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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7701–7750 of 17610 papers

TitleStatusHype
Integrating Pre-Trained Speech and Language Models for End-to-End Speech Recognition—0
Run LoRA Run: Faster and Lighter LoRA Implementations—0
GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science—0
Customization Assistant for Text-to-image GenerationCode2
ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise PreferenceCode1
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense RetrievalCode1
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models—0
Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence—0
A Hardware Evaluation Framework for Large Language Model Inference—0
Efficient Online Data Mixing For Language Model Pre-TrainingCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
LLaRA: Large Language-Recommendation AssistantCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Scaling Laws for Adversarial Attacks on Language Model Activations—0
Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic—0
Large Language Models on Graphs: A Comprehensive SurveyCode2
FG-MDM: Towards Zero-Shot Human Motion Generation via ChatGPT-Refined Descriptions—0
Large Knowledge Model: Perspectives and Challenges—0
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model—0
Weakly Supervised Detection of Hallucinations in LLM ActivationsCode5
WhisBERT: Multimodal Text-Audio Language Modeling on 100M WordsCode1
Describing Differences in Image Sets with Natural LanguageCode1
Visually Grounded Language Learning: a review of language games, datasets, tasks, and models—0
Intelligent Virtual Assistants with LLM-based Process Automation—0
Revisiting Topic-Guided Language ModelsCode0
RINAS: Training with Dataset Shuffling Can Be General and Fast—0
Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings—0
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding—0
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment—0
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model CommunicationCode1
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication AwarenessCode0
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language ModelCode1
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with FakepediaCode0
StoryGPT-V: Large Language Models as Consistent Story VisualizersCode1
Jellyfish: A Large Language Model for Data Preprocessing—0
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language ModelsCode0
The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model—0
Object Recognition as Next Token PredictionCode1
A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly—0
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario—0
Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and GenerationCode1
TPPoet: Transformer-Based Persian Poem Generation using Minimal Data and Advanced Decoding Techniques—0
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video UnderstandingCode2
Semantics-aware Motion Retargeting with Vision-Language Models—0
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERTCode0
SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects—0
SymNoise: Advancing Language Model Fine-tuning with Symmetric Noise—0
Can We Learn Communication-Efficient Optimizers?Code0
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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