Incentivizing Truthful Language Models via Peer Elicitation Games

• URL: http://arxiv.org/abs/2505.13636v1
• Date: Mon, 19 May 2025 18:16:58 GMT
• Title: Incentivizing Truthful Language Models via Peer Elicitation Games
• Authors: Baiting Chen, Tong Zhu, Jiale Han, Lexin Li, Gang Li, Xiaowu Dai,
• Abstract summary: Large Language Models (LLMs) have demonstrated strong generative capabilities but remain prone to inconsistencies and hallucinations.<n>We introduce Peer Elicitation Games (PEG), a training-free, game-theoretic framework for aligning LLMs through a peer elicitation mechanism involving a generator and multiple discriminators instantiated from distinct base models.
• Score: 10.530016288072506
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated strong generative capabilities but remain prone to inconsistencies and hallucinations. We introduce Peer Elicitation Games (PEG), a training-free, game-theoretic framework for aligning LLMs through a peer elicitation mechanism involving a generator and multiple discriminators instantiated from distinct base models. Discriminators interact in a peer evaluation setting, where rewards are computed using a determinant-based mutual information score that provably incentivizes truthful reporting without requiring ground-truth labels. We establish theoretical guarantees showing that each agent, via online learning, achieves sublinear regret in the sense their cumulative performance approaches that of the best fixed truthful strategy in hindsight. Moreover, we prove last-iterate convergence to a truthful Nash equilibrium, ensuring that the actual policies used by agents converge to stable and truthful behavior over time. Empirical evaluations across multiple benchmarks demonstrate significant improvements in factual accuracy. These results position PEG as a practical approach for eliciting truthful behavior from LLMs without supervision or fine-tuning.

Related papers

• Stochastically Dominant Peer Prediction [11.183872292320824]
  We propose dominantally dominant (SD-truthfulness) as a stronger guarantee of truthful reporting.<n>A simple solution -- rounding into binary lotteries -- can enforce SD-truthfulness, but often degrades sensitivity.<n>We demonstrate how a more careful application of rounding can better preserve sensitivity.
  arXiv  Detail & Related papers  (2025-06-02T21:07:24Z)
• Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards [11.149294285483782]
  We propose a unified RLVR-based training paradigm that bridges the gap between non-verifiable tasks and verifiable rewards.<n>We introduce a writing-principle-based pairwise Generative Reward Model (GenRM) and a novel Bootstrapped Relative Policy Optimization (BRPO) algorithm.<n>Our approach empowers LLMs to develop robust writing capabilities without supervised fine-tuning.
  arXiv  Detail & Related papers  (2025-05-30T14:34:57Z)
• Improving Fairness in LLMs Through Testing-Time Adversaries [1.7811840395202343]
  Large Language Models (LLMs) push the bound-aries in natural language processing and generative AI.<n>In this work, we propose a straightforward, user-friendly and practical method to mitigate such biases.<n>Our method creates multiple variations of a given sentence by modifying specific attributes and evaluates the corresponding prediction behavior.
  arXiv  Detail & Related papers  (2025-05-17T17:56:53Z)
• Search-Based Correction of Reasoning Chains for Language Models [72.61861891295302]
  Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs)<n>We introduce a new self-correction framework that augments each reasoning step in a CoT with a latent variable indicating its veracity.<n>We also introduce Search Corrector, a discrete search algorithm over-valued veracity assignments.
  arXiv  Detail & Related papers  (2025-05-17T04:16:36Z)
• Towards Robust LLMs: an Adversarial Robustness Measurement Framework [0.0]
  Large Language Models (LLMs) remain vulnerable to adversarial perturbations, undermining their reliability in high-stakes applications.<n>We adapt the Robustness Measurement and Assessment framework to quantify LLM resilience against adversarial inputs without requiring access to model parameters.<n>Our work provides a systematic methodology to assess LLM robustness, advancing the development of more reliable language models for real-world deployment.
  arXiv  Detail & Related papers  (2025-04-24T16:36:19Z)
• Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games [3.725822359130832]
  Large Language Models (LLMs) are increasingly being explored as evaluators in serious games.<n>This study investigates the reliability of five small-scale LLMs when assessing player responses in textitEn-join, a game that simulates decision-making within energy communities.<n>Our results highlight the strengths and limitations of each model, revealing trade-offs between sensitivity, specificity, and overall performance.
  arXiv  Detail & Related papers  (2025-04-13T10:46:13Z)
• Estimating Commonsense Plausibility through Semantic Shifts [66.06254418551737]
  We propose ComPaSS, a novel discriminative framework that quantifies commonsense plausibility by measuring semantic shifts.<n> Evaluations on two types of fine-grained commonsense plausibility estimation tasks show that ComPaSS consistently outperforms baselines.
  arXiv  Detail & Related papers  (2025-02-19T06:31:06Z)
• Confidence Diagram of Nonparametric Ranking for Uncertainty Assessment in Large Language Models Evaluation [20.022623972491733]
  Ranking large language models (LLMs) has proven to be an effective tool to improve alignment based on the best-of-$N$ policy.<n>We propose a new inferential framework for hypothesis testing among the ranking for language models.
  arXiv  Detail & Related papers  (2024-12-07T02:34:30Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• Towards Effective Evaluations and Comparisons for LLM Unlearning Methods [97.2995389188179]
  This paper seeks to refine the evaluation of machine unlearning for large language models.<n>It addresses two key challenges -- the robustness of evaluation metrics and the trade-offs between competing goals.
  arXiv  Detail & Related papers  (2024-06-13T14:41:00Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Calibrating Reasoning in Language Models with Internal Consistency [18.24350001344488]
  Large language models (LLMs) have demonstrated impressive capabilities in various reasoning tasks.<n>LLMs often generate text with obvious mistakes and contradictions.<n>In this work, we investigate reasoning in LLMs through the lens of internal representations.
  arXiv  Detail & Related papers  (2024-05-29T02:44:12Z)
• FactCHD: Benchmarking Fact-Conflicting Hallucination Detection [64.4610684475899]
  FactCHD is a benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. We introduce Truth-Triangulator that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2.
  arXiv  Detail & Related papers  (2023-10-18T16:27:49Z)
• Exploring validation metrics for offline model-based optimisation with diffusion models [50.404829846182764]
  In model-based optimisation (MBO) we are interested in using machine learning to design candidates that maximise some measure of reward with respect to a black box function called the (ground truth) oracle. While an approximation to the ground oracle can be trained and used in place of it during model validation to measure the mean reward over generated candidates, the evaluation is approximate and vulnerable to adversarial examples. This is encapsulated under our proposed evaluation framework which is also designed to measure extrapolation.
  arXiv  Detail & Related papers  (2022-11-19T16:57:37Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• Adversarial Robustness of Supervised Sparse Coding [34.94566482399662]
  We consider a model that involves learning a representation while at the same time giving a precise generalization bound and a robustness certificate. We focus on the hypothesis class obtained by combining a sparsity-promoting encoder coupled with a linear encoder. We provide a robustness certificate for end-to-end classification.
  arXiv  Detail & Related papers  (2020-10-22T22:05:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.