{"list":"other","total":62,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2307.09288","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","created":"2023-07-18","cited_by_count":13028},{"id":"1904.09751","title":"The Curious Case of Neural Text Degeneration","created":"2019-04-22","cited_by_count":2916},{"id":"1808.06226","title":"SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing","created":"2018-08-19","cited_by_count":1989},{"id":"1804.08771","title":"A Call for Clarity in Reporting BLEU Scores","created":"2018-04-23","cited_by_count":1799},{"id":"2107.03006","title":"Structured Denoising Diffusion Models in Discrete State-Spaces","created":"2021-07-07","cited_by_count":1719},{"id":"2004.04696","title":"BLEURT: Learning Robust Metrics for Text Generation","created":"2020-04-09","cited_by_count":1203},{"id":"2205.14217","title":"Diffusion-LM Improves Controllable Text Generation","created":"2022-05-27","cited_by_count":1027},{"id":"1804.04235","title":"Adafactor: Adaptive Learning Rates with Sublinear Memory Cost","created":"2018-04-11","cited_by_count":905},{"id":"2401.10774","title":"Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads","created":"2024-01-19","cited_by_count":843},{"id":"1606.07947","title":"Sequence-Level Knowledge Distillation","created":"2016-06-25","cited_by_count":801},{"id":"1511.06349","title":"Generating Sentences from a Continuous Space","created":"2015-11-19","cited_by_count":612},{"id":"2102.05379","title":"Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions","created":"2021-02-10","cited_by_count":574},{"id":"1711.02281","title":"Non-Autoregressive Neural Machine Translation","created":"2017-11-07","cited_by_count":463},{"id":"2210.08933","title":"DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models","created":"2022-10-17","cited_by_count":450},{"id":"1912.10077","title":"Are Transformers universal approximators of sequence-to-sequence functions?","created":"2019-12-20","cited_by_count":406},{"id":"1609.05473","title":"SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient","created":"2016-09-18","cited_by_count":376},{"id":"2210.10749","title":"Transformers Learn Shortcuts to Automata","created":"2022-10-19","cited_by_count":268},{"id":"1802.06901","title":"Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement","created":"2018-02-19","cited_by_count":195},{"id":"2203.17189","title":"Scaling Up Models and Data with $\\texttt{t5x}$ and $\\texttt{seqio}$","created":"2022-03-31","cited_by_count":187},{"id":"1902.04094","title":"BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model","created":"2019-02-11","cited_by_count":185},{"id":"2202.11705","title":"COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics","created":"2022-02-23","cited_by_count":161},{"id":"2302.05737","title":"A Reparameterized Discrete Diffusion Model for Text Generation","created":"2023-02-11","cited_by_count":152},{"id":"2110.10090","title":"Inductive Biases and Variable Creation in Self-Attention Mechanisms","created":"2021-10-19","cited_by_count":144},{"id":"1902.03249","title":"Insertion Transformer: Flexible Sequence Generation via Insertion Operations","created":"2019-02-08","cited_by_count":125},{"id":"1911.02727","title":"Understanding Knowledge Distillation in Non-autoregressive Machine Translation","created":"2019-11-07","cited_by_count":122},{"id":"2006.10369","title":"Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation","created":"2020-06-18","cited_by_count":105},{"id":"2105.11115","title":"Self-Attention Networks Can Process Bounded Hierarchical Languages","created":"2021-05-24","cited_by_count":105},{"id":"2008.07905","title":"Glancing Transformer for Non-Autoregressive Neural Machine Translation","created":"2020-08-18","cited_by_count":103},{"id":"2107.13163","title":"Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers","created":"2021-07-28","cited_by_count":102},{"id":"2112.06749","title":"Step-unrolled Denoising Autoencoders for Text Generation","created":"2021-12-13","cited_by_count":102},{"id":"1709.08624","title":"Long Text Generation via Adversarial Training with Leaked Information","created":"2017-09-24","cited_by_count":100},{"id":"2012.15833","title":"Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade","created":"2020-12-31","cited_by_count":99},{"id":"2004.07437","title":"Non-Autoregressive Machine Translation with Latent Alignments","created":"2020-04-16","cited_by_count":96},{"id":"2004.11714","title":"Residual Energy-Based Models for Text Generation","created":"2020-04-22","cited_by_count":95},{"id":"2110.06341","title":"Learning Compact Metrics for MT","created":"2021-10-12","cited_by_count":93},{"id":"2210.09221","title":"Vision Transformers provably learn spatial structure","created":"2022-10-13","cited_by_count":93},{"id":"1909.02480","title":"FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow","created":"2019-09-05","cited_by_count":89},{"id":"2303.04245","title":"How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding","created":"2023-03-07","cited_by_count":86},{"id":"1901.10548","title":"Latent Normalizing Flows for Discrete Sequences","created":"2019-01-29","cited_by_count":67},{"id":"2002.08926","title":"Imputer: Sequence Modelling via Imputation and Dynamic Programming","created":"2020-02-20","cited_by_count":66},{"id":"2210.00726","title":"Statistical Efficiency of Score Matching: The View from Isoperimetry","created":"2022-10-03","cited_by_count":64},{"id":"2010.11939","title":"Limitations of Autoregressive Models and Their Alternatives","created":"2020-10-22","cited_by_count":55},{"id":"1705.11001","title":"Adversarial Ranking for Language Generation","created":"2017-05-31","cited_by_count":50},{"id":"2303.08117","title":"Do Transformers Parse while Predicting the Masked Word?","created":"2023-03-14","cited_by_count":48},{"id":"1605.07252","title":"Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models","created":"2016-05-24","cited_by_count":43},{"id":"1702.07983","title":"Maximum-Likelihood Augmented Discrete Generative Adversarial Networks","created":"2017-02-26","cited_by_count":39},{"id":"2106.02736","title":"Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings","created":"2021-06-04","cited_by_count":37},{"id":"2001.08785","title":"Semi-Autoregressive Training Improves Mask-Predict Decoding","created":"2020-01-23","cited_by_count":33},{"id":"2004.10454","title":"A Study of Non-autoregressive Model for Sequence Generation","created":"2020-04-22","cited_by_count":31},{"id":"2312.01429","title":"Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars","created":"2023-12-03","cited_by_count":30},{"id":"2306.01993","title":"Provable benefits of score matching","created":"2023-06-03","cited_by_count":18},{"id":"2009.07177","title":"Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation","created":"2020-09-15","cited_by_count":13},{"id":"2302.02060","title":"Representation Deficiency in Masked Language Modeling","created":"2023-02-04","cited_by_count":10},{"id":"2011.06132","title":"Incorporating a Local Translation Mechanism into Non-autoregressive Translation","created":"2020-11-12","cited_by_count":9},{"id":"2305.15501","title":"Deriving Language Models from Masked Language Models","created":"2023-05-24","cited_by_count":9},{"id":"2011.10036","title":"On the Dynamics of Training Attention Models","created":"2020-11-19","cited_by_count":8},{"id":"2004.07683","title":"Do sequence-to-sequence VAEs learn global features of sentences?","created":"2020-04-16","cited_by_count":6},{"id":"2306.09332","title":"Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions","created":"2023-06-15","cited_by_count":6},{"id":"2106.01580","title":"The Limitations of Limited Context for Constituency Parsing","created":"2021-06-03","cited_by_count":5}],"next_cursor":null}