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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7001–7050 of 17610 papers

TitleStatusHype
Selective Preference Optimization via Token-Level Reward Function Estimation—0
Selective Token Generation for Few-shot Language Modeling—0
Select Via Proxy: Efficient Data Selection For Training Deep Networks—0
Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems—0
Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein—0
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping—0
Self-calibration for Language Model Quantization and Pruning—0
Self-Challenging Language Model Agents—0
Self-conditioned Embedding Diffusion for Text Generation—0
Lightweight reranking for language model generations—0
Self-consistent context aware conformer transducer for speech recognition—0
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives—0
Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation—0
SelfCP: Compressing Over-Limit Prompt via the Frozen Large Language Model Itself—0
Self-Destructive Language Model—0
Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages—0
Self-Distilled Pruning Of Neural Networks—0
Self-Distilled Pruning of Neural Networks—0
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning—0
Self-Evaluation Guided Beam Search for Reasoning—0
Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model—0
Self-GIVE: Associative Thinking from Limited Structured Knowledge for Enhanced Large Language Model Reasoning—0
Self-Guard: Empower the LLM to Safeguard Itself—0
Self-Imagine: Effective Unimodal Reasoning with Multimodal Models using Self-Imagination—0
Self-Improvement in Language Models: The Sharpening Mechanism—0
Self-Influence Guided Data Reweighting for Language Model Pre-training—0
Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy—0
Self-Knowledge Distillation in Natural Language Processing—0
SELF: Self-Evolution with Language Feedback—0
Self-Normalization Properties of Language Modeling—0
Self-Normalized Importance Sampling for Neural Language Modeling—0
Self-organized Hierarchical Softmax—0
SelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation—0
Self-Refined Generative Foundation Models for Wireless Traffic Prediction—0
Self-Selected Attention Span for Accelerating Large Language Model Inference—0
Self-Specialization: Uncovering Latent Expertise within Large Language Models—0
Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation—0
Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays—0
Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge—0
Self-Supervised learning with cross-modal transformers for emotion recognition—0
Self-supervised Product Title Rewrite for Product Listing Ads—0
Self-Supervised Relationship Probing—0
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts—0
Self-Supervised Singing Voice Pre-Training towards Speech-to-Singing Conversion—0
Self-Supervised Speech Recognition via Local Prior Matching—0
Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels—0
Self-Training for End-to-End Speech Recognition—0
Self-Training for Unsupervised Parsing with PRPN—0
Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability—0
SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions—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