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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6951–6975 of 17610 papers

TitleStatusHype
SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation—0
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search—0
SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline—0
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing—0
SCORPIO: Serving the Right Requests at the Right Time for Heterogeneous SLOs in LLM Inference—0
Script Induction as Language Modeling—0
Script knowledge constrains ellipses in fragments – Evidence from production data and language modeling—0
SCRIPT: Self-Critic PreTraining of Transformers—0
Linking-Enhanced Pre-Training for Table Semantic Parsing—0
SDoH-GPT: Using Large Language Models to Extract Social Determinants of Health (SDoH)—0
Seal: Advancing Speech Language Models to be Few-Shot Learners—0
SEAL: SEmantic-Augmented Imitation Learning via Language Model—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
SeamlessExpressiveLM: Speech Language Model for Expressive Speech-to-Speech Translation with Chain-of-Thought—0
Searching, fast and slow, through product catalogs—0
Searching for Efficient Transformers for Language Modeling—0
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability—0
SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs—0
SecEncoder: Logs are All You Need in Security—0
Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment—0
Secret Use of Large Language Model (LLM)—0
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification—0
Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions—0
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition—0
SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs—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