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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 72017250 of 17610 papers

TitleStatusHype
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines0
ShufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models0
Shushing! Let's Imagine an Authentic Speech from the Silent Video0
Tempest: Autonomous Multi-Turn Jailbreaking of Large Language Models with Tree Search0
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data0
Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words0
SignLLM: Sign Language Production Large Language Models0
SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning0
Sigsoftmax: Reanalysis of the Softmax Bottleneck0
SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning0
Silver-Tongued and Sundry: Exploring Intersectional Pronouns with ChatGPT0
SimCT: A Simple Consistency Test Protocol in LLMs Development Lifecycle0
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs0
Similarity-Aware Multimodal Prompt Learning for Fake News Detection0
Similarity Guided Multimodal Fusion Transformer for Semantic Location Prediction in Social Media0
Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model0
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval0
Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody0
Simple and Scalable Strategies to Continually Pre-train Large Language Models0
SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words0
SimpleBooks: Long-term dependency book dataset with simplified English vocabulary for word-level language modeling0
Simple Construction of Mixed-Language Texts for Vocabulary Learning0
Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies0
Simple Feedfoward Neural Networks are Almost All You Need for Time Series Forecasting0
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions0
Simple Image Description Generator via a Linear Phrase-Based Approach0
SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving0
SIMPLEMIX: Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference Learning0
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation0
SimpleStrat: Diversifying Language Model Generation with Stratification0
Simple Tagging System with RoBERTa for Ancient Chinese0
Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings0
Simple Token-Level Confidence Improves Caption Correctness0
Simplified End-to-End MMI Training and Voting for ASR0
Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases0
Simplifying Formal Proof-Generating Models with ChatGPT and Basic Searching Techniques0
Simplifying Multimodality: Unimodal Approach to Multimodal Challenges in Radiology with General-Domain Large Language Model0
Simplifying the Bible and Wikipedia Using Statistical Machine Translation0
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions0
Simulating Filter Bubble on Short-video Recommender System with Large Language Model Agents0
Simulating Financial Market via Large Language Model based Agents0
Simulating H.P. Lovecraft horror literature with the ChatGPT large language model0
Simulating Human-like Daily Activities with Desire-driven Autonomy0
Simulating Macroeconomic Expectations using LLM Agents0
Simulating realistic speech overlaps improves multi-talker ASR0
Simultaneous Reward Distillation and Preference Learning: Get You a Language Model Who Can Do Both0
SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian0
SINA-BERT: A Pre-Trained Language Model for Analysis of Medical Texts in Persian0
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard0
Single-Read Reconstruction for DNA Data Storage Using Transformers0
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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