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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 50265050 of 17610 papers

TitleStatusHype
Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis0
Test-Time Fairness and Robustness in Large Language Models0
MambaLRP: Explaining Selective State Space Sequence ModelsCode1
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing0
Scaling Large Language Model-based Multi-Agent CollaborationCode1
TernaryLLM: Ternarized Large Language Model0
Scholarly Question Answering using Large Language Models in the NFDI4DataScience GatewayCode0
World Models with Hints of Large Language Models for Goal Achieving0
Teaching Language Models to Self-Improve by Learning from Language Feedback0
VersiCode: Towards Version-controllable Code GenerationCode1
PLUM: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases0
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language ModelCode0
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad PredictionCode0
HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination EvaluationCode0
Evolving Subnetwork Training for Large Language ModelsCode0
UVIS: Unsupervised Video Instance Segmentation0
Simple and Effective Masked Diffusion Language ModelsCode4
Multi-objective Reinforcement learning from AI FeedbackCode0
Beyond Bare Queries: Open-Vocabulary Object Grounding with 3D Scene Graph0
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language ModelingCode4
RS-Agent: Automating Remote Sensing Tasks through Intelligent AgentCode2
Towards Signal Processing In Large Language Models0
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific LiteratureCode1
TRINS: Towards Multimodal Language Models that Can ReadCode0
NarrativeBridge: Enhancing Video Captioning with Causal-Temporal Narrative0
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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