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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 251–300 of 17610 papers

TitleStatusHype
Investigating the Impact of Word Informativeness on Speech Emotion Recognition—0
Why Gradients Rapidly Increase Near the End of Training—0
Self-Challenging Language Model Agents—0
MLorc: Momentum Low-rank Compression for Large Language Model Adaptation—0
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 Dataset—0
CLAP-ART: Automated Audio Captioning with Semantic-rich Audio Representation Tokenizer—0
GigaAM: Efficient Self-Supervised Learner for Speech RecognitionCode4
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction—0
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG—0
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation—0
A Large Language Model-Supported Threat Modeling Framework for Transportation Cyber-Physical Systems—0
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 Systems—0
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model TranslationsCode0
Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings—0
Speech Token Prediction via Compressed-to-fine Language Modeling for Speech Generation—0
MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform—0
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 Summarization—0
From Macro to Micro: Probing Dataset Diversity in Language Model Fine-Tuning—0
Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization—0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction—0
Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking—0
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 Labeling—0
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 Consistent—0
GradPower: Powering Gradients for Faster Language Model Pre-Training—0
HardTests: Synthesizing High-Quality Test Cases for LLM Coding—0
Grid-LOGAT: Grid Based Local and Global Area Transcription for Video Question Answering—0
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 Navigation—0
Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model—0
Large Language Model-Based Agents for Automated Research Reproducibility: An Exploratory Study in Alzheimer's Disease—0
Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine—0
Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training—0
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model CompressionCode0
Large Language Model Meets Constraint Propagation—0
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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