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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15551–15600 of 17610 papers

TitleStatusHype
A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries—0
A Transformer Based Pitch Sequence Autoencoder with MIDI Augmentation—0
A Tree Transducer Model for Grammatical Error Correction—0
Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers—0
Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation—0
Attention Augmented Convolutional Transformer for Tabular Time-series—0
Attention-Based End-to-End Speech Recognition on Voice Search—0
Attention-based Memory Selection Recurrent Network for Language Modeling—0
Attention-based Speech Enhancement Using Human Quality Perception Modelling—0
Tomography of Quantum States from Structured Measurements via quantum-aware transformer—0
Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models—0
Attention Fusion: a light yet efficient late fusion mechanism for task adaptation in NLU—0
Attention Is Not All You Need: The Importance of Feedforward Networks in Transformer Models—0
AttentionLego: An Open-Source Building Block For Spatially-Scalable Large Language Model Accelerator With Processing-In-Memory Technology—0
Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation—0
Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention—0
Attention with Intention for a Neural Network Conversation Model—0
Attention with Trained Embeddings Provably Selects Important Tokens—0
Attention! You Vision Language Model Could Be Maliciously Manipulated—0
Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue—0
Attributions toward Artificial Agents in a modified Moral Turing Test—0
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning—0
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification—0
A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model—0
Auctions with LLM Summaries—0
Audience size and contextual effects on information density in Twitter conversations—0
Audio-Agent: Leveraging LLMs For Audio Generation, Editing and Composition—0
Audio-attention discriminative language model for ASR rescoring—0
Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval—0
Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model—0
Audio Dialogues: Dialogues dataset for audio and music understanding—0
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion—0
Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities—0
Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models—0
Audio Generation with Multiple Conditional Diffusion Model—0
AudioPaLM: A Large Language Model That Can Speak and Listen—0
AudioSetMix: Enhancing Audio-Language Datasets with LLM-Assisted Augmentations—0
Audio-Visual LLM for Video Understanding—0
Aud-Sur: An Audio Analyzer Assistant for Audio Surveillance Applications—0
Augmented Language Models: a Survey—0
Augmented Neural Story Generation with Commonsense Inference—0
Augmenting a Large Language Model with a Combination of Text and Visual Data for Conversational Visualization of Global Geospatial Data—0
Augmenting Autotelic Agents with Large Language Models—0
Augmenting emotion features in irony detection with Large language modeling—0
Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment—0
Augmenting Language Models with Long-Term Memory—0
Augmenting Large Language Model Translators via Translation Memories—0
Augmenting LLMs with Knowledge: A survey on hallucination prevention—0
Augmenting Translation Models with Simulated Acoustic Confusions for Improved Spoken Language Translation—0
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics—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