Towards Strategic Persuasion with Language Models
Overview
Overall Novelty Assessment
The paper contributes a theory-driven framework grounded in Bayesian Persuasion for measuring LLM persuasive capabilities, environments for evaluating strategic persuasion, and a reinforcement learning approach for training persuaders. It resides in the 'Reinforcement Learning for Strategic Persuasion' leaf under 'Training Approaches for Persuasive Language Models', which contains only two papers total. This represents a relatively sparse research direction within the broader taxonomy of fifty papers, suggesting the specific combination of Bayesian Persuasion theory with RL-based training for strategic influence is not yet heavily explored.
The taxonomy reveals neighboring work in adjacent leaves: 'Persuasive Dataset Construction and Generation' focuses on creating training data through multi-LLM communication, while 'Supervised Training and Fine-Tuning Methods' employs prompt engineering and supervised learning. The sibling paper in the same leaf addresses planning without search in strategic settings, emphasizing efficient decision-making rather than influence optimization. Nearby branches include 'Game-Based Persuasion Environments' and 'Strategic Reasoning Integration in Complex Games', which examine persuasion through game mechanics but typically without the Bayesian theoretical grounding or RL training focus presented here.
Among twenty-six candidates examined, the contribution-level analysis shows varied novelty signals. The theory-driven measurement framework examined seven candidates with one appearing to provide overlapping prior work, suggesting some precedent exists for principled persuasion evaluation. The environment construction contribution examined nine candidates with none clearly refuting it, indicating relative novelty in repurposing human-human datasets for LLM strategic persuasion training. The reinforcement learning training approach examined ten candidates with no clear refutations, suggesting this specific application of RL to Bayesian Persuasion environments may be less explored in the limited search scope.
Based on the limited search of top-K semantic matches and citation expansion, the work appears to occupy a moderately novel position, particularly in combining Bayesian theoretical foundations with RL-based training. The sparse population of its taxonomy leaf and the absence of clear refutations for two of three contributions support this impression, though the analysis does not cover exhaustive literature review or domain-specific persuasion research outside the examined candidates.
Taxonomy
Research Landscape Overview
Claimed Contributions
The authors propose a principled framework grounded in Bayesian persuasion theory to systematically measure and evaluate the persuasive capabilities of large language models. This framework provides scalable measurements using persuasion gains and signals as instruments, addressing challenges in evaluating persuasion across heterogeneous domains.
The authors construct scalable environments by repurposing existing human persuasion datasets, enabling both evaluation and training of LLMs in strategic persuasion settings. These environments implement both Sender and Receiver roles using LLMs within the Bayesian persuasion framework.
The authors develop a reinforcement learning framework to train LLMs as strategic persuaders, demonstrating that even small LLMs can achieve significantly higher persuasion gains through RL training. The approach maximizes persuasion rewards defined by utility gains over prior beliefs.
Core Task Comparisons
Comparisons with papers in the same taxonomy category
[50] Planning without Search: Refining Frontier LLMs with Offline Goal-Conditioned RL PDF
Contribution Analysis
Detailed comparisons for each claimed contribution
Theory-driven framework for measuring LLM persuasive capabilities
The authors propose a principled framework grounded in Bayesian persuasion theory to systematically measure and evaluate the persuasive capabilities of large language models. This framework provides scalable measurements using persuasion gains and signals as instruments, addressing challenges in evaluating persuasion across heterogeneous domains.
[14] Verbalized Bayesian Persuasion PDF
[58] Efficient Model-agnostic Alignment via Bayesian Persuasion PDF
[59] AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors PDF
[60] Information Bargaining: Bilateral Commitment in Bayesian Persuasion PDF
[61] Information Design With Large Language Models PDF
[62] Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment PDF
[63] BASIL: Bayesian Assessment of Sycophancy in LLMs PDF
Environments for evaluating and training LLMs in strategic persuasion
The authors construct scalable environments by repurposing existing human persuasion datasets, enabling both evaluation and training of LLMs in strategic persuasion settings. These environments implement both Sender and Receiver roles using LLMs within the Bayesian persuasion framework.
[38] The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation PDF
[48] âI understand your perspectiveâ: LLM Persuasion through the Lens of Communicative Action Theory PDF
[51] The persuasive potential of AI-paraphrased information at scale PDF
[52] âReasoningâ with Rhetoric: On the Style-Evidence Tradeoff in LLM-Generated Counter-Arguments PDF
[53] Emergent Persuasion: Will LLMs Persuade Without Being Prompted? PDF
[54] Uncovering the Persuasive Fingerprint of LLMs in Jailbreaking Attacks PDF
[55] The Anatomy of Speech Persuasion: Linguistic Shifts in LLM-Modified Speeches PDF
[56] Training and Analyzing Language Agents in Socially Complex Dialogues PDF
[57] Optimizing for Persuasion Improves LLM Generalization: Evidence from Quality-Diversity Evolution of Debate Strategies PDF
Reinforcement learning approach for training strategic persuaders
The authors develop a reinforcement learning framework to train LLMs as strategic persuaders, demonstrating that even small LLMs can achieve significantly higher persuasion gains through RL training. The approach maximizes persuasion rewards defined by utility gains over prior beliefs.