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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10651–10700 of 17610 papers

TitleStatusHype
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs—0
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis—0
Quantifying Semantics using Complex Network Analysis—0
Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing—0
Quantifying the Role of Textual Predictability in Automatic Speech Recognition—0
Quantifying Uncertainties in Natural Language Processing Tasks—0
Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness—0
Quantized-Dialog Language Model for Goal-Oriented Conversational Systems—0
Quantized Embedding Vectors for Controllable Diffusion Language Models—0
Quantized Neural Network Inference with Precision Batching—0
Quantized Transformer Language Model Implementations on Edge Devices—0
Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting—0
Quantum Graph Transformer for NLP Sentiment Classification—0
Quantum Language Model with Entanglement Embedding for Question Answering—0
Quantum Natural Language Processing on Near-Term Quantum Computers—0
Quantum State Preparation via Large-Language-Model-Driven Evolution—0
Quantum Transfer Learning for Acceptability Judgements—0
QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning—0
QueEn: A Large Language Model for Quechua-English Translation—0
Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models—0
Query-based summarization using MDL principle—0
Query Expansion Using Contextual Clue Sampling with Language Models—0
Query Generation with External Knowledge for Dense Retrieval—0
Querying as Prompt: Parameter-Efficient Learning for Multimodal Language Model—0
Query of CC: Unearthing Large Scale Domain-Specific Knowledge from Public Corpora—0
Query Performance Explanation through Large Language Model for HTAP Systems—0
Query Rewriting for Retrieval-Augmented Large Language Models—0
QuesBELM: A BERT based Ensemble Language Model for Natural Questions—0
QuesNet: A Unified Representation for Heterogeneous Test Questions—0
Question Answering and Question Generation for Finnish—0
Question Answering based Clinical Text Structuring Using Pre-trained Language Model—0
Question Answering over Knowledge Base using Language Model Embeddings—0
Question Aware Vision Transformer for Multimodal Reasoning—0
Question-focused Summarization by Decomposing Articles into Facts and Opinions and Retrieving Entities—0
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders—0
Quranic Verses Semantic Relatedness Using AraBERT—0
QURIOUS: Question Generation Pretraining for Text Generation—0
QWENDY: Gene Regulatory Network Inference Enhanced by Large Language Model and Transformer—0
QwenLong-CPRS: Towards -LLMs with Dynamic Context Optimization—0
Qwen vs. Gemma Integration with Whisper: A Comparative Study in Multilingual SpeechLLM Systems—0
R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model—0
R2GenCSR: Retrieving Context Samples for Large Language Model based X-ray Medical Report Generation—0
R2H: Building Multimodal Navigation Helpers that Respond to Help Requests—0
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation—0
R^3Mem: Bridging Memory Retention and Retrieval via Reversible Compression—0
RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning—0
Racing Thoughts: Explaining Contextualization Errors in Large Language Models—0
Radar Spectra-Language Model for Automotive Scene Parsing—0
RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization—0
RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models—0
Show:102550
← PrevPage 214 of 353Next →
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