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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7351–7400 of 17610 papers

TitleStatusHype
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation—0
Space Efficiencies in Discourse Modeling via Conditional Random Sampling—0
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability—0
Span Labeling Approach for Vietnamese and Chinese Word Segmentation—0
SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals—0
SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models—0
SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model—0
Sparks: Inspiration for Science Writing using Language Models—0
Sparks of Science: Hypothesis Generation Using Structured Paper Data—0
SPAR: Personalized Content-Based Recommendation via Long Engagement Attention—0
Sparse associative memory based on contextual code learning for disambiguating word senses—0
Sparse Attention with Learning to Hash—0
Sparse Auto-Encoder Interprets Linguistic Features in Large Language Models—0
Sparse Autoencoders, Again?—0
SParse: Ko University Graph-Based Parsing System for the CoNLL 2018 Shared Task—0
Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document Retrieval—0
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling—0
Sparse Mixture of Experts as Unified Competitive Learning—0
Sparse Non-negative Matrix Language Modeling—0
Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers—0
Sparse Transformer: Concentrated Attention Through Explicit Selection—0
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval—0
Spatial Audio Processing with Large Language Model on Wearable Devices—0
Spatial-Language Attention Policies for Efficient Robot Learning—0
Spatial Latent Dirichlet Allocation—0
SpatialLM: Training Large Language Models for Structured Indoor Modeling—0
SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models—0
Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction—0
Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning—0
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding—0
Spatio-temporal Storytelling? Leveraging Generative Models for Semantic Trajectory Analysis—0
SPDB Innovation Lab at SemEval-2022 Task 10: A Novel End-to-End Structured Sentiment Analysis Model based on the ERNIE-M—0
Speaker Clustering in Textual Dialogue with Utterance Correlation and Cross-corpus Dialogue Act Supervision—0
Speaker Clustering in Textual Dialogue with Pairwise Utterance Relation and Cross-corpus Dialogue Act Supervision—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment—0
SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization—0
SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths—0
SpecEE: Accelerating Large Language Model Inference with Speculative Early Exiting—0
Need a Small Specialized Language Model? Plan Early!—0
SpecServe: Efficient and SLO-Aware Large Language Model Serving with Adaptive Speculative Decoding—0
SpectraLDS: Provable Distillation for Linear Dynamical Systems—0
SpecTr: Fast Speculative Decoding via Optimal Transport—0
SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization—0
Speculative Beam Search for Simultaneous Translation—0
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion—0
Speculative Sampling via Exponential Races—0
Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models—0
Speculative Streaming: Fast LLM Inference without Auxiliary Models—0
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech—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