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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7201–7250 of 17610 papers

TitleStatusHype
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows—0
Flexible Model Interpretability through Natural Language Model Editing—0
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization—0
Flextron: Many-in-One Flexible Large Language Model—0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs—0
FLORA: Formal Language Model Enables Robust Training-free Zero-shot Object Referring Analysis—0
Flow-based generative models as iterative algorithms in probability space—0
FlowDubber: Movie Dubbing with LLM-based Semantic-aware Learning and Flow Matching based Voice Enhancing—0
FlowKV: A Disaggregated Inference Framework with Low-Latency KV Cache Transfer and Load-Aware Scheduling—0
FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models—0
FltLM: An Intergrated Long-Context Large Language Model for Effective Context Filtering and Understanding—0
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering—0
FLuRKA: Fast and accurate unified Low-Rank & Kernel Attention—0
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention—0
FocusChat: Text-guided Long Video Understanding via Spatiotemporal Information Filtering—0
Focused Contrastive Training for Test-based Constituency Analysis—0
FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models—0
Focusing Annotation for Semantic Role Labeling—0
FoldGPT: Simple and Effective Large Language Model Compression Scheme—0
FoleyGen: Visually-Guided Audio Generation—0
FoMo Rewards: Can we cast foundation models as reward functions?—0
FoodGPT: A Large Language Model in Food Testing Domain with Incremental Pre-training and Knowledge Graph Prompt—0
FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists—0
FootGPT : A Large Language Model Development Experiment on a Minimal Setting—0
Foot In The Door: Understanding Large Language Model Jailbreaking via Cognitive Psychology—0
uto\!L: Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks—0
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model—0
Forecasting Deep Learning Dynamics with Applications to Hyperparameter Tuning—0
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families—0
Forecasting Frontier Language Model Agent Capabilities—0
Forecasting Live Chat Intent from Browsing History—0
Forecasting People's Needs in Hurricane Events from Social Network—0
Forecasting Rare Language Model Behaviors—0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—0
Forest-to-String SMT for Asian Language Translation: NAIST at WAT 2014—0
ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization—0
Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage—0
Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation—0
Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models—0
Formal Aspects of Language Modeling—0
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements—0
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information—0
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction—0
Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering—0
Foundation Posteriors for Approximate Probabilistic Inference—0
FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model—0
Founder-GPT: Self-play to evaluate the Founder-Idea fit—0
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks—0
FPM: A Collection of Large-scale Foundation Pre-trained Language Models—0
FQuAD2.0: French Question Answering and knowing that you know nothing—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