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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9551–9600 of 17610 papers

TitleStatusHype
Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!Code1
TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity RecognitionCode0
Leveraging Explicit Procedural Instructions for Data-Efficient Action Prediction—0
On the Difference of BERT-style and CLIP-style Text EncodersCode1
Automatic Assessment of Oral Reading Accuracy for Reading Diagnostics—0
A generative framework for conversational laughter: Its 'language model' and laughter sound synthesis—0
Iterative Translation Refinement with Large Language Models—0
Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias—0
Semantically-Prompted Language Models Improve Visual Descriptions—0
AutoScrum: Automating Project Planning Using Large Language ModelsCode1
Information Flow Control in Machine Learning through Modular Model Architecture—0
CoSiNES: Contrastive Siamese Network for Entity Standardization—0
A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models—0
Cheap-fake Detection with LLM using Prompt Engineering—0
Improving Conversational Recommendation Systems via Counterfactual Data SimulationCode1
CTRL: Connect Collaborative and Language Model for CTR Prediction—0
Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model—0
CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels—0
Benchmarking Middle-Trained Language Models for Neural Search—0
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local SearchCode1
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video UnderstandingCode4
PolyVoice: Language Models for Speech to Speech Translation—0
On "Scientific Debt" in NLP: A Case for More Rigour in Language Model Pre-Training Research—0
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight CompressionCode2
Sequential Monte Carlo Steering of Large Language Models using Probabilistic ProgramsCode1
User Behavior Simulation with Large Language Model based AgentsCode2
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language—0
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications—0
Efficient GPT Model Pre-training using Tensor Train Matrix Representation—0
Evaluation of AI Chatbots for Patient-Specific EHR Questions—0
Large Language Model Augmented Narrative Driven RecommendationsCode0
RadLing: Towards Efficient Radiology Report Understanding—0
Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Exploring and Verbalizing Academic Ideas by Concept Co-occurrenceCode0
Utilizing ChatGPT to Enhance Clinical Trial Enrollment—0
Fine-Grained Human Feedback Gives Better Rewards for Language Model TrainingCode2
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment GenerationCode1
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation—0
Evaluating Language Models for Mathematics through InteractionsCode1
Harnessing large-language models to generate private synthetic text—0
Data-Efficient French Language Modeling with CamemBERTa—0
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?—0
Fair multilingual vandalism detection system for WikipediaCode0
EmoUS: Simulating User Emotions in Task-Oriented Dialogues—0
Log Parsing: How Far Can ChatGPT Go?Code1
LIC-GAN: Language Information Conditioned Graph Generative GAN Model—0
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction—0
Light Coreference Resolution for Russian with Hierarchical Discourse FeaturesCode0
Probabilistic Adaptation of Text-to-Video Models—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