{"total":28,"rows":[{"id":"2307.05782","title":"Large Language Models","created":"2023-07-11","cited_by_count":780,"area":null},{"id":"2207.02098","title":"Neural Networks and the Chomsky Hierarchy","created":"2022-07-05","cited_by_count":217,"area":null},{"id":"2112.08726","title":"NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics","created":"2021-12-16","cited_by_count":154,"area":null},{"id":"2210.00045","title":"Calibrating Sequence likelihood Improves Conditional Language Generation","created":"2022-09-30","cited_by_count":121,"area":null},{"id":"2107.13163","title":"Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers","created":"2021-07-28","cited_by_count":102,"area":null},{"id":"2301.10743","title":"Tighter Bounds on the Expressivity of Transformer Encoders","created":"2023-01-25","cited_by_count":66,"area":null},{"id":"2212.10502","title":"A Measure-Theoretic Characterization of Tight Language Models","created":"2022-12-20","cited_by_count":33,"area":null},{"id":"2401.14953","title":"Learning Universal Predictors","created":"2024-01-26","cited_by_count":31,"area":null},{"id":"2203.15108","title":"A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation","created":"2022-03-28","cited_by_count":28,"area":null},{"id":"2407.03310","title":"Universal Length Generalization with Turing Programs","created":"2024-07-03","cited_by_count":25,"area":null},{"id":"2310.16076","title":"Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions","created":"2023-10-24","cited_by_count":20,"area":null},{"id":"2410.03170","title":"Autoregressive Large Language Models are Computationally Universal","created":"2024-10-04","cited_by_count":15,"area":null},{"id":"2410.15580","title":"Language Models are Symbolic Learners in Arithmetic","created":"2024-10-21","cited_by_count":11,"area":null},{"id":"2210.01343","title":"The Surprising Computational Power of Nondeterministic Stack RNNs","created":"2022-10-04","cited_by_count":6,"area":null},{"id":"2310.05161","title":"Recurrent Neural Language Models as Probabilistic Finite-state Automata","created":"2023-10-08","cited_by_count":6,"area":null},{"id":"2210.06464","title":"Predictive Querying for Autoregressive Neural Sequence Models","created":"2022-10-12","cited_by_count":5,"area":null},{"id":"2402.00236","title":"Positional Encoding Helps Recurrent Neural Networks Handle a Large Vocabulary","created":"2024-01-31","cited_by_count":5,"area":null},{"id":"2312.06149","title":"Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding","created":"2023-12-11","cited_by_count":4,"area":null},{"id":"2202.09717","title":"Understanding Robust Generalization in Learning Regular Languages","created":"2022-02-20","cited_by_count":3,"area":null},{"id":"2406.05045","title":"A Tensor Decomposition Perspective on Second-order RNNs","created":"2024-06-07","cited_by_count":3,"area":null},{"id":"2409.12446","title":"Neural Networks Generalize on Low Complexity Data","created":"2024-09-19","cited_by_count":3,"area":null},{"id":"2308.13696","title":"On the Depth between Beam Search and Exhaustive Search for Text Generation","created":"2023-08-25","cited_by_count":2,"area":null},{"id":"2606.01765","title":"An Algebraic View of the Expressivity of Recurrent Language Models","created":"2026-06-01","cited_by_count":2,"area":null},{"id":"2310.12942","title":"On the Representational Capacity of Recurrent Neural Language Models","created":"2023-10-19","cited_by_count":1,"area":null},{"id":"2501.06278","title":"Aligning Brain Activity with Advanced Transformer Models: Exploring the Role of Punctuation in Semantic Processing","created":"2025-01-10","cited_by_count":1,"area":null},{"id":"2303.06841","title":"Learning Transductions and Alignments with RNN Seq2seq Models","created":"2023-03-13","cited_by_count":0,"area":null},{"id":"2410.03154","title":"Exploring Learnability in Memory-Augmented Recurrent Neural Networks: Precision, Stability, and Empirical Insights","created":"2024-10-04","cited_by_count":0,"area":null},{"id":"2503.02512","title":"LTL Verification of Memoryful Neural Agents","created":"2025-03-04","cited_by_count":0,"area":null}],"next_cursor":null}