Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust. While Large Language Models (LLMs) excel in text generation, their capability for producing faithful explanations in fact-checking remains underexamined. Our study investigates LLMs' ability to generate such explanations, finding that zero-shot prompts often result in unfaithfulness. To address these challenges, we propose the Multi-Agent Debate Refinement (MADR) framework, leveraging multiple LLMs as agents with diverse roles in an iterative refining process aimed at enhancing faithfulness in generated explanations. MADR ensures that the final explanation undergoes rigorous validation, significantly reducing the likelihood of unfaithful elements and aligning closely with the provided evidence. Experimental results demonstrate that MADR significantly improves the faithfulness of LLM-generated explanations to the evidence, advancing the credibility and trustworthiness of these explanations.

Don't you hate it," says Jon the Robot, gesturing with tiny articulated arms at an expectant crowd, "when you're trying to solve inverse kinematics equations to pick up a cup and then you get'Error 453, no solution found'?" An experiment billed as a comedy act, Jon is the brainchild of Naomi Fitter, an assistant professor in the School of Mechanical, Industrial and Manufacturing Engineering at Oregon State University. The tiny android performs when a handler (who must also hold the mic) presses a button, then tells the same jokes in the same order, like a grizzled veteran comic at a down-market Vegas casino. But the robot's act is more human than it might first appear. Jon is learning how to respond to its audience--it can now vary the timing of its delivery based on the length of the audience's laughter, and append different responses to jokes based on the level of noise in the room. It can deliver one line if a joke gets a roar of laughter ("Please tell the booking agents how funny that joke was") and another if there are crickets ("Sorry about that.