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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 851–900 of 17610 papers

TitleStatusHype
Are Retrials All You Need? Enhancing Large Language Model Reasoning Without Verbalized Feedback—0
Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge—0
CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training—0
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training—0
Sparks of Science: Hypothesis Generation Using Structured Paper Data—0
Low-hallucination Synthetic Captions for Large-Scale Vision-Language Model Pre-training—0
Perception Encoder: The best visual embeddings are not at the output of the networkCode8
ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images—0
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization—0
Chinese-Vicuna: A Chinese Instruction-following Llama-based ModelCode7
Energy-Based Reward Models for Robust Language Model AlignmentCode0
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep ClassificationCode0
Mixer Metaphors: audio interfaces for non-musical applications—0
BitNet b1.58 2B4T Technical Report—0
Higher-Order Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions—0
Generative Recommendation with Continuous-Token Diffusion—0
Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach—0
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment—0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning—0
Towards Conversational AI for Human-Machine Collaborative MLOps—0
Interpreting the linear structure of vision-language model embedding spaces—0
Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions—0
Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence—0
GraphicBench: A Planning Benchmark for Graphic Design with Language Agents—0
Co-STAR: Collaborative Curriculum Self-Training with Adaptive Regularization for Source-Free Video Domain AdaptationCode0
A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports—0
From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image SegmentationCode0
Large Language Model-Informed Feature Discovery Improves Prediction and Interpretation of Credibility Perceptions of Visual Content—0
DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings—0
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
Looking beyond the next token—0
Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance—0
A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science—0
Mavors: Multi-granularity Video Representation for Multimodal Large Language Model—0
Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data—0
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models—0
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model—0
SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical ImagingCode1
The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single TransformerCode2
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families—0
MorphTok: Morphologically Grounded Tokenization for Indian Languages—0
α-Flow: A Unified Framework for Continuous-State Discrete Flow Matching Models—0
Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis—0
LangPert: Detecting and Handling Task-level Perturbations for Robust Object Rearrangement—0
Joint Action Language Modelling for Transparent Policy Execution—0
Automated Testing of COBOL to Java Transformation—0
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design—0
Show:102550
← PrevPage 18 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