificial General Science
  Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow doma…
• When Large Language Models are More Persuasive Than Incentivized Humans, and Why
  Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (Claude 3.5 Sonnet an…
• Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards
  Reinforcement learning with verifiable rewards (RLVR) has facilitated significant advances in large language models (LLMs), particularly for reasoning tasks with objective, ground-truth answers, such …