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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 22012250 of 17610 papers

TitleStatusHype
Improving Transformer Optimization Through Better InitializationCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
Improving Spoken Language Modeling with Phoneme Classification: A Simple Fine-tuning ApproachCode1
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language ModelCode1
Improving Seq2Seq Grammatical Error Correction via Decoding InterventionsCode1
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic ChangeCode1
Fact-checking information from large language models can decrease headline discernmentCode1
Improving NER's Performance with Massive financial corpusCode1
Improving Neural Machine Translation Models with Monolingual DataCode1
A Critical Analysis of Biased Parsers in Unsupervised ParsingCode1
C-STS: Conditional Semantic Textual SimilarityCode1
Improving Passage Retrieval with Zero-Shot Question GenerationCode1
Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine TranslationCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little CostCode1
ArtGPT-4: Towards Artistic-understanding Large Vision-Language Models with Enhanced AdapterCode1
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign UsersCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
ARS: Automatic Routing Solver with Large Language ModelsCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Improving Language Understanding by Generative Pre-TrainingCode1
Improving Mandarin End-to-End Speech Recognition with Word N-gram Language ModelCode1
Improving Indonesian Text Classification Using Multilingual Language ModelCode1
CDLM: Cross-Document Language ModelingCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based TechniquesCode1
Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language ModelsCode1
Improving Mandarin Speech Recogntion with Block-augmented TransformerCode1
Improving Conversational Recommendation Systems via Counterfactual Data SimulationCode1
Improving End-to-End SLU performance with Prosodic Attention and DistillationCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and NegativesCode1
CriticEval: Evaluating Large Language Model as CriticCode1
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved NegativesCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta InformationCode1
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level TransferCode1
Improving Multi-Party Dialogue Discourse Parsing via Domain IntegrationCode1
Improved training of end-to-end attention models for speech recognitionCode1
Argmax Flows and Multinomial Diffusion: Learning Categorical DistributionsCode1
ImProver: Agent-Based Automated Proof OptimizationCode1
CrAM: A Compression-Aware MinimizerCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
An Analysis and Mitigation of the Reversal CurseCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Improving antibody language models with native pairingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified