SOTAVerified

Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

Showing 51515200 of 17610 papers

TitleStatusHype
Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit LogsCode0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?Code0
Do Vision-Language Models Understand Compound Nouns?Code0
Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair PredictionCode0
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo MethodsCode0
AllenNLP Interpret: A Framework for Explaining Predictions of NLP ModelsCode0
Alleviating Sequence Information Loss with Data Overlapping and Prime Batch SizesCode0
Improved training of neural trans-dimensional random field language models with dynamic noise-contrastive estimationCode0
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified TextCode0
DPPA: Pruning Method for Large Language Model to Model MergingCode0
Improved Word Representation Learning with SememesCode0
Improve Language Model and Brain Alignment via Associative MemoryCode0
Evaluating Online Continual Learning with CALMCode0
DPTDR: Deep Prompt Tuning for Dense Passage RetrievalCode0
Auto-tagging of Short Conversational Sentences using Natural Language Processing MethodsCode0
A Feasible Framework for Arbitrary-Shaped Scene Text RecognitionCode0
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model DecodingCode0
A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative GenerationCode0
Classifier-guided CLIP Distillation for Unsupervised Multi-label ClassificationCode0
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical DataCode0
Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative StudyCode0
Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot SettingCode0
Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question AlignmentCode0
Improving Context Aware Language ModelsCode0
B-AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Black-box Adversarial Visual-InstructionsCode0
A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake NewsCode0
Drop Dropout on Single-Epoch Language Model PretrainingCode0
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data AugmentationCode0
DropMicroFluidAgents (DMFAs): Autonomous Droplet Microfluidic Research Framework Through Large Language Model AgentsCode0
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust OptimizationCode0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply InteractionsCode0
DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule GraphsCode0
DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy OptimizationCode0
A-VL: Adaptive Attention for Large Vision-Language ModelsCode0
DrugTar Improves Druggability Prediction by Integrating Large Language Models and Gene OntologiesCode0
DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition ExtractionCode0
DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language ModelsCode0
Avoiding Copyright Infringement via Large Language Model UnlearningCode0
ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMsCode0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"?Code0
Improving Generalization Performance by Switching from Adam to SGDCode0
Improving Grammatical Error Correction with Machine Translation PairsCode0
DS-TOD: Efficient Domain Specialization for Task Oriented DialogCode0
Improving In-Context Learning with Small Language Model EnsemblesCode0
Improving Information Extraction on Business Documents with Specific Pre-Training TasksCode0
DS-TOD: Efficient Domain Specialization for Task-Oriented DialogCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified