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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 151–200 of 17610 papers

TitleStatusHype
TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding—0
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QACode0
Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information—0
LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation—0
Disclosure Audits for LLM Agents—0
XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented GenerationCode0
SUTA-LM: Bridging Test-Time Adaptation and Language Model Rescoring for Robust ASR—0
The Predictive Brain: Neural Correlates of Word Expectancy Align with Large Language Model Prediction Probabilities—0
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning—0
PHRASED: Phrase Dictionary Biasing for Speech Translation—0
Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data—0
PlantBert: An Open Source Language Model for Plant Science—0
DeepForm: Reasoning Large Language Model for Communication System Formulation—0
Towards Secure and Private Language Models for Nuclear Power Plants—0
Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness—0
PropMEND: Hypernetworks for Knowledge Propagation in LLMsCode0
MLVTG: Mamba-Based Feature Alignment and LLM-Driven Purification for Multi-Modal Video Temporal Grounding—0
JoFormer (Journey-based Transformer): Theory and Empirical Analysis on the Tiny Shakespeare DatasetCode0
From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge—0
Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language ModelCode7
WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis—0
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models—0
SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models—0
SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills—0
A Hybrid GA LLM Framework for Structured Task OptimizationCode0
Diffusion Sequence Models for Enhanced Protein Representation and GenerationCode1
Towards a Small Language Model Lifecycle Framework—0
Quantum Graph Transformer for NLP Sentiment Classification—0
Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs—0
LiteVLM: A Low-Latency Vision-Language Model Inference Pipeline for Resource-Constrained Environments—0
OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting—0
Reinforcement Pre-Training—0
Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language ModelsCode0
Event-Priori-Based Vision-Language Model for Efficient Visual Understanding—0
SpatialLM: Training Large Language Models for Structured Indoor Modeling—0
A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks—0
Scaling Laws of Motion Forecasting and Planning -- A Technical Report—0
Towards Universal Offline Black-Box Optimization via Learning Language Model EmbeddingsCode1
AnnoDPO: Protein Functional Annotation Learning with Direct Preference OptimizationCode0
SAFE: Finding Sparse and Flat Minima to Improve PruningCode1
An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design—0
Automatic Speech Recognition of African American English: Lexical and Contextual Effects—0
PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time—0
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
Masked Language Models are Good Heterogeneous Graph GeneralizersCode0
WhisQ: Cross-Modal Representation Learning for Text-to-Music MOS Prediction—0
Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management—0
Label-Context-Dependent Internal Language Model Estimation for CTC—0
Benchmarking Misuse Mitigation Against Covert AdversariesCode0
Training-Free Query Optimization via LLM-Based Plan Similarity—0
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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