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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5251–5275 of 17610 papers

TitleStatusHype
Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback—0
Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language ModelCode1
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual InstructionsCode0
Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models—0
Leveraging Open-Source Large Language Models for encoding Social Determinants of Health using an Intelligent Router—0
Efficient Indirect LLM Jailbreak via Multimodal-LLM Jailbreak—0
SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths—0
Large Language Model Watermark Stealing With Mixed Integer Programming—0
Conveyor: Efficient Tool-aware LLM Serving with Tool Partial ExecutionCode0
Adaptive In-conversation Team Building for Language Model AgentsCode7
Posterior Sampling via Autoregressive Generation—0
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive LearningCode1
A Full-duplex Speech Dialogue Scheme Based On Large Language Models—0
Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts—0
Robust Preference Optimization through Reward Model Distillation—0
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models—0
Multi-Modal Generative Embedding Model—0
Matryoshka Query Transformer for Large Vision-Language ModelsCode2
Contextual Position Encoding: Learning to Count What's Important—0
MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model—0
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study—0
Nearest Neighbor Speculative Decoding for LLM Generation and Attribution—0
Voice Jailbreak Attacks Against GPT-4oCode1
Enhancing Vision-Language Model with Unmasked Token AlignmentCode1
ChartFormer: A Large Vision Language Model for Converting Chart Images into Tactile Accessible SVGsCode0
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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