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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 36513700 of 17610 papers

TitleStatusHype
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
Spectral Editing of Activations for Large Language Model AlignmentCode1
Copy Is All You NeedCode1
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine TranslationCode1
AMPERSAND: Argument Mining for PERSuAsive oNline DiscussionsCode1
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERCode1
SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language ModelCode1
SpeechLMScore: Evaluating speech generation using speech language modelCode1
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
SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space ModelsCode1
Automatic Controllable Product Copywriting for E-CommerceCode1
BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?Code1
SPRec: Leveraging Self-Play to Debias Preference Alignment for Large Language Model-based RecommendationsCode1
SPRING: Studying the Paper and Reasoning to Play GamesCode1
SQ-LLaVA: Self-Questioning for Large Vision-Language AssistantCode1
SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal BudgetCode1
Stabilizing Transformers for Reinforcement LearningCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model InfillingCode1
Stack Attention: Improving the Ability of Transformers to Model Hierarchical PatternsCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
Starbucks: Improved Training for 2D Matryoshka EmbeddingsCode1
SentenceMIM: A Latent Variable Language ModelCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
CrAM: A Compression-Aware MinimizerCode1
Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less ReasonableCode1
Steering Language Model to Stable Speech Emotion Recognition via Contextual Perception and Chain of ThoughtCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security InspectionCode1
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language ModelingCode1
DeViL: Decoding Vision features into LanguageCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
BERT got a Date: Introducing Transformers to Temporal TaggingCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Dynamic Grained Encoder for Vision TransformersCode1
BERT Goes Shopping: Comparing Distributional Models for Product RepresentationsCode1
Style Vectors for Steering Generative Large Language ModelCode1
Stylometric Detection of AI-Generated Text in Twitter TimelinesCode1
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language ModelsCode1
Dynamic Contextualized Word EmbeddingsCode1
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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