# A Voter-Based Stochastic Rejection-Method Framework for Asymptotically Safe Language Model Outputs

arXiv: https://arxiv.org/abs/2407.16994 · HTML: https://mlsanity.com/paper/2407.16994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [Low-Resource Languages Jailbreak GPT-4](https://mlsanity.com/paper/2310.02446/) (2023)
- [Large Language Model Guided Tree-of-Thought](https://mlsanity.com/paper/2305.08291/) (2023)
- [AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks](https://mlsanity.com/paper/2403.04783/) (2024)
- [Deception Abilities Emerged in Large Language Models](https://mlsanity.com/paper/2307.16513/) (2023)
- [Do LLM Agents Exhibit Social Behavior?](https://mlsanity.com/paper/2312.15198/) (2023)
- [Where's the Liability in Harmful AI Speech?](https://mlsanity.com/paper/2308.04635/) (2023)

## Abstract (arXiv)

We propose an approach for preventing unsafe or otherwise low-quality large language model (LLM) outputs by leveraging the stochasticity of LLMs, an approach we call Repeated Checking with Regeneration (RCR). In this system, LLM checkers vote on the acceptability of a generated output, regenerating it if a threshold of disapproval is reached, until sufficient checkers approve. Based on our estimators for cost and failure rate and experimental data tailored to the application, our algorithm achieves a desired expected failure rate at Pareto-optimal cost. The failure rate provably decreases exponentially as a function of cost, and the models reasonably estimate the actual performance of such a system in action, even with limited data. This approach does not depend on the language model used, and could allow cheap, small LLMs to control, constrain, or at some tasks even outperform very complex and costly ones.