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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 501550 of 17610 papers

TitleStatusHype
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning0
Mechanistic evaluation of Transformers and state space models0
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech TranslationCode0
Listen to the Context: Towards Faithful Large Language Models for Retrieval Augmented Generation on Climate Questions0
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning0
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering0
Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications0
Automated Journalistic Questions: A New Method for Extracting 5W1H in French0
Too Long, Didn't Model: Decomposing LLM Long-Context Understanding With NovelsCode0
MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM HallucinationsCode0
Studying the Role of Input-Neighbor Overlap in Retrieval-Augmented Language Models Training Efficiency0
HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing0
Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives0
CtrlDiff: Boosting Large Diffusion Language Models with Dynamic Block Prediction and Controllable Generation0
Exploring Graph Representations of Logical Forms for Language ModelingCode0
Speculative Decoding Reimagined for Multimodal Large Language ModelsCode1
CAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring0
Structured Agent Distillation for Large Language Model0
Improve Language Model and Brain Alignment via Associative MemoryCode0
FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation0
CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code GenerationCode2
UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation0
TRATES: Trait-Specific Rubric-Assisted Cross-Prompt Essay Scoring0
Rank-K: Test-Time Reasoning for Listwise RerankingCode0
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising0
U-SAM: An audio language Model for Unified Speech, Audio, and Music UnderstandingCode1
sudoLLM : On Multi-role Alignment of Language Models0
MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow0
A*-Decoding: Token-Efficient Inference Scaling0
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation0
Krikri: Advancing Open Large Language Models for Greek0
ReSW-VL: Representation Learning for Surgical Workflow Analysis Using Vision-Language Model0
VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping0
Efficient Speech Language Modeling via Energy Distance in Continuous Latent SpaceCode2
The Traitors: Deception and Trust in Multi-Agent Language Model SimulationsCode0
ORQA: A Benchmark and Foundation Model for Holistic Operating Room Modeling0
R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model0
Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation0
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks0
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection0
SpatialLLM: From Multi-modality Data to Urban Spatial IntelligenceCode0
3D Visual Illusion Depth EstimationCode1
SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models0
On the Thinking-Language Modeling Gap in Large Language Models0
Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video UnderstandingCode0
IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model Alignment0
A Physics-Inspired Optimizer: Velocity Regularized Adam0
Structure-Aware Corpus Construction and User-Perception-Aligned Metrics for Large-Language-Model Code Completion0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified