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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7401–7450 of 17610 papers

TitleStatusHype
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental LearningCode1
PLLaMa: An Open-source Large Language Model for Plant ScienceCode1
Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control—0
Cheetah: Natural Language Generation for 517 African LanguagesCode0
Efficient Parallel Audio Generation using Group Masked Language Modeling—0
Discovering Significant Topics from Legal Decisions with Selective Inference—0
BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving—0
DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever—0
Quokka: An Open-source Large Language Model ChatBot for Material ScienceCode1
PeVL: Pose-Enhanced Vision-Language Model for Fine-Grained Human Action Recognition—0
Jack of All Tasks Master of Many: Designing General-Purpose Coarse-to-Fine Vision-Language Model—0
General Point Model Pretraining with Autoencoding and AutoregressiveCode0
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation—0
Revisiting Counterfactual Problems in Referring Expression ComprehensionCode0
Few-Shot Object Detection with Foundation Models—0
Pixel-Aligned Language Model—0
LION: Empowering Multimodal Large Language Model with Dual-Level Visual KnowledgeCode2
On Scaling Up a Multilingual Vision and Language Model—0
Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior—0
Unknown Prompt the only Lacuna: Unveiling CLIP's Potential for Open Domain GeneralizationCode1
Discovering Syntactic Interaction Clues for Human-Object Interaction Detection—0
Querying as Prompt: Parameter-Efficient Learning for Multimodal Language Model—0
AssistGUI: Task-Oriented PC Graphical User Interface Automation—0
Disentangled Prompt Representation for Domain Generalization—0
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training—0
Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education—0
Searching, fast and slow, through product catalogs—0
Large language model for Bible sentiment analysis: Sermon on the MountCode0
Predicting Anti-microbial Resistance using Large Language Models—0
Large Language Models aren't all that you need—0
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training—0
HSC-GPT: A Large Language Model for Human Settlements Construction—0
SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent DetectionCode1
GeoGalactica: A Scientific Large Language Model in GeoscienceCode1
DocLLM: A layout-aware generative language model for multimodal document understanding—0
Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition—0
Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled SetsCode0
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws—0
Trace and Edit Relation Associations in GPT—0
Open-TI: Open Traffic Intelligence with Augmented Language ModelCode1
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge GraphCode0
Boosting Large Language Model for Speech Synthesis: An Empirical Study—0
Tracking with Human-Intent ReasoningCode1
MosaicBERT: A Bidirectional Encoder Optimized for Fast PretrainingCode2
Principled Gradient-based Markov Chain Monte Carlo for Text Generation—0
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning—0
State Machine of Thoughts: Leveraging Past Reasoning Trajectories for Enhancing Problem Solving—0
DrugAssist: A Large Language Model for Molecule OptimizationCode1
Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation—0
TinyGPT-V: Efficient Multimodal Large Language Model via Small BackbonesCode3
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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