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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 46014650 of 17610 papers

TitleStatusHype
Can (A)I Change Your Mind?Code0
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity RecognitionCode0
FOSI: Hybrid First and Second Order OptimizationCode0
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data VisualizationCode0
Can AI Relate: Testing Large Language Model Response for Mental Health SupportCode0
DATETIME: A new benchmark to measure LLM translation and reasoning capabilitiesCode0
An Evalutation of Programming Language Models' performance on Software Defect DetectionCode0
FPT: Feature Prompt Tuning for Few-shot Readability AssessmentCode0
Can a large language model be a gaslighter?Code0
Can a Large Language Model Learn Matrix Functions In Context?Code0
A Transformer with Stack AttentionCode0
FRAGE: Frequency-Agnostic Word RepresentationCode0
Attacks on Third-Party APIs of Large Language ModelsCode0
Fraternal DropoutCode0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Attention as a Guide for Simultaneous Speech TranslationCode0
A Deep Generative Model for Fragment-Based Molecule GenerationCode0
A deep language model for software codeCode0
Frequency Is What You Need: Word-frequency Masking Benefits Vision-Language Model Pre-trainingCode0
FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story VideosCode0
From Alignment to Entailment: A Unified Textual Entailment Framework for Entity AlignmentCode0
Debiasing Pre-Trained Language Models via Efficient Fine-TuningCode0
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language RepresentationCode0
Language Models with TransformersCode0
From Captions to Visual Concepts and BackCode0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
Can ChatGPT's Responses Boost Traditional Natural Language Processing?Code0
Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairsCode0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
From Discrimination to Generation: Knowledge Graph Completion with Generative TransformerCode0
From Disfluency Detection to Intent Detection and Slot FillingCode0
From Distributional to Overton Pluralism: Investigating Large Language Model AlignmentCode0
From English to ASIC: Hardware Implementation with Large Language ModelCode0
From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense ConsistencyCode0
A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLPCode0
From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image SegmentationCode0
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of TransformersCode0
A Japanese Masked Language Model for Academic DomainCode0
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic SystemsCode0
Can discrete information extraction prompts generalize across language models?Code0
From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language QueriesCode0
Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase ExtractionCode0
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMsCode0
From Machine Translation to Code-Switching: Generating High-Quality Code-Switched TextCode0
Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social MediaCode0
From Markov to Laplace: How Mamba In-Context Learns Markov ChainsCode0
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language UnderstandingCode0
An Exploration of Softmax Alternatives Belonging to the Spherical Loss FamilyCode0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
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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