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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9476–9500 of 17610 papers

TitleStatusHype
LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer—0
LLM Agent Honeypot: Monitoring AI Hacking Agents in the Wild—0
LLM Agents for Education: Advances and Applications—0
LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning—0
LLM App Squatting and Cloning—0
LLM-as-a-Judge & Reward Model: What They Can and Cannot Do—0
LLM as A Robotic Brain: Unifying Egocentric Memory and Control—0
LLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embedding—0
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
LLM-based Extraction of Contradictions from Patents—0
LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks—0
LLM-based Iterative Approach to Metamodeling in Automotive—0
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction—0
LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems—0
LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems—0
LLMCad: Fast and Scalable On-device Large Language Model Inference—0
LLMcap: Large Language Model for Unsupervised PCAP Failure Detection—0
LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences—0
Large Language Model Compression with Neural Architecture Search—0
LLMD: A Large Language Model for Interpreting Longitudinal Medical Records—0
LLM-Enabled EV Charging Stations Recommendation—0
RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model—0
LLM Enhancer: Merged Approach using Vector Embedding for Reducing Large Language Model Hallucinations with External Knowledge—0
Large Language Model Enhancers for Graph Neural Networks: An Analysis from the Perspective of Causal Mechanism Identification—0
LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction—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