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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 18011850 of 17610 papers

TitleStatusHype
Few-Shot Learning for Opinion SummarizationCode1
Image Hijacks: Adversarial Images can Control Generative Models at RuntimeCode1
ImaginaryNet: Learning Object Detectors without Real Images and AnnotationsCode1
Automated Spinal MRI Labelling from Reports Using a Large Language ModelCode1
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
FedJudge: Federated Legal Large Language ModelCode1
A Surprisingly Robust Trick for Winograd Schema ChallengeCode1
BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QACode1
Imposing Relation Structure in Language-Model Embeddings Using Contrastive LearningCode1
Improved GUI Grounding via Iterative NarrowingCode1
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot ClassificationCode1
Improved training of end-to-end attention models for speech recognitionCode1
FedScale: Benchmarking Model and System Performance of Federated Learning at ScaleCode1
Feature Structure Distillation with Centered Kernel Alignment in BERT TransferringCode1
Improving Biomedical Pretrained Language Models with KnowledgeCode1
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and NegativesCode1
Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta InformationCode1
Federated Learning for ASR based on Wav2vec 2.0Code1
Felix: Flexible Text Editing Through Tagging and InsertionCode1
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion PromptsCode1
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level TransferCode1
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based TechniquesCode1
Fast Vocabulary Transfer for Language Model CompressionCode1
Improving Mandarin Speech Recogntion with Block-augmented TransformerCode1
Improving NER's Performance with Massive financial corpusCode1
Improving Neural Machine Translation Models with Monolingual DataCode1
AMPERSAND: Argument Mining for PERSuAsive oNline DiscussionsCode1
Improving Seq2Seq Grammatical Error Correction via Decoding InterventionsCode1
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic ChangeCode1
Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine TranslationCode1
AMR Parsing via Graph-Sequence Iterative InferenceCode1
Automatic Controllable Product Copywriting for E-CommerceCode1
Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery ModelsCode1
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
Salmon: A Suite for Acoustic Language Model EvaluationCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little CostCode1
FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging FrameworkCode1
In-context Autoencoder for Context Compression in a Large Language ModelCode1
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular DataCode1
Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less ReasonableCode1
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense RetrievalCode1
Incorporating Clinical Guidelines through Adapting Multi-modal Large Language Model for Prostate Cancer PI-RADS ScoringCode1
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout GroupsCode1
Automatic Evaluation of Attribution by Large Language ModelsCode1
A Large Scale Search Dataset for Unbiased Learning to RankCode1
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary InitializationCode1
Inductive Relation Prediction by BERTCode1
Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code GenerationCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model CompressionCode1
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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