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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 25012550 of 17610 papers

TitleStatusHype
Decoding Speculative DecodingCode1
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Inverse Materials Design by Large Language Model-Assisted Generative FrameworkCode1
CAB: Comprehensive Attention Benchmarking on Long Sequence ModelingCode1
Decoupled Visual Interpretation and Linguistic Reasoning for Math Problem SolvingCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied AgentsCode1
The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?Code1
CrAM: A Compression-Aware MinimizerCode1
InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply ChainsCode1
Deep contextualized word representationsCode1
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
Accurate Retraining-free Pruning for Pretrained Encoder-based Language ModelsCode1
CAFe: Unifying Representation and Generation with Contrastive-Autoregressive FinetuningCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Knowledge Rumination for Pre-trained Language ModelsCode1
DeepInception: Hypnotize Large Language Model to Be JailbreakerCode1
Cal-DPO: Calibrated Direct Preference Optimization for Language Model AlignmentCode1
Invariant Language ModelingCode1
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution DataCode1
Inverse Constitutional AI: Compressing Preferences into PrinciplesCode1
Investigating Fairness Disparities in Peer Review: A Language Model Enhanced ApproachCode1
Kosmos-2: Grounding Multimodal Large Language Models to the WorldCode1
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpusCode1
IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code GeneratorsCode1
Breaking the HISCO Barrier: Automatic Occupational Standardization with OccCANINECode1
CPT: Efficient Deep Neural Network Training via Cyclic PrecisionCode1
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
Interpreting Language Models Through Knowledge Graph ExtractionCode1
Interpreting Language Models with Contrastive ExplanationsCode1
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from TextCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
Interpretation of Intracardiac Electrograms Through Textual RepresentationsCode1
DeepStruct: Pretraining of Language Models for Structure PredictionCode1
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language ModelCode1
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing AnomaliesCode1
Camoscio: an Italian Instruction-tuned LLaMACode1
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningCode1
DeltaZip: Efficient Serving of Multiple Full-Model-Tuned LLMsCode1
Can AI-Generated Text be Reliably Detected?Code1
LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health RecordsCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
Democratizing Reasoning Ability: Tailored Learning from Large Language ModelCode1
Interpretable Language Modeling via Induction-head Ngram ModelsCode1
BreakGPT: A Large Language Model with Multi-stage Structure for Financial Breakout DetectionCode1
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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