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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR CorrectionCode0
Improving Self Consistency in LLMs through Probabilistic Tokenization—0
Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration—0
Chain-of-Thought Augmentation with Logit Contrast for Enhanced Reasoning in Language Models—0
Generative Technology for Human Emotion Recognition: A Scope Review—0
Narrow Transformer: StarCoder-Based Java-LM For Desktop—0
MRIR: Integrating Multimodal Insights for Diffusion-based Realistic Image Restoration—0
Uncertainty-Guided Optimization on Large Language Model Search TreesCode0
On the Effectiveness of Acoustic BPE in Decoder-Only TTS—0
The Mysterious Case of Neuron 1512: Injectable Realignment Architectures Reveal Internal Characteristics of Meta's Llama 2 ModelCode0
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization—0
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation LearningCode0
Unlocking the Potential of Model Merging for Low-Resource Languages—0
CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics—0
Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering—0
Images Speak Louder than Words: Understanding and Mitigating Bias in Vision-Language Model from a Causal Mediation Perspective—0
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output—0
Croppable Knowledge Graph Embedding—0
Efficient Training of Language Models with Compact and Consistent Next Token Distributions—0
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model—0
Model-Enhanced LLM-Driven VUI Testing of VPA Apps—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
Let the Code LLM Edit Itself When You Edit the Code—0
Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data—0
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models—0
Towards Federated RLHF with Aggregated Client Preference for LLMs—0
Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulness—0
RDBE: Reasoning Distillation-Based Evaluation Enhances Automatic Essay Scoring—0
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning—0
LLMcap: Large Language Model for Unsupervised PCAP Failure Detection—0
Multi-Modal Video Dialog State Tracking in the Wild—0
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning—0
SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing—0
Synthetic Multimodal Question Generation—0
Lightweight Large Language Model for Medication Enquiry: Med-Pal—0
Neurocache: Efficient Vector Retrieval for Long-range Language ModelingCode0
Why do LLaVA Vision-Language Models Reply to Images in English?—0
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning—0
Investigating the Effects of Large-Scale Pseudo-Stereo Data and Different Speech Foundation Model on Dialogue Generative Spoken Language Model—0
Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?Code0
Accompanied Singing Voice Synthesis with Fully Text-controlled Melody—0
FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs—0
LLMs Plagiarize: Ensuring Responsible Sourcing of Large Language Model Training Data Through Knowledge Graph Comparison—0
Helpful assistant or fruitful facilitator? Investigating how personas affect language model behaviorCode0
An End-to-End Speech Summarization Using Large Language Model—0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions—0
Fake News Detection and Manipulation Reasoning via Large Vision-Language Models—0
Assessing the Effectiveness of GPT-4o in Climate Change Evidence Synthesis and Systematic Assessments: Preliminary Insights—0
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities—0
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via AdaptersCode0
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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