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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3951–3975 of 17610 papers

TitleStatusHype
Great Memory, Shallow Reasoning: Limits of kNN-LMsCode1
UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and GenerationCode1
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers—0
biorecap: an R package for summarizing bioRxiv preprints with a local LLMCode2
MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt TuningCode1
Language Modeling on Tabular Data: A Survey of Foundations, Techniques and EvolutionCode1
CTP-LLM: Clinical Trial Phase Transition Prediction Using Large Language Models—0
Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models—0
Analysis of Plan-based Retrieval for Grounded Text Generation—0
Mistral-SPLADE: LLMs for better Learned Sparse RetrievalCode0
Hide Your Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Carrier Articles—0
Scaling Law with Learning Rate Annealing—0
ColBERT Retrieval and Ensemble Response Scoring for Language Model Question AnsweringCode0
Task-level Distributionally Robust Optimization for Large Language Model-based Dense RetrievalCode1
HMoE: Heterogeneous Mixture of Experts for Language Modeling—0
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language ModelCode0
Prompt-Guided Image-Adaptive Neural Implicit Lookup Tables for Interpretable Image EnhancementCode1
BEYOND DIALOGUE: A Profile-Dialogue Alignment Framework Towards General Role-Playing Language ModelCode2
Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups—0
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal ModelCode3
MEGen: Generative Backdoor in Large Language Models via Model Editing—0
Large Language Model Driven Recommendation—0
Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology—0
HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language ModelsCode1
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning—0
Show:102550
← PrevPage 159 of 705Next →
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