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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5251–5300 of 17610 papers

TitleStatusHype
"Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation—0
Don't Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information—0
Don’t Forget About Pronouns: Removing Gender Bias in Language Models without Losing Factual Gender Information—0
Don’t Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information—0
Don't Forget It! Conditional Sparse Autoencoder Clamping Works for Unlearning—0
Don't Forget to Connect! Improving RAG with Graph-based Reranking—0
Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration—0
Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Do People Prefer "Natural" code?—0
Doppelgänger's Watch: A Split Objective Approach to Large Language Models—0
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment—0
Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling—0
0.8% Nyquist computational ghost imaging via non-experimental deep learning—0
1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training—0
1.5 million materials narratives generated by chatbots—0
Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition—0
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale—0
Neural Machine Translation with Recurrent Highway Networks—0
Gmail Smart Compose: Real-Time Assisted Writing—0
Adversarial Generation and Encoding of Nested Texts—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
Latent Relation Language Models—0
Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings—0
Adapting Language Models for Non-Parallel Author-Stylized Rewriting—0
Inducing Constituency Trees through Neural Machine Translation—0
Focus Your Attention (with Adaptive IIR Filters)—0
Estimating class separability of text embeddings with persistent homology—0
RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair—0
Characterizing Latent Perspectives of Media Houses Towards Public Figures—0
AstroLLaMA: Towards Specialized Foundation Models in Astronomy—0
LawLLM: Law Large Language Model for the US Legal System—0
Apple Intelligence Foundation Language Models—0
Accelerating Large Language Model Inference with Self-Supervised Early Exits—0
Entropy, Thermodynamics and the Geometrization of the Language Model—0
Interpreting and learning voice commands with a Large Language Model for a robot system—0
Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins—0
Handling Numeric Expressions in Automatic Speech Recognition—0
ABC Align: Large Language Model Alignment for Safety & Accuracy—0
Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation—0
Are Bigger Encoders Always Better in Vision Large Models?—0
SynesLM: A Unified Approach for Audio-visual Speech Recognition and Translation via Language Model and Synthetic Data—0
Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model—0
ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model—0
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation—0
Multi-Aspect Reviewed-Item Retrieval via LLM Query Decomposition and Aspect Fusion—0
Peptide Sequencing Via Protein Language Models—0
Towards Zero-Shot Annotation of the Built Environment with Vision-Language Models (Vision Paper)—0
Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios—0
The Impact of Hyperparameters on Large Language Model Inference Performance: An Evaluation of vLLM and HuggingFace Pipelines—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