Papers with PBT
Teaching Models to Balance Resisting and Accepting Persuasion (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are susceptible to persuasion, which can pose risks when faced with an adversarial interlocutor. |
| Approach: | They propose a method to balance positive and negative persuasion by using recursive dialogue trees to train models to accept persulasion. |
| Outcome: | The proposed model-based training improves resistance to misinformation and resilience to being challenged while also resulting in the best overall performance on multi-agent debates across two domains. |
Beyond Superficial Tests: Adversarial Refinement for Reliable Property-Based Testing (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation, yet their application to Property-Based Testing (PBT) remains fraught with a superficiality gap. |
| Approach: | They propose an agentic framework that hardens software properties through Adversarial Refinement. |
| Outcome: | a new framework hardens software properties through Adversarial Refinement that detects and fixes bugs in top-tier libraries. |