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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10151–10200 of 17610 papers

TitleStatusHype
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence GenerationCode0
Toucan: Token-Aware Character Level Language Modeling—0
Improving Deep Learning Optimization through Constrained Parameter RegularizationCode0
PsyEval: A Suite of Mental Health Related Tasks for Evaluating Large Language ModelsCode0
Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers—0
Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder—0
User Persona Identification and New Service Adaptation Recommendation—0
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects—0
When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource LanguagesCode0
Summarization-Based Document IDs for Generative Retrieval with Language ModelsCode0
Automated title and abstract screening for scoping reviews using the GPT-4 Large Language ModelCode0
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios—0
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncodersCode0
How good are Large Language Models on African Languages?—0
A Survey of Confidence Estimation and Calibration in Large Language Models—0
Anti-LM Decoding for Zero-shot In-context Machine TranslationCode0
Large Language Model-Driven Classroom Flipping: Empowering Student-Centric Peer Questioning with Flipped Interaction—0
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning—0
Memory-efficient Stochastic methods for Memory-based TransformersCode0
Text Retrieval with Multi-Stage Re-Ranking ModelsCode0
On the Discussion of Large Language Models: Symmetry of Agents and Interplay with Prompts—0
To Tell The Truth: Language of Deception and Language ModelsCode0
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games—0
On Elastic Language Models—0
On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models—0
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings—0
The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4—0
Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image RetrievalCode0
Reducing the Need for Backpropagation and Discovering Better Optima With Explicit Optimizations of Neural Networks—0
ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models—0
Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions—0
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning—0
Controlled Text Generation for Black-box Language Models via Score-based Progressive EditorCode0
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided SearchCode0
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising—0
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference—0
Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model—0
Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding—0
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect—0
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models—0
Tunable Soft Prompts are Messengers in Federated Learning—0
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System—0
Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in textCode0
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models—0
Intentional Biases in LLM Responses—0
Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study—0
Language Models can be Logical Solvers—0
Model-as-a-Service (MaaS): A Survey—0
Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking—0
Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit LogsCode0
Show:102550
← PrevPage 204 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