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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1060110650 of 17610 papers

TitleStatusHype
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching0
Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural NetworksCode0
Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model0
You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and PersonaCode1
SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm0
Neural Codec Language Models are Zero-Shot Text to Speech SynthesizersCode7
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning0
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating ApproachCode0
t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule GenerationCode1
ClusTop: An unsupervised and integrated text clustering and topic extraction framework0
Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic ReasoningCode0
Large Language Models as Corporate LobbyistsCode1
PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora0
Understanding Political Polarisation using Language Models: A dataset and method0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring0
Analysing Discrete Self Supervised Speech Representation for Spoken Language ModelingCode1
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-ShotCode4
Muse: Text-To-Image Generation via Masked Generative TransformersCode2
PODA: Prompt-driven Zero-shot Domain AdaptationCode1
Open Set Video HOI detection from Action-Centric Chain-of-Look Prompting0
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
C2ST: Cross-Modal Contextualized Sequence Transduction for Continuous Sign Language Recognition0
Distilling DETR with Visual-Linguistic Knowledge for Open-Vocabulary Object DetectionCode1
Dual Learning with Dynamic Knowledge Distillation for Partially Relevant Video RetrievalCode1
AutoAD II: The Sequel - Who, When, and What in Movie Audio Description0
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering0
Fusing Pre-Trained Language Models With Multimodal Prompts Through Reinforcement LearningCode1
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation0
EC2: Emergent Communication for Embodied Control0
Open-Category Human-Object Interaction Pre-Training via Language Modeling Framework0
CLIPPING: Distilling CLIP-Based Models With a Student Base for Video-Language Retrieval0
Dynamic Inference With Grounding Based Vision and Language Models0
Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks0
TeViS:Translating Text Synopses to Video StoryboardsCode1
Logic Mill -- A Knowledge Navigation System0
Rethinking with Retrieval: Faithful Large Language Model InferenceCode1
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition0
How would Stance Detection Techniques Evolve after the Launch of ChatGPT?Code0
ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports0
Black-box language model explanation by context length probingCode0
Hungry Hungry Hippos: Towards Language Modeling with State Space ModelsCode2
Cramming: Training a Language Model on a Single GPU in One DayCode3
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPCode7
TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence0
Measuring an artificial intelligence agent's trust in humans using machine incentives0
Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question AlignmentCode0
Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers0
MicroBERT: Effective Training of Low-resource Monolingual BERTs through Parameter Reduction and Multitask LearningCode1
Benchmark for Uncertainty & Robustness in Self-Supervised LearningCode0
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of GeneralizationCode1
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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