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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 801–850 of 17610 papers

TitleStatusHype
TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation—0
Automatically Generating Rules of Malicious Software Packages via Large Language Model—0
FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Model—0
Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion—0
Monte Carlo Planning with Large Language Model for Text-Based Game Agents—0
ParamΔ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost—0
Planning with Diffusion Models for Target-Oriented Dialogue Systems—0
In-Context Learning can distort the relationship between sequence likelihoods and biological fitness—0
SplitReason: Learning To Offload Reasoning—0
Improving Significant Wave Height Prediction Using Chronos Models—0
LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field EnlargementCode1
FaceInsight: A Multimodal Large Language Model for Face Perception—0
Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations—0
Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3—0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models—0
DATETIME: A new benchmark to measure LLM translation and reasoning capabilitiesCode0
LLMs meet Federated Learning for Scalable and Secure IoT Management—0
Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software—0
What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token PatternsCode0
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes—0
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning—0
Speculative Sampling via Exponential Races—0
Virology Capabilities Test (VCT): A Multimodal Virology Q&A BenchmarkCode0
RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents—0
Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions—0
EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models—0
Kuwain 1.5B: An Arabic SLM via Language Injection—0
ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task—0
Automatic Text Summarization (ATS) for Research Documents in Sorani Kurdish—0
OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding—0
ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations—0
PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines—0
Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts—0
Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Management—0
SOTOPIA-S4: a user-friendly system for flexible, customizable, and large-scale social simulation—0
A Multimodal Recaptioning Framework to Account for Perceptual Diversity in Multilingual Vision-Language Modeling—0
Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models—0
Walk the Talk? Measuring the Faithfulness of Large Language Model ExplanationsCode1
Large Language Bayes—0
System of Agentic AI for the Discovery of Metal-Organic Frameworks—0
A Baseline for Self-state Identification and Classification in Mental Health Data: CLPsych 2025 Task—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
PV-VLM: A Multimodal Vision-Language Approach Incorporating Sky Images for Intra-Hour Photovoltaic Power Forecasting—0
Learning to Attribute with AttentionCode1
A mean teacher algorithm for unlearning of language modelsCode0
Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt GenerationCode0
Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving SafetyCode0
Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization—0
RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines—0
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization—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