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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5401–5450 of 17610 papers

TitleStatusHype
Accuracy Assessment of OpenAlex and Clarivate Scholar ID with an LLM-Assisted Benchmark—0
AdaGC: Improving Training Stability for Large Language Model Pretraining—0
G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent SystemsCode0
FELLE: Autoregressive Speech Synthesis with Token-Wise Coarse-to-Fine Flow Matching—0
Improved Unbiased Watermark for Large Language Models—0
Towards Achieving Concept Completeness for Textual Concept Bottleneck Models—0
Leveraging Conditional Mutual Information to Improve Large Language Model Fine-Tuning For Classification—0
Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring—0
SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human InterventionCode0
LogiDynamics: Unraveling the Dynamics of Logical Inference in Large Language Model Reasoning—0
Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit ViewCode0
BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop—0
Hierarchically-Structured Open-Vocabulary Indoor Scene Synthesis with Pre-trained Large Language Model—0
An Empirical Analysis of Uncertainty in Large Language Model EvaluationsCode0
HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model—0
Distraction is All You Need for Multimodal Large Language Model Jailbreaking—0
Hallucinations are inevitable but can be made statistically negligible. The "innate" inevitability of hallucinations cannot explain practical LLM issues—0
LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging—0
K-Edit: Language Model Editing with Contextual Knowledge Awareness—0
PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learning—0
Order-agnostic Identifier for Large Language Model-based Generative Recommendation—0
MTLM: Incorporating Bidirectional Text Information to Enhance Language Model Training in Speech Recognition Systems—0
MixMin: Finding Data Mixtures via Convex Minimization—0
From Markov to Laplace: How Mamba In-Context Learns Markov ChainsCode0
Has My System Prompt Been Used? Large Language Model Prompt Membership Inference—0
DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products—0
Hybrid Offline-online Scheduling Method for Large Language Model Inference Optimization—0
A Survey on LLM-powered Agents for Recommender Systems—0
Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators—0
A Survey on LLM-based News Recommender Systems—0
Improve LLM-based Automatic Essay Scoring with Linguistic Features—0
InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU—0
AIDE: Agentically Improve Visual Language Model with Domain Experts—0
Escaping Collapse: The Strength of Weak Data for Large Language Model Training—0
Unleashing the Power of Large Language Model for Denoising Recommendation—0
Vision-Language In-Context Learning Driven Few-Shot Visual Inspection ModelCode0
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia—0
Theoretical Benefit and Limitation of Diffusion Language Model—0
Reinforced Large Language Model is a formal theorem proverCode0
Logical forms complement probability in understanding language model (and human) performance—0
MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing—0
Large Language Models and Provenance Metadata for Determining the Relevance of Images and Videos in News Stories—0
Structured Convergence in Large Language Model Representations via Hierarchical Latent Space Folding—0
On Mechanistic Circuits for Extractive Question-Answering—0
QA-Expand: Multi-Question Answer Generation for Enhanced Query Expansion in Information Retrieval—0
Lexical Manifold Reconfiguration in Large Language Models: A Novel Architectural Approach for Contextual Modulation—0
LLM Pretraining with Continuous Concepts—0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection—0
Contextual Subspace Manifold Projection for Structural Refinement of Large Language Model Representations—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