Inference and Generation

2026

1. 

   Reasoning with Latent Tokens in Diffusion Language Models

   Andre He, Sean Welleck, and Daniel Fried

   arXiv, 2026

2. 

   Scaling Evaluation-Time Compute with Reasoning Models as Evaluators

   Seungone Kim, Ian Wu, Jinu Lee, and 8 more authors

   In Findings of the Association for Computational Linguistics: ACL 2026, 2026

   ACL 2026 Findings

   Code

2025

1. 

   Optimizing Temperature for Language Models with Multi-Sample Inference

   Weihua Du, Yiming Yang, and Sean Welleck

   arXiv preprint arXiv:2502.05234, 2025

   ICML 2025

2. 

   Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

   Yangzhen Wu, Zhiqing Sun, Shanda Li, and 2 more authors

   2025

   ICLR 2025

2024

1. 

   From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

   Sean Welleck, Amanda Bertsch, Matthew Finlayson, and 5 more authors

   Transactions on Machine Learning Research, Oct 2024

2023

1. 

   Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning

   Ximing Lu, Faeze Brahman, Peter West, and 14 more authors

   EMNLP, Oct 2023

2022

1. 

   COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics

   Lianhui Qin, Sean Welleck, Daniel Khashabi, and 1 more author

   In Advances in Neural Information Processing Systems, Oct 2022

   NeurIPS 2022 Oral

2. 

   NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics

   Ximing Lu, Sean Welleck, Peter West, and 9 more authors

   In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jul 2022

   Best Paper Award

   Abs

   The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing inspiration from the A^* search algorithm, we propose NeuroLogic A*esque, a decoding algorithm that incorporates heuristic estimates of future cost. We develop lookahead heuristics that are efficient for large-scale language models, making our method a drop-in replacement for common techniques such as beam search and top-k sampling. To enable constrained generation, we build on NeuroLogic decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A*esque estimates of future constraint satisfaction. Our approach outperforms competitive baselines on five generation tasks, and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. The improvements are particularly notable on tasks that require complex constraint satisfaction or in few-shot or zero-shot settings. NeuroLogic A*esque illustrates the power of decoding for improving and enabling new capabilities of large-scale language models.

3. 

   Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

   Jaehun Jung, Lianhui Qin, Sean Welleck, and 4 more authors

   In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Dec 2022

   Abs

   Pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this. However, these approaches are fundamentally bounded by the correctness of explanations, which themselves are often noisy and inconsistent. In this work, we develop Maieutic Prompting, which aims to infer a correct answer to a question even from the unreliable generations of LM. Maieutic Prompting induces a tree of explanations abductively (e.g. X is true, because ...) and recursively, then frames the inference as a satisfiability problem over these explanations and their logical relations. We test Maieutic Prompting for true/false QA on three challenging benchmarks that require complex commonsense reasoning. Maieutic Prompting achieves up to 20% better accuracy than state-of-the-art prompting methods, and as a fully unsupervised approach, performs competitively with supervised models. We also show that Maieutic Prompting improves robustness in inference while providing interpretable rationales.

2020

1. 

   Consistency of a Recurrent Language Model With Respect to Incomplete Decoding

   Sean Welleck, Ilia Kulikov, Jaedeok Kim, and 2 more authors

   In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020

   Abs

   Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. We study the related issue of receiving infinite-length sequences from a recurrent language model when using common decoding algorithms. To analyze this issue, we first define inconsistency of a decoding algorithm, meaning that the algorithm can yield an infinite-length sequence that has zero probability under the model. We prove that commonly used incomplete decoding algorithms – greedy search, beam search, top-k sampling, and nucleus sampling – are inconsistent, despite the fact that recurrent language models are trained to produce sequences of finite length. Based on these insights, we propose two remedies which address inconsistency: consistent variants of top-k and nucleus sampling, and a self-terminating recurrent language model. Empirical results show that inconsistency occurs in practice, and that the proposed methods prevent inconsistency.

2. 

   A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models

   Elman Mansimov, Alex Wang, Sean Welleck, and 1 more author

   Nov 2020

2019

1. 

   Non-Monotonic Sequential Text Generation

   Sean Welleck, Kianté Brantley, Hal Daumé Iii, and 1 more author

   In Proceedings of the 36th International Conference on Machine Learning, 09–15 jun 2019

   Abs PDF

   Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation.