@inproceedings{richardson-etal-2026-token,
title = "From Token Probabilities to Semantic Constraints: Towards Declarative Probabilistic Evaluation of Language Models",
author = "Richardson, Kyle and
Anderson, Cullen and
Balakrishnan, Pranav and
Ban, Takuto and
Ladia, Daksha and
Gupta, Ankita and
Hudspeth, Marisa",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1752/",
pages = "39963--39982",
ISBN = "979-8-89176-459-0",
abstract = "While Large Language Models have improved rapidly, many fundamental questions remain about how to evaluate the knowledge and reasoning abilities they acquire, and how such evaluations relate to the learning signals used in pre-training. In this paper, we propose ModelLog, a declarative probabilistic framework for pre-training evaluation that makes the semantic structure of model behavior explicit and provides new formal tools for relating evaluation to learning. ModelLog specifies evaluation targets as symbolic constraints over token-level predictions and measures how strongly a model{'}s distribution satisfies those constraints. We explore the framework through a new suite of tasks targeting negation, mutual exclusivity, and consistency, finding systematic failures that are difficult to characterize through token likelihood or answer accuracy alone. We further show that these evaluation scores can also be interpreted as losses, whose gradients reflect logical strength, informativeness, and variable-level sensitivity. This links evaluation and learning through a shared semantics, suggesting evaluation methods that diagnose model behavior while also helping to clarify the semantic structure of learning."
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%0 Conference Proceedings
%T From Token Probabilities to Semantic Constraints: Towards Declarative Probabilistic Evaluation of Language Models
%A Richardson, Kyle
%A Anderson, Cullen
%A Balakrishnan, Pranav
%A Ban, Takuto
%A Ladia, Daksha
%A Gupta, Ankita
%A Hudspeth, Marisa
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F richardson-etal-2026-token
%X While Large Language Models have improved rapidly, many fundamental questions remain about how to evaluate the knowledge and reasoning abilities they acquire, and how such evaluations relate to the learning signals used in pre-training. In this paper, we propose ModelLog, a declarative probabilistic framework for pre-training evaluation that makes the semantic structure of model behavior explicit and provides new formal tools for relating evaluation to learning. ModelLog specifies evaluation targets as symbolic constraints over token-level predictions and measures how strongly a model’s distribution satisfies those constraints. We explore the framework through a new suite of tasks targeting negation, mutual exclusivity, and consistency, finding systematic failures that are difficult to characterize through token likelihood or answer accuracy alone. We further show that these evaluation scores can also be interpreted as losses, whose gradients reflect logical strength, informativeness, and variable-level sensitivity. This links evaluation and learning through a shared semantics, suggesting evaluation methods that diagnose model behavior while also helping to clarify the semantic structure of learning.
%U https://aclanthology.org/2026.emnlp-main.1752/
%P 39963-39982
Markdown (Informal)
[From Token Probabilities to Semantic Constraints: Towards Declarative Probabilistic Evaluation of Language Models](https://aclanthology.org/2026.emnlp-main.1752/) (Richardson et al., EMNLP 2026)
ACL