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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 5901–5925 of 17610 papers

TitleStatusHype
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model—0
MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter ExpertsCode5
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision—0
ChimpVLM: Ethogram-Enhanced Chimpanzee Behaviour Recognition—0
On Speculative Decoding for Multimodal Large Language Models—0
CUDA-Accelerated Soft Robot Neural Evolution with Large Language Model Supervision—0
Measuring the Quality of Answers in Political Q&As with Large Language Models—0
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied AgentsCode1
The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential RecommendationCode1
LLM-Seg: Bridging Image Segmentation and Large Language Model ReasoningCode2
Training a Vision Language Model as Smartphone Assistant—0
Thematic Analysis with Large Language Models: does it work with languages other than English? A targeted test in Italian—0
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs—0
Inheritune: Training Smaller Yet More Attentive Language ModelsCode2
Pretraining and Updates of Domain-Specific LLM: A Case Study in the Japanese Business Domain—0
Toward a Theory of Tokenization in LLMs—0
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation—0
Emerging Property of Masked Token for Effective Pre-training—0
Language Model Prompt Selection via Simulation Optimization—0
The Future of Scientific Publishing: Automated Article Generation—0
Human Latency Conversational Turns for Spoken Avatar Systems—0
Introducing L2M3, A Multilingual Medical Large Language Model to Advance Health Equity in Low-Resource Regions—0
CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes—0
A Multi-Expert Large Language Model Architecture for Verilog Code Generation—0
Audio Dialogues: Dialogues dataset for audio and music understanding—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified