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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 3401–3425 of 17610 papers

TitleStatusHype
Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent ExplorationCode4
Neutral residues: revisiting adapters for model extension—0
Selective Attention Improves Transformer—0
FAN: Fourier Analysis NetworksCode3
Morphological evaluation of subwords vocabulary used by BETO language model—0
Discovering Spoofing Attempts on Language Model WatermarksCode0
Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce—0
Long-range gene expression prediction with token alignment of large language model—0
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model CompressionCode1
A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model—0
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific TopicsCode0
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
TypedThinker: Typed Thinking Improves Large Language Model ReasoningCode0
OCC-MLLM-Alpha:Empowering Multi-modal Large Language Model for the Understanding of Occluded Objects with Self-Supervised Test-Time Learning—0
Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker—0
Racing Thoughts: Explaining Contextualization Errors in Large Language Models—0
EMMA: Efficient Visual Alignment in Multi-Modal LLMsCode1
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
FARM: Functional Group-Aware Representations for Small Molecules—0
LS-HAR: Language Supervised Human Action Recognition with Salient Fusion, Construction Sites as a Use-Case—0
Automatic deductive coding in discourse analysis: an application of large language models in learning analyticsCode0
Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads on Consumer-Grade DevicesCode1
Frozen Large Language Models Can Perceive Paralinguistic Aspects of Speech—0
Mind Scramble: Unveiling Large Language Model Psychology Via TypoglycemiaCode0
OCC-MLLM:Empowering Multimodal Large Language Model For the Understanding of Occluded Objects—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