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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6651–6700 of 17610 papers

TitleStatusHype
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation—0
Adaptive Reasoning and Acting in Medical Language Agents—0
COrAL: Order-Agnostic Language Modeling for Efficient Iterative RefinementCode0
Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation—0
Extended Japanese Commonsense Morality Dataset with Masked Token and Label Enhancement—0
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense ReasoningCode0
Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos—0
LLMD: A Large Language Model for Interpreting Longitudinal Medical Records—0
Emergent social conventions and collective bias in LLM populations—0
Preferential Normalizing Flows—0
nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder—0
SocialGaze: Improving the Integration of Human Social Norms in Large Language ModelsCode0
SimpleStrat: Diversifying Language Model Generation with Stratification—0
MedMobile: A mobile-sized language model with expert-level clinical capabilitiesCode0
The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling—0
Simultaneous Reward Distillation and Preference Learning: Get You a Language Model Who Can Do Both—0
Lifelong Event Detection via Optimal Transport—0
Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective—0
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation—0
VLM See, Robot Do: Human Demo Video to Robot Action Plan via Vision Language Model—0
Distributionally robust self-supervised learning for tabular dataCode0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference—0
Calibrated Cache Model for Few-Shot Vision-Language Model Adaptation—0
ACER: Automatic Language Model Context Extension via Retrieval—0
Generation with Dynamic VocabularyCode0
Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning—0
uto\!L: Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks—0
Enterprise Benchmarks for Large Language Model EvaluationCode0
Can a large language model be a gaslighter?Code0
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression—0
A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts—0
HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation PredictionCode0
Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentions—0
Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student RevisionsCode0
Semantic Self-Consistency: Enhancing Language Model Reasoning via Semantic Weighting—0
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Evolutionary Contrastive Distillation for Language Model Alignment—0
Animating the Past: Reconstruct Trilobite via Video Generation—0
Mechanistic Permutability: Match Features Across Layers—0
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models—0
Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art—0
More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed RoutingCode0
PLaMo-100B: A Ground-Up Language Model Designed for Japanese Proficiency—0
The Large Language Model GreekLegalRoBERTa—0
LecPrompt: A Prompt-based Approach for Logical Error Correction with CodeBERT—0
Language model developers should report train-test overlap—0
Uncovering Overfitting in Large Language Model Editing—0
Plug-and-Play Performance Estimation for LLM Services without Relying on Labeled DataCode0
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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