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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 11601–11650 of 17610 papers

TitleStatusHype
SemMAE: Semantic-Guided Masking for Learning Masked AutoencodersCode1
TAPHSIR: Towards AnaPHoric Ambiguity Detection and ReSolution In Requirements—0
Automatic Controllable Product Copywriting for E-CommerceCode1
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender BiasCode0
SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity PredictionCode1
0/1 Deep Neural Networks via Block Coordinate Descent—0
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt LearningCode1
Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa—0
Making first order linear logic a generating grammar—0
Evolution through Large Models—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media—0
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition—0
Know your audience: specializing grounded language models with listener subtraction—0
PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix QualityCode0
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models—0
A Language Model With Million Context Length For Raw Audio—0
Zero-Shot Video Question Answering via Frozen Bidirectional Language ModelsCode1
Test-Time Adaptation for Visual Document Understanding—0
Write and Paint: Generative Vision-Language Models are Unified Modal LearnersCode1
Residual Language Model for End-to-end Speech Recognition—0
Emergent Abilities of Large Language Models—0
DIRECTOR: Generator-Classifiers For Supervised Language Modeling—0
A Survey : Neural Networks for AMR-to-Text—0
LAVENDER: Unifying Video-Language Understanding as Masked Language ModelingCode1
Language Models are General-Purpose Interfaces—0
Memory-Based Model Editing at ScaleCode1
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem UnderstandingCode1
GLIPv2: Unifying Localization and Vision-Language UnderstandingCode4
Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization—0
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams—0
Putting GPT-3's Creativity to the (Alternative Uses) TestCode0
Sort by Structure: Language Model Ranking as Dependency Probing—0
Measuring the Carbon Intensity of AI in Cloud Instances—0
Unsupervised and Few-shot Parsing from Pretrained Language Models—0
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement PredictionCode1
Traditional and context-specific spam detection in low resource settingsCode1
SsciBERT: A Pre-trained Language Model for Social Science TextsCode1
Joint Encoder-Decoder Self-Supervised Pre-training for ASR—0
Context-based out-of-vocabulary word recovery for ASR systems in Indian languages—0
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning—0
DynaMaR: Dynamic Prompt with Mask Token Representation—0
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense RetrievalCode0
Revealing Single Frame Bias for Video-and-Language LearningCode2
Making Large Language Models Better Reasoners with Step-Aware Verifier—0
Pretrained Models for Multilingual Federated LearningCode1
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal RegressionCode1
ID-Agnostic User Behavior Pre-training for Sequential Recommendation—0
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved NegativesCode1
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified