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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8251–8300 of 17610 papers

TitleStatusHype
Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective—0
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
Towards Understanding Task-agnostic Debiasing Through the Lenses of Intrinsic Bias and Forgetfulness—0
Towards Understanding the Influence of Reward Margin on Preference Model Performance—0
Towards Unified Facial Action Unit Recognition Framework by Large Language Models—0
Towards Unified Prompt Tuning for Few-shot Learning—0
Towards Unified Prompt Tuning for Few-shot Text Classification—0
Towards Unifying Multi-Lingual and Cross-Lingual Summarization—0
Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures—0
Towards Unsupervised Image Captioning with Shared Multimodal Embeddings—0
Towards Unsupervised Speech-to-Text Translation—0
Towards Using Context-Dependent Symbols in CTC Without State-Tying Decision Trees—0
Towards Using EEG to Improve ASR Accuracy—0
Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference—0
Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video—0
Towards Visual Syntactical Understanding—0
Towards Visual Text Grounding of Multimodal Large Language Model—0
Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model—0
Towards Zero-Shot Code-Switched Speech Recognition—0
Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration—0
Towards Zero-shot Language Modeling—0
Toward Tree Substitution Grammars with Latent Annotations—0
Toward Tweets Normalization Using Maximum Entropy—0
Toward Understanding In-context vs. In-weight Learning—0
Toxicity Detection with Generative Prompt-based Inference—0
Toxic Subword Pruning for Dialogue Response Generation on Large Language Models—0
TPC: Cross-Temporal Prediction Connection for Vision-Language Model Hallucination Reduction—0
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance—0
TPPoet: Transformer-Based Persian Poem Generation using Minimal Data and Advanced Decoding Techniques—0
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning—0
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage—0
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems—0
Trace and Edit Relation Associations in GPT—0
Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses—0
Tracing the Genealogies of Ideas with Large Language Model Embeddings—0
Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports—0
Tracking Universal Features Through Fine-Tuning and Model Merging—0
TrackVLA: Embodied Visual Tracking in the Wild—0
Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases—0
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System—0
Training a Bilingual Language Model by Mapping Tokens onto a Shared Character Space—0
Training a code-switching language model with monolingual data—0
Training and Analysing Deep Recurrent Neural Networks—0
Training a T5 Using Lab-sized Resources—0
Training a Tokenizer for Free with Private Federated Learning—0
Training a Vision Language Model as Smartphone Assistant—0
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models—0
Training Data for Large Language Model—0
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data—0
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments—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