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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
Extracting Latent Steering Vectors from Pretrained Language ModelsCode1
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language TranslationCode1
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer LearningCode1
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language ModelsCode1
Exploring the impact of low-rank adaptation on the performance, efficiency, and regularization of RLHFCode1
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete LabelsCode1
Exploring the Limits of Language ModelingCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
Exploring Large Language Model for Graph Data Understanding in Online Job RecommendationsCode1
Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic SegmentationCode1
Exploring and Predicting Transferability across NLP TasksCode1
Exploring Quantization for Efficient Pre-Training of Transformer Language ModelsCode1
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous DrivingCode1
Factorized Neural Transducer for Efficient Language Model AdaptationCode1
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular DataCode1
Explaining Answers with Entailment TreesCode1
Argmax Flows and Multinomial Diffusion: Learning Categorical DistributionsCode1
Explaining Datasets in Words: Statistical Models with Natural Language ParametersCode1
ExpertQA: Expert-Curated Questions and Attributed AnswersCode1
Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space TranslationCode1
An Analysis and Mitigation of the Reversal CurseCode1
Excuse me, sir? Your language model is leaking (information)Code1
ExaRanker: Explanation-Augmented Neural RankerCode1
Breaking the HISCO Barrier: Automatic Occupational Standardization with OccCANINECode1
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model CommunicationCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language InferenceCode1
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language TasksCode1
Emergent Representations of Program Semantics in Language Models Trained on ProgramsCode1
Event Causality Identification via Derivative Prompt Joint LearningCode1
Acoustic Prompt Tuning: Empowering Large Language Models with Audition CapabilitiesCode1
Evolutionary Large Language Model for Automated Feature TransformationCode1
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM FamilyCode1
Evaluation Benchmarks for Spanish Sentence RepresentationsCode1
Evaluation of large language models for discovery of gene set functionCode1
Does It Make Sense? And Why? A Pilot Study for Sense Making and ExplanationCode1
Are Large Pre-Trained Language Models Leaking Your Personal Information?Code1
Evaluating the Robustness of Retrieval Pipelines with Query Variation GeneratorsCode1
Evolving Deep Neural NetworksCode1
Exploiting Novel GPT-4 APIsCode1
Evaluating Language Models for Mathematics through InteractionsCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
Evaluating Language Model Finetuning Techniques for Low-resource LanguagesCode1
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation MethodCode1
Mathfish: Evaluating Language Model Math Reasoning via Grounding in Educational CurriculaCode1
Evaluating Morphological Alignment of Tokenizers in 70 LanguagesCode1
Evaluating Human-Language Model InteractionCode1
Evaluating Attribution in Dialogue Systems: The BEGIN BenchmarkCode1
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability TreesCode1
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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