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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 101125 of 17610 papers

TitleStatusHype
FlashAttention-2: Faster Attention with Better Parallelism and Work PartitioningCode6
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-AwarenessCode6
CodeGen: An Open Large Language Model for Code with Multi-Turn Program SynthesisCode6
FinGPT: Open-Source Financial Large Language ModelsCode6
Efficient Memory Management for Large Language Model Serving with PagedAttentionCode6
Extending Context Window of Large Language Models via Positional InterpolationCode6
Simple and Controllable Music GenerationCode6
A Survey of Large Language ModelsCode6
Mamba: Linear-Time Sequence Modeling with Selective State SpacesCode6
Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsCode6
QLoRA: Efficient Finetuning of Quantized LLMsCode6
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model SocietyCode6
Efficient Guided Generation for Large Language ModelsCode6
SGLang: Efficient Execution of Structured Language Model ProgramsCode6
ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming LanguagesCode6
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across LanguagesCode6
Qwen Technical ReportCode6
Direct Preference Optimization: Your Language Model is Secretly a Reward ModelCode6
Pythia: A Suite for Analyzing Large Language Models Across Training and ScalingCode6
DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal AttentionCode5
DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZCode5
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language ModelsCode5
InstructPix2Pix: Learning to Follow Image Editing InstructionsCode5
NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training ParadigmsCode5
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge AdaptationCode5
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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