The paper introduces a flexible, theoretically grounded framework for steering and scaling autoregressive large language models (LLMs) through sampling. It presents two algorithms—Sequential Monte Carlo (SMC) and Replica Exchange (RE)—that guide generation toward desired distributions such as powering, product, or tilting of the base model. Experiments show these methods outperform Best‑of‑N and standard MCMC baselines, offering a systematic recipe for probabilistic inference with LLMs via sampling.
By Jiajun He, Zongyu Guo, Jos\'e Miguel Hern\'andez-Lobato, Yuanqi Du
arXiv:2608. 15448v1 Announce Type: cross Abstract: Large language models increasingly rely on sampling as a driver of their own improvement, making the fidelity of their learned distributions more critical than ever.
By Nicolas Zucchet, Hyun Dong Lee, Scott Linderman
arXiv:2607. 25292v1 Announce Type: new Abstract: Silicon sampling uses language models as proxies for human survey respondents, treating each model call as an independent draw from the persona's response distribution.
By Chaemin Jang, Dongman Lee, Jihee Kim
arXiv:2608. 11215v1 Announce Type: new Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent.
By Igor Itkin
arXiv:2502. 11027v5 Announce Type: replace Abstract: Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it.
By Tianchun Wang, Zichuan Liu, Yuanzhou Chen, Jonathan Light, Weiyang Liu, Haifeng Chen, Xiang Zhang, Wei Cheng
The paper introduces RareTrap, a framework that estimates the probability of severe behaviors in black‑box large language models. RareTrap constructs a geometry‑aware mapping from a low‑dimensional latent space into token‑embedding space using a surrogate LLM, creating an explicit and reproducible distribution over input prompts. By applying a response‑level performance function and sequential rare‑event simulation, RareTrap concentrates evaluations on increasingly severe behaviors while preserving probability, enabling estimation of such behaviors with as few as 200 evaluations across multiple open‑weight and frontier models.
By Elsayed Eshra, Ali Al-Lawati, Dongwon Lee, Suhang Wang
The paper introduces Dual-Seed Comparison (DSC), a protocol that uses two independent LLM-generated seeds to reduce systematic bias in probabilistic sampling. DSC constructs a bit sequence from the character-level ordinal values of the seeds, normalizes it into a pseudo-uniform variate, and maps it to the target distribution via the inverse cumulative distribution function. Empirical results show DSC outperforms existing methods in 96% of evaluated settings and enhances distributional control in tasks like MCQ generation and attribute-constrained text-to-image prompting.
By Zihao Guo, Hongtao Lv, Chaoli Zhang, Laiguo Yin, Lei Liu, Yonghui Xu, Lizhen Cui
arXiv:2607. 18454v1 Announce Type: cross Abstract: Quantifying the risk of rare failures in language models, such as those triggered by adversarial distribution shifts or very large-scale deployments, requires estimating probabilities far too small for random sampling.
By Nikita Y. Parulekar, Anqi Liu
arXiv:2604. 09921v2 Announce Type: replace Abstract: Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs).
By Theo X. Olausson, Metod Jazbec, Xi Wang, Armando Solar-Lezama, Christian A. Naesseth, Stephan Mandt, Eric Nalisnick
arXiv:2606. 19868v1 Announce Type: new Abstract: Although large language models (LLMs) have shown strong capabilities across a wide range of tasks, their outputs often remain unreliable and may contain hallucinations, making uncertainty estimation (UE) essential for building trustworthy LLMs.
By Jiayi Wang, Xu-Yao Zhang
arXiv:2607. 03882v1 Announce Type: cross Abstract: LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information in natural language.
By Diego Cerda-Mardini, Sarath Chandar, Sreenath Madathil
Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for ensuring predictive credibility. While traditional uncertainty taxonomy paradigms, such as the dichotomy of aleatoric and epistemic uncertainties, provide conceptual foundations, they often fail to capture the multi-component and multi-stage nature of LLM generation and struggle to evaluate the effectiveness of various Uncertainty Quantification (UQ) methods.