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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15701–15750 of 17610 papers

TitleStatusHype
Automatic Speech Recognition of African American English: Lexical and Contextual Effects—0
Automatic Speech Recognition on a Firefighter TETRA Broadcast Channel—0
Automatic Speech Summarisation: A Scoping Review—0
Automatic Spoken Language Identification using a Time-Delay Neural Network—0
Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning—0
Automatic Text Summarization (ATS) for Research Documents in Sorani Kurdish—0
Automatic Transliteration of Romanized Dialectal Arabic—0
Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings—0
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model—0
Automating Date Format Detection for Data Visualization—0
Automating Knowledge Acquisition for Content-Centric Cognitive Agents Using LLMs—0
Automating Mathematical Proof Generation Using Large Language Model Agents and Knowledge Graphs—0
Automating psychological hypothesis generation with AI: when large language models meet causal graph—0
Automating PTSD Diagnostics in Clinical Interviews: Leveraging Large Language Models for Trauma Assessments—0
Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning—0
Automating Security Audit Using Large Language Model based Agent: An Exploration Experiment—0
Automating speech reception threshold measurements using automatic speech recognition—0
Automating the Analysis of Public Saliency and Attitudes towards Biodiversity from Digital Media—0
Automating Thought of Search: A Journey Towards Soundness and Completeness—0
AutoML-GPT: Large Language Model for AutoML—0
Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval—0
Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology—0
Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model—0
Autonomous Building Cyber-Physical Systems Using Decentralized Autonomous Organizations, Digital Twins, and Large Language Model—0
Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing—0
AutoRad-Lung: A Radiomic-Guided Prompting Autoregressive Vision-Language Model for Lung Nodule Malignancy Prediction—0
Autoregressive + Chain of Thought = Recurrent: Recurrence's Role in Language Models' Computability and a Revisit of Recurrent Transformer—0
Autoregressive Language Model for Zero-shot Constrained Keyphrase Generation—0
Autoregressive Language Models for Knowledge Base Population: A case study in the space mission domain—0
Autoregressive Large Language Models are Computationally Universal—0
Autoregressive Linguistic Steganography Based on BERT and Consistency Coding—0
Limitations of Autoregressive Models and Their Alternatives—0
Autoregressive Speech Synthesis with Next-Distribution Prediction—0
Autoregressive Speech Synthesis without Vector Quantization—0
Auto-Sizing Neural Networks: With Applications to n-gram Language Models—0
AutoTask: Task Aware Multi-Faceted Single Model for Multi-Task Ads Relevance—0
Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning—0
Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems—0
A Vector-Based Approach to Few-Shot Veracity Classification for Automated Fact-Checking—0
AviationGPT: A Large Language Model for the Aviation Domain—0
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent—0
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features—0
A Vision-Language Model for Focal Liver Lesion Classification—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
A Voter-Based Stochastic Rejection-Method Framework for Asymptotically Safe Language Model Outputs—0
AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora—0
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis—0
Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination—0
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings—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