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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 36013650 of 17610 papers

TitleStatusHype
Effective Attention Sheds Light On InterpretabilityCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
Sequential Recommendation with Latent Relations based on Large Language ModelCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
An Engorgio Prompt Makes Large Language Model Babble onCode1
Describing Differences in Image Sets with Natural LanguageCode1
Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree SearchCode1
Automated Spinal MRI Labelling from Reports Using a Large Language ModelCode1
BERTweet: A pre-trained language model for English TweetsCode1
Should You Mask 15% in Masked Language Modeling?Code1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text GenerationCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Counterfactual Data Augmentation for Neural Machine TranslationCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
ELECTRAMed: a new pre-trained language representation model for biomedical NLPCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
Dynamic Grained Encoder for Vision TransformersCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine TranslationCode1
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales DialogueCode1
SimVLM: Simple Visual Language Model Pretraining with Weak SupervisionCode1
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERCode1
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language ModelCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
BERT Loses Patience: Fast and Robust Inference with Early ExitCode1
BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QACode1
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language ModelsCode1
BERTje: A Dutch BERT ModelCode1
BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?Code1
cosFormer: Rethinking Softmax in AttentionCode1
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant WeightsCode1
DUnE: Dataset for Unified EditingCode1
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal LearningCode1
DuplexMamba: Enhancing Real-time Speech Conversations with Duplex and Streaming CapabilitiesCode1
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
SentenceMIM: A Latent Variable Language ModelCode1
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural NetworksCode1
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image SegmentationCode1
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language ModelsCode1
SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity PredictionCode1
Dual Learning with Dynamic Knowledge Distillation for Partially Relevant Video RetrievalCode1
DUMA: Reading Comprehension with Transposition ThinkingCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
Dual-Alignment Pre-training for Cross-lingual Sentence EmbeddingCode1
Dual-path CNN with Max Gated block for Text-Based Person Re-identificationCode1
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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