Hugging Face Trending Papers October 5, 2026

Nash Equilibrium Text: A Game-Theoretic Decoding Framework for Text Generation

Read the original on Hugging Face Trending Papers →

The paper introduces a game-theoretic approach to text revision, treating token positions as players and vocabulary items as actions, with utilities based on a language model’s log conditional probability. It shows that Nash equilibria can yield exponentially higher likelihoods than autoregressive outputs as sequence length increases, and proposes Nash decoding, an algorithm that finds an ε-Nash equilibrium in O(1/ε) time. Experiments on CLAPNQ, PubMedQA, and CoQA demonstrate that equilibria derived from masked language models achieve higher F1 and ROUGE scores than autoregressive models, up to 18× larger, without fine-tuning, though with extra test-time computation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Jun 26

When are likely answers right? On Sequence Probability and Correctness in LLMs

arXiv:2606. 27359v1 Announce Type: cross Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, either locally at the token level or globally at the sequence level.

By Johannes Zenn, Jonas Geiping

llms benchmarks

arXiv Computation and Language

Sep 4

Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding

The paper introduces Variance‑Calibrated Modulation (VCM), a training‑free pre‑decoding technique that reshapes language model probability distributions before truncation. VCM uses two dynamic mechanisms: a Contextual Searchlight via PMI to suppress stopwords and highlight context‑relevant tokens, and an Adaptive Self‑Debiasing that applies scale‑invariant penalization based on real‑time logit standard deviation. Experiments on open‑ended generation, factual QA, and mathematical reasoning show that VCM consistently reduces the likelihood trap, improving diversity, coherence, and reasoning accuracy with minimal computational cost.

By Yuanhao Ding, Meimingwei Li, Esteban Garces Arias, Matthias A{\ss}enmacher, Christian Heumann, Chongsheng Zhang

llms

arXiv Machine Learning

Jun 2

d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation

arXiv:2509. 21474v4 Announce Type: replace Abstract: While diffusion language models (DLMs) have achieved competitive performance in text generation, improving their reasoning ability with reinforcement learning remains an active research area.

By Guanghan Wang, Gilad Turok, Yair Schiff, Marianne Arriola, Volodymyr Kuleshov

llms diffusion reinforcement-learning benchmarks

arXiv AI

Jul 21

Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning

arXiv:2607. 16209v1 Announce Type: new Abstract: Context reranking and pruning have become essential for improving the efficiency of modern Retrieval-Augmented Generation (RAG) systems, yet an interpretable and unified framework remains underexplored.

By Yanqiao Chen, Dongsheng Hou, Yuhan Rui, Zhen Cao, Yepang Liu

llms rag fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 16

Enhancing LLM Safety Through a Theoretical Minimax Game Lens

arXiv:2502. 05163v2 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) necessitates effective mechanisms to ensure their responsible deployment by accurately distinguishing unsafe content from benign content.

By Yihe Deng, Yu Yang, Junkai Zhang, Wei Wang, Bo Li

llms reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Aug 11

From token probabilities to calibrated confidence: An empirical study of mathematical question answering

arXiv:2608. 07827v1 Announce Type: new Abstract: Confidence estimation for large language models (LLMs) aims to estimate the probability that a generated answer is correct, while calibration aligns these estimates with empirical accuracy.

By Avery Ma, Lorne Schell, Vin Bhaskara, Leila Pishdad

llms nlp

One email a morning, machine-written

Related stories

When are likely answers right? On Sequence Probability and Correctness in LLMs

Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding

d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation

Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning

Enhancing LLM Safety Through a Theoretical Minimax Game Lens

From token probabilities to calibrated confidence: An empirical study of mathematical question answering