{"total":21,"rows":[{"id":"2106.09226","title":"Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning","created":"2021-06-17","cited_by_count":82,"area":null},{"id":"2304.07438","title":"Tractable Control for Autoregressive Language Generation","created":"2023-04-15","cited_by_count":65,"area":null},{"id":"2210.04398","title":"Scaling Up Probabilistic Circuits by Latent Variable Distillation","created":"2022-10-10","cited_by_count":35,"area":null},{"id":"2109.01135","title":"Sequence-to-Sequence Learning with Latent Neural Grammars","created":"2021-09-02","cited_by_count":30,"area":null},{"id":"2104.13727","title":"PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols","created":"2021-04-28","cited_by_count":22,"area":null},{"id":"2105.15021","title":"Neural Bi-Lexicalized PCFG Induction","created":"2021-05-31","cited_by_count":18,"area":null},{"id":"2211.16742","title":"Protein Language Models and Structure Prediction: Connection and Progression","created":"2022-11-30","cited_by_count":17,"area":null},{"id":"2105.12848","title":"BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition","created":"2021-05-26","cited_by_count":15,"area":null},{"id":"2104.00772","title":"Low-Resource Language Modelling of South African Languages","created":"2021-04-01","cited_by_count":10,"area":null},{"id":"2104.03474","title":"Revisiting Simple Neural Probabilistic Language Models","created":"2021-04-08","cited_by_count":10,"area":null},{"id":"2205.00484","title":"Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs","created":"2022-05-01","cited_by_count":10,"area":null},{"id":"2406.04089","title":"On Limitation of Transformer for Learning HMMs","created":"2024-06-06","cited_by_count":10,"area":null},{"id":"2201.02715","title":"Low-Rank Constraints for Fast Inference in Structured Models","created":"2022-01-08","cited_by_count":8,"area":null},{"id":"2212.09140","title":"Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars","created":"2022-12-18","cited_by_count":7,"area":null},{"id":"2407.07636","title":"MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations","created":"2024-07-10","cited_by_count":3,"area":null},{"id":"2112.03638","title":"Scaling Structured Inference with Randomization","created":"2021-12-07","cited_by_count":2,"area":null},{"id":"2210.16659","title":"Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models","created":"2022-10-29","cited_by_count":1,"area":null},{"id":"2301.00488","title":"Information Transfer Rate in BCIs: Towards Tightly Integrated Symbiosis","created":"2023-01-01","cited_by_count":1,"area":null},{"id":"2306.02671","title":"Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints","created":"2023-06-05","cited_by_count":1,"area":null},{"id":"2410.00699","title":"Investigating the Impact of Model Complexity in Large Language Models","created":"2024-10-01","cited_by_count":0,"area":null},{"id":"2511.16054","title":"Learning Tractable Distributions Of Language Model Continuations","created":"2025-11-20","cited_by_count":0,"area":null}],"next_cursor":null}