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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 251300 of 17610 papers

TitleStatusHype
Investigating the Impact of Word Informativeness on Speech Emotion Recognition0
Why Gradients Rapidly Increase Near the End of Training0
Self-Challenging Language Model Agents0
MLorc: Momentum Low-rank Compression for Large Language Model Adaptation0
Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian LawsCode0
Reasoning-Table: Exploring Reinforcement Learning for Table ReasoningCode2
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document ParsingCode0
HouseTS: A Large-Scale, Multimodal Spatiotemporal U.S. Housing Dataset0
CLAP-ART: Automated Audio Captioning with Semantic-rich Audio Representation Tokenizer0
GigaAM: Efficient Self-Supervised Learner for Speech RecognitionCode4
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction0
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG0
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation0
A Large Language Model-Supported Threat Modeling Framework for Transportation Cyber-Physical Systems0
Goal-Aware Identification and Rectification of Misinformation in Multi-Agent SystemsCode0
MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and GenerationCode2
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems0
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model TranslationsCode0
Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings0
Speech Token Prediction via Compressed-to-fine Language Modeling for Speech Generation0
MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform0
Period-LLM: Extending the Periodic Capability of Multimodal Large Language ModelCode1
GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language ModelsCode2
Beyond Multiple Choice: Evaluating Steering Vectors for Adaptive Free-Form Summarization0
From Macro to Micro: Probing Dataset Diversity in Language Model Fine-Tuning0
Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction0
Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking0
Probing the Robustness Properties of Neural Speech CodecsCode0
Circuit Stability Characterizes Language Model GeneralizationCode0
Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series ForecastingCode1
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data SynthesisCode0
Drop Dropout on Single-Epoch Language Model PretrainingCode0
How much do language models memorize?0
Intuitionistic Fuzzy Sets for Large Language Model Data Annotation: A Novel Approach to Side-by-Side Preference Labeling0
ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RLCode2
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and FinetuningCode0
Transformers Are Universally Consistent0
GradPower: Powering Gradients for Faster Language Model Pre-Training0
HardTests: Synthesizing High-Quality Test Cases for LLM Coding0
Grid-LOGAT: Grid Based Local and Global Area Transcription for Video Question Answering0
Interpreting Large Text-to-Image Diffusion Models with Dictionary LearningCode0
FABLE: A Novel Data-Flow Analysis Benchmark on Procedural Text for Large Language Model EvaluationCode0
Reducing Latency in LLM-Based Natural Language Commands Processing for Robot Navigation0
Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model0
Large Language Model-Based Agents for Automated Research Reproducibility: An Exploratory Study in Alzheimer's Disease0
Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine0
Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training0
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model CompressionCode0
Large Language Model Meets Constraint Propagation0
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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