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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 80018050 of 17610 papers

TitleStatusHype
Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library0
Improving Autoregressive Image Generation through Coarse-to-Fine Token Prediction0
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation0
Improving BERT with Hybrid Pooling Network and Drop Mask0
Improving Black-box Speech Recognition using Semantic Parsing0
Improving Block-Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations0
Improving Brain-to-Image Reconstruction via Fine-Grained Text Bridging0
Improving callsign recognition with air-surveillance data in air-traffic communication0
Improving Character-Aware Neural Language Model by Warming up Character Encoder under Skip-gram Architecture0
Improving Chess Commentaries by Combining Language Models with Symbolic Reasoning Engines0
Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation0
Improving Code-switched ASR with Linguistic Information0
Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks0
Improving Coherence of Language Model Generation with Latent Semantic State0
Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation0
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources0
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition0
Improving Controllable Text Generation with Position-Aware Weighted Decoding0
Improving Controllable Text Generation with Position-Aware Weighted Decoding0
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information0
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information0
Improving corpus annotation productivity: a method and experiment with interactive tagging0
Improving cross-domain n-gram language modelling with skipgrams0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation0
Improving CTC-based ASR Models with Gated Interlayer Collaboration0
Improving Deliberation by Text-Only and Semi-Supervised Training0
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning0
Improving Diversity of Neural Text Generation via Inverse Probability Weighting0
Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions0
Improving domain-specific SMT for low-resourced languages using data from different domains0
Improving EEG based Continuous Speech Recognition0
Improving Emotional Expression and Cohesion in Image-Based Playlist Description and Music Topics: A Continuous Parameterization Approach0
Improving Emotional Support Delivery in Text-Based Community Safety Reporting Using Large Language Models0
Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets0
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning0
Improving Explainable Recommendations with Synthetic Reviews0
Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model0
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model0
Improving Image Captioning by Concept-based Sentence Reranking0
Improving Image Captioning Descriptiveness by Ranking and LLM-based Fusion0
Improving Input-label Mapping with Demonstration Replay for In-context Learning0
Improving Interactive Diagnostic Ability of a Large Language Model Agent Through Clinical Experience Learning0
Improving Language Model Adaptation using Automatic Data Selection and Neural Network0
Improving Language Modeling using Densely Connected Recurrent Neural Networks0
Improving Language Model Integration for Neural Machine Translation0
Improving Language Modelling with Noise-contrastive estimation0
Improving Language Model Personas via Rationalization with Psychological Scaffolds0
Improving Language Model Prompting in Support of Semi-autonomous Task Learning0
Improving Language Model Reasoning with Self-motivated Learning0
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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