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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 44514500 of 17610 papers

TitleStatusHype
A Hybrid Convolutional Variational Autoencoder for Text GenerationCode0
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language ModelCode0
Cross-lingual Language Model PretrainingCode0
A Hybrid GA LLM Framework for Structured Task OptimizationCode0
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder ParadigmCode0
Breaking the Silence: the Threats of Using LLMs in Software EngineeringCode0
Breaking the Softmax Bottleneck: A High-Rank RNN Language ModelCode0
Cross-lingual Similarity of Multilingual Representations RevisitedCode0
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment ClassificationCode0
Cross-Lingual Speaker Identification Using Distant SupervisionCode0
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-trainingCode0
Breaking Time Invariance: Assorted-Time Normalization for RNNsCode0
An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding TasksCode0
An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual RepresentationCode0
BRENT: Bidirectional Retrieval Enhanced Norwegian TransformerCode0
A Comparative Study on Language Models for Task-Oriented Dialogue SystemsCode0
A Survey for Biomedical Text Summarization: From Pre-trained to Large Language ModelsCode0
AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene UnderstandingCode0
Fast Multipole Attention: A Divide-and-Conquer Attention Mechanism for Long SequencesCode0
Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User ControlCode0
Fast-Slow Recurrent Neural NetworksCode0
Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-TuningCode0
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix TreesCode0
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language ModelCode0
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-trainingCode0
Fast Training of Recurrent Neural Networks with Stationary State FeedbacksCode0
Fast transcription of speech in low-resource languagesCode0
FastTrees: Parallel Latent Tree-Induction for Faster Sequence EncodingCode0
Bridging Information-Theoretic and Geometric Compression in Language ModelsCode0
A Survey on Large Language Model based Human-Agent SystemsCode0
Cross-Modal Cloze Task: A New Task to Brain-to-Word DecodingCode0
Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language ModelsCode0
Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image GenerationCode0
A Hybrid System for Systematic Generalization in Simple Arithmetic ProblemsCode0
Cross-Refine: Improving Natural Language Explanation Generation by Learning in TandemCode0
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QACode0
An Empirical Study of Language CNN for Image CaptioningCode0
An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4Code0
Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as JudgesCode0
Federated Learning for Mobile Keyboard PredictionCode0
Federated Learning of Medical Concepts Embedding using BEHRTCode0
CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language ModelsCode0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing TasksCode0
A Data Cartography based MixUp for Pre-trained Language ModelsCode0
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual UnderstandingCode0
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial DesignCode0
Fennec: Fine-grained Language Model Evaluation and Correction Extended through Branching and BridgingCode0
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender BiasCode0
Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context ExpertsCode0
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answeringCode0
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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