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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 351–400 of 17610 papers

TitleStatusHype
ChemHAS: Hierarchical Agent Stacking for Enhancing Chemistry Tools—0
LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization AlgorithmsCode2
Pretraining Language Models to Ponder in Continuous SpaceCode1
CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language ModelsCode1
PolarGrad: A Class of Matrix-Gradient Optimizers from a Unifying Preconditioning Perspective—0
HAD: Hybrid Architecture Distillation Outperforms Teacher in Genomic Sequence Modeling—0
Improved Representation Steering for Language ModelsCode2
REAL-Prover: Retrieval Augmented Lean Prover for Mathematical ReasoningCode1
Creativity in LLM-based Multi-Agent Systems: A Survey—0
StreamLink: Large-Language-Model Driven Distributed Data Engineering System—0
A Lightweight Multi-Expert Generative Language Model System for Engineering Information and Knowledge Extraction—0
Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion—0
Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework—0
Automated Privacy Information Annotation in Large Language Model InteractionsCode0
LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs—0
VSCBench: Bridging the Gap in Vision-Language Model Safety CalibrationCode0
In-context Language Learning for Endangered Languages in Speech Recognition—0
What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models—0
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM CompressionCode1
Learning to Select In-Context Demonstration Preferred by Large Language Model—0
SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety—0
Language Model-Enhanced Message Passing for Heterophilic Graph Learning—0
Style2Code: A Style-Controllable Code Generation Framework with Dual-Modal Contrastive Representation LearningCode0
WINA: Weight Informed Neuron Activation for Accelerating Large Language Model InferenceCode2
MSD-LLM: Predicting Ship Detention in Port State Control Inspections with Large Language Model—0
Unifying Multimodal Large Language Model Capabilities and Modalities via Model MergingCode1
Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations—0
Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?—0
Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language ModelsCode0
Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model—0
ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining—0
SeMe: Training-Free Language Model Merging via Semantic Alignment—0
REARANK: Reasoning Re-ranking Agent via Reinforcement LearningCode1
It's High Time: A Survey of Temporal Information Retrieval and Question Answering—0
Attention! You Vision Language Model Could Be Maliciously Manipulated—0
Balancing Computation Load and Representation Expressivity in Parallel Hybrid Neural Networks—0
Causal Distillation: Transferring Structured Explanations from Large to Compact Language Models—0
ImgEdit: A Unified Image Editing Dataset and BenchmarkCode4
ResSVD: Residual Compensated SVD for Large Language Model Compression—0
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving—0
On the Same Page: Dimensions of Perceived Shared Understanding in Human-AI Interaction—0
VoiceStar: Robust Zero-Shot Autoregressive TTS with Duration Control and ExtrapolationCode3
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees—0
LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer—0
TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking AgentCode0
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence MaskingCode0
Paying Alignment Tax with Contrastive Learning—0
ScreenExplorer: Training a Vision-Language Model for Diverse Exploration in Open GUI WorldCode1
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models—0
Evaluating Steering Techniques using Human Similarity Judgments—0
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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