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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6776–6800 of 17610 papers

TitleStatusHype
Purifying Large Language Models by Ensembling a Small Language Model—0
Structure Guided Large Language Model for SQL Generation—0
GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence—0
Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation—0
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data SelectionCode1
Induced Model Matching: How Restricted Models Can Help Larger OnesCode0
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction—0
Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic ForgettingCode0
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term ConversationsCode1
Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations—0
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search—0
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!Code1
Can LLMs Compute with Reasons?—0
A novel molecule generative model of VAE combined with Transformer for unseen structure generation—0
Language Model Adaptation to Specialized Domains through Selective Masking based on Genre and Topical CharacteristicsCode0
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt DistillationCode0
LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge AugmentationCode1
Uncovering Latent Human Wellbeing in Language Model Embeddings—0
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct DecodingCode1
CodeArt: Better Code Models by Attention Regularization When Symbols Are LackingCode1
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks—0
Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One—0
Transformer-based Causal Language Models Perform Clustering—0
DiLA: Enhancing LLM Tool Learning with Differential Logic Layer—0
AnyGPT: Unified Multimodal LLM with Discrete Sequence ModelingCode4
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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