SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7551–7600 of 17610 papers

TitleStatusHype
LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits—0
Visual Text Generation in the Wild—0
Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation—0
Handling Numeric Expressions in Automatic Speech Recognition—0
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking—0
Affordance Perception by a Knowledge-Guided Vision-Language Model with Efficient Error Correction—0
BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models—0
Combining Constraint Programming Reasoning with Large Language Model Predictions—0
FuLG: 150B Romanian Corpus for Language Model Pretraining—0
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization—0
AlcLaM: Arabic Dialectal Language ModelCode0
SegPoint: Segment Any Point Cloud via Large Language Model—0
TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models—0
Transformer-based Single-Cell Language Model: A Survey—0
Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data—0
Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models—0
Towards Zero-Shot Multimodal Machine TranslationCode0
Research on Tibetan Tourism Viewpoints information generation system based on LLM—0
Learning Visual Grounding from Generative Vision and Language Model—0
Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle—0
SENTAUR: Security EnhaNced Trojan Assessment Using LLMs Against Undesirable Revisions—0
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities—0
Krutrim LLM: A Novel Tokenization Strategy for Multilingual Indic Languages with Petabyte-Scale Data Processing—0
LLM Inference Serving: Survey of Recent Advances and Opportunities—0
LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models—0
R+X: Retrieval and Execution from Everyday Human Videos—0
VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions—0
F-HOI: Toward Fine-grained Semantic-Aligned 3D Human-Object Interactions—0
Conversational Query Reformulation with the Guidance of Retrieved Documents—0
GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text—0
BadRobot: Jailbreaking Embodied LLMs in the Physical World—0
CCoE: A Compact LLM with Collaboration of Experts—0
How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language ModelsCode0
A Language Modeling Approach to Diacritic-Free Hebrew TTS—0
A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora—0
Mask-Free Neuron Concept Annotation for Interpreting Neural Networks in Medical DomainCode0
The Foundations of Tokenization: Statistical and Computational Concerns—0
XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI ApproachCode0
Building Intelligence Identification System via Large Language Model Watermarking: A Survey and Beyond—0
How and where does CLIP process negation?—0
Can Textual Semantics Mitigate Sounding Object Segmentation Preference?Code0
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together—0
Enhancing Medication Recommendation with LLM Text Representation—0
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework—0
BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy—0
Quantized Prompt for Efficient Generalization of Vision-Language ModelsCode0
Large Language Model-based FMRI Encoding of Language Functions for Subjects with Neurocognitive Disorder—0
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction—0
MMM: Multilingual Mutual Reinforcement Effect Mix Datasets & Test with Open-domain Information Extraction Large Language Models—0
Lean-STaR: Learning to Interleave Thinking and Proving—0
Show:102550
← PrevPage 152 of 353Next →
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