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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9251–9300 of 17610 papers

TitleStatusHype
Fast Quantum Algorithm for Attention Computation—0
Planting a SEED of Vision in Large Language ModelCode2
Language Conditioned Traffic GenerationCode1
The Potential and Pitfalls of using a Large Language Model such as ChatGPT or GPT-4 as a Clinical Assistant—0
Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language ModellingCode2
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge GraphCode2
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning—0
Transformers are Universal Predictors—0
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from TextCode1
Drive Like a Human: Rethinking Autonomous Driving with Large Language ModelsCode2
HYTREL: Hypergraph-enhanced Tabular Data Representation LearningCode1
Gloss Attention for Gloss-free Sign Language TranslationCode1
Improving BERT with Hybrid Pooling Network and Drop Mask—0
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis—0
Population Expansion for Training Language Models with Private Federated Learning—0
MorphPiece : A Linguistic Tokenizer for Large Language Models—0
Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section—0
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?—0
Generating Benchmarks for Factuality Evaluation of Language ModelsCode2
Copy Is All You NeedCode1
In-context Autoencoder for Context Compression in a Large Language ModelCode1
Electoral Agitation Data Set: The Use Case of the Polish ElectionCode0
Instruction Mining: Instruction Data Selection for Tuning Large Language Models—0
Transformers in Reinforcement Learning: A Survey—0
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language ModelsCode2
PolyLM: An Open Source Polyglot Large Language Model—0
Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems—0
Model Card and Evaluations for Claude Models—0
Lightweight reranking for language model generations—0
OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-LearningCode1
SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue Summarization—0
Epidemic Modeling with Generative AgentsCode1
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization—0
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation—0
Linear Alignment of Vision-language Models for Image CaptioningCode1
Exploring Large Language Model for Graph Data Understanding in Online Job RecommendationsCode1
Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training—0
Text Descriptions are Compressive and Invariant Representations for Visual Learning—0
Natural Language Instructions for Intuitive Human Interaction with Robotic Assistants in Field Construction Work—0
Assessing the efficacy of large language models in generating accurate teacher responses—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
ChatGPT in the Age of Generative AI and Large Language Models: A Concise SurveyCode1
Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes—0
Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generatorsCode1
ScriptWorld: Text Based Environment For Learning Procedural KnowledgeCode1
On decoder-only architecture for speech-to-text and large language model integration—0
Bidirectional Attention as a Mixture of Continuous Word ExpertsCode0
Masked Latent Semantic Modeling: an Efficient Pre-training Alternative to Masked Language ModelingCode0
Procedurally generating rules to adapt difficulty for narrative puzzle games—0
LaunchpadGPT: Language Model as Music Visualization Designer on LaunchpadCode1
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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