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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 3976–4000 of 17610 papers

TitleStatusHype
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning—0
Self-Refined Generative Foundation Models for Wireless Traffic Prediction—0
BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction—0
Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models—0
Development of an AI Anti-Bullying System Using Large Language Model Key Topic Detection—0
Cross-composition Feature Disentanglement for Compositional Zero-shot Learning—0
SSDTrain: An Activation Offloading Framework to SSDs for Faster Large Language Model Training—0
Attribution Analysis Meets Model Editing: Advancing Knowledge Correction in Vision Language Models with VisEditCode1
IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and AbductionCode0
MePT: Multi-Representation Guided Prompt Tuning for Vision-Language Model—0
GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts—0
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation—0
MoDeGPT: Modular Decomposition for Large Language Model Compression—0
BLADE: Benchmarking Language Model Agents for Data-Driven ScienceCode1
R2GenCSR: Retrieving Context Samples for Large Language Model based X-ray Medical Report Generation—0
MSDiagnosis: A Benchmark for Evaluating Large Language Models in Multi-Step Clinical Diagnosis—0
AutoML-guided Fusion of Entity and LLM-based Representations for Document ClassificationCode0
Geometry Informed Tokenization of Molecules for Language Model Generation—0
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis AssistantCode1
Minor DPO reject penalty to increase training robustness—0
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic ModelsCode1
Importance Weighting Can Help Large Language Models Self-ImproveCode0
A Comparison of Large Language Model and Human Performance on Random Number Generation TasksCode0
Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework—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