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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 6651–6700 of 17610 papers

TitleStatusHype
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction—0
BertAA : BERT fine-tuning for Authorship Attribution—0
BERTaú: Itaú BERT for digital customer service—0
BERT, can HE predict contrastive focus? Predicting and controlling prominence in neural TTS using a language model—0
BERT-CNN: a Hierarchical Patent Classifier Based on a Pre-Trained Language Model—0
BERT-CoQAC: BERT-based Conversational Question Answering in Context—0
BERT for Question Generation—0
BERT got a Date: Introducing Transformers to Temporal Tagging—0
BERTić -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian—0
BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian—0
BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling—0
Exploring Early Prediction of Buyer-Seller Negotiation Outcomes—0
BERT in Plutarch's Shadows—0
BERT Masked Language Modeling for Co-reference Resolution—0
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model—0
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models—0
BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets—0
BERTwich: Extending BERT's Capabilities to Model Dialectal and Noisy Text—0
BeSimulator: A Large Language Model Powered Text-based Behavior Simulator—0
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental—0
BESTOW: Efficient and Streamable Speech Language Model with the Best of Two Worlds in GPT and T5—0
Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization—0
Better Character Language Modeling Through Morphology—0
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering—0
Better Language Model with Hypernym Class Prediction—0
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning—0
Better Process Supervision with Bi-directional Rewarding Signals—0
Better Prompt Compression Without Multi-Layer Perceptrons—0
Better Think with Tables: Leveraging Tables to Enhance Large Language Model Comprehension—0
Better Transcription of UK Supreme Court Hearings—0
Better Word Representations with Recursive Neural Networks for Morphology—0
BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving—0
Beware the Rationalization Trap! When Language Model Explainability Diverges from our Mental Models of Language—0
Beyond Bare Queries: Open-Vocabulary Object Grounding with 3D Scene Graph—0
Beyond Characters: Subword-level Morpheme Segmentation—0
Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses—0
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction—0
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws—0
Beyond [CLS] through Ranking by Generation—0
Beyond Context: A New Perspective for Word Embeddings—0
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine—0
Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks—0
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering—0
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models—0
Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems—0
Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models—0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation—0
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning—0
Beyond Left-to-Right: Multiple Decomposition Structures for SMT—0
Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified