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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 35513600 of 17610 papers

TitleStatusHype
Copy Suppression: Comprehensively Understanding an Attention HeadCode1
Effective Batching for Recurrent Neural Network GrammarsCode1
Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer LearningCode1
DeLighT: Deep and Light-weight TransformerCode1
Contrastive Chain-of-Thought PromptingCode1
SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in HuBERTCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive PruningCode1
Contrastive Distillation on Intermediate Representations for Language Model CompressionCode1
A Neural Algorithm of Artistic StyleCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model GenerationCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
Contrastive Learning for Prompt-Based Few-Shot Language LearnersCode1
See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual ReasoningCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
DELIFT: Data Efficient Language model Instruction Fine TuningCode1
Contrastive Learning with Hard Negative Entities for Entity Set ExpansionCode1
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical ReasoningCode1
Selective Differential Privacy for Language ModelingCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
An Engorgio Prompt Makes Large Language Model Babble onCode1
BERTweet: A pre-trained language model for English TweetsCode1
DynaPipe: Optimizing Multi-task Training through Dynamic PipelinesCode1
SelfElicit: Your Language Model Secretly Knows Where is the Relevant EvidenceCode1
DziriBERT: a Pre-trained Language Model for the Algerian DialectCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Controllable Generation from Pre-trained Language Models via Inverse PromptingCode1
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model EvaluationCode1
BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text GenerationCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
Copy Is All You NeedCode1
Controllable Sentence Simplification with a Unified Text-to-Text Transfer TransformerCode1
Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language ModelingCode1
Self-supervised vision-language pretraining for Medical visual question answeringCode1
Controllable Text Generation with Neurally-Decomposed OracleCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
Semantic HELM: A Human-Readable Memory for Reinforcement LearningCode1
Effective Attention Sheds Light On InterpretabilityCode1
Controlled Text Generation as Continuous Optimization with Multiple ConstraintsCode1
Semi-supervised Multitask Learning for Sequence LabelingCode1
Controlled Text Generation for Large Language Model with Dynamic Attribute GraphsCode1
DUnE: Dataset for Unified EditingCode1
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal LearningCode1
DuplexMamba: Enhancing Real-time Speech Conversations with Duplex and Streaming CapabilitiesCode1
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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