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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10251–10300 of 17610 papers

TitleStatusHype
Self-Influence Guided Data Reweighting for Language Model Pre-training—0
Predicting Question-Answering Performance of Large Language Models through Semantic Consistency—0
Recommendations by Concise User Profiles from Review Text—0
FlashDecoding++: Faster Large Language Model Inference on GPUs—0
Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations—0
Continual Learning Under Language Shift—0
Comparing Optimization Targets for Contrast-Consistent SearchCode0
Efficient Human-AI Coordination via Preparatory Language-based Convention—0
Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation—0
Improving Interpersonal Communication by Simulating Audiences with Language ModelsCode0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection—0
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements—0
An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach—0
Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem SolvingCode0
ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection—0
Mukh-Oboyob: Stable Diffusion and BanglaBERT enhanced Bangla Text-to-Face SynthesisCode0
Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities—0
Text Rendering Strategies for Pixel Language Models—0
Language Model Training Paradigms for Clinical Feature EmbeddingsCode0
Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networks—0
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision—0
Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure BuildingCode0
Filter bubbles and affective polarization in user-personalized large language model outputs—0
Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model—0
FA Team at the NTCIR-17 UFO Task—0
BERTwich: Extending BERT's Capabilities to Model Dialectal and Noisy Text—0
A Multi-Modal Foundation Model to Assist People with Blindness and Low Vision in Environmental Interaction—0
The Impact of Depth on Compositional Generalization in Transformer Language Models—0
Remember what you did so you know what to do next—0
ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common SenseCode0
Musical Form Generation—0
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks—0
Leveraging Language Models to Detect Greenwashing—0
'Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion—0
Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models—0
EHRTutor: Enhancing Patient Understanding of Discharge Instructions—0
Improving Input-label Mapping with Demonstration Replay for In-context Learning—0
Emotional Theory of Mind: Bridging Fast Visual Processing with Slow Linguistic Reasoning—0
BTRec: BERT-Based Trajectory Recommendation for Personalized ToursCode0
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design—0
Adapter Pruning using Tropical Characterization—0
Interpretable-by-Design Text Understanding with Iteratively Generated Concept BottleneckCode0
Generating Medical Prescriptions with Conditional TransformerCode0
Integrating Pre-trained Language Model into Neural Machine Translation—0
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing—0
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content—0
Counterfactually Probing Language Identity in Multilingual ModelsCode0
Unified Representation for Non-compositional and Compositional ExpressionsCode0
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise—0
PACuna: Automated Fine-Tuning of Language Models for Particle AcceleratorsCode0
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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