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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9751–9800 of 17610 papers

TitleStatusHype
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchCode0
Dynamic Masking Rate Schedules for MLM Pretraining—0
Estimating class separability of text embeddings with persistent homology—0
An Efficient Multilingual Language Model Compression through Vocabulary TrimmingCode1
C-STS: Conditional Semantic Textual SimilarityCode1
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic SystemsCode0
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language ModelCode0
Trade-Offs Between Fairness and Privacy in Language ModelingCode0
Textless Speech-to-Speech Translation With Limited Parallel DataCode0
Focus Your Attention (with Adaptive IIR Filters)—0
Mitigating Test-Time Bias for Fair Image RetrievalCode0
QLoRA: Efficient Finetuning of Quantized LLMsCode6
Regex-augmented Domain Transfer Topic Classification based on a Pre-trained Language Model: An application in Financial Domain—0
Natural Language Decompositions of Implicit Content Enable Better Text RepresentationsCode0
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement LearningCode1
Language Model Self-improvement by Reinforcement Learning Contemplation—0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning ProblemsCode1
On Robustness of Finetuned Transformer-based NLP ModelsCode0
From Characters to Words: Hierarchical Pre-trained Language Model for Open-vocabulary Language Understanding—0
Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding—0
Cascaded Beam Search: Plug-and-Play Terminology-Forcing For Neural Machine Translation—0
FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual ModelsCode1
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction—0
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language TasksCode0
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate ModelsCode0
Domain Private Transformers for Multi-Domain Dialog SystemsCode0
Goal-Driven Explainable Clustering via Language DescriptionsCode1
Error Detection for Text-to-SQL Semantic ParsingCode0
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text EmbeddingsCode1
AxomiyaBERTa: A Phonologically-aware Transformer Model for AssameseCode0
APPLS: Evaluating Evaluation Metrics for Plain Language SummarizationCode0
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation—0
Aligning Large Language Models through Synthetic FeedbackCode1
CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language ModelCode1
When your Cousin has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced LanguagesCode0
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-rankerCode0
Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models—0
GenSpectrum Chat: Data Exploration in Public Health Using Large Language Models—0
Query Rewriting for Retrieval-Augmented Large Language Models—0
Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger DetectionCode0
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-trainingCode2
Learning from Mistakes via Cooperative Study Assistant for Large Language ModelsCode0
R2H: Building Multimodal Navigation Helpers that Respond to Help Requests—0
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language ModelsCode0
Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings—0
Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model—0
Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy PlanningCode1
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language ModelsCode2
Show:102550
← PrevPage 196 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