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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 2651–2675 of 17610 papers

TitleStatusHype
Large Vision-Language Models for Remote Sensing Visual Question Answering—0
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus InfectionCode0
Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons—0
Take Package as Language: Anomaly Detection Using TransformerCode0
Debias your Large Multi-Modal Model at Test-Time with Non-Contrastive Visual Attribute Steering—0
Chain of Alignment: Integrating Public Will with Expert Intelligence for Language Model Alignment—0
Leveraging large language models for efficient representation learning for entity resolution—0
TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models—0
Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
Xmodel-1.5: An 1B-scale Multilingual LLMCode0
Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry—0
CART: Compositional Auto-Regressive Transformer for Image Generation—0
SlimLM: An Efficient Small Language Model for On-Device Document Assistance—0
Jal Anveshak: Prediction of fishing zones using fine-tuned LlaMa 2—0
Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving—0
SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction TuningCode2
BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency—0
Adaptive Decoding via Latent Preference Optimization—0
Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting—0
Local deployment of large-scale music AI models on commodity hardware—0
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment—0
MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity—0
LLV-FSR: Exploiting Large Language-Vision Prior for Face Super-resolution—0
On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse—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