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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1851–1900 of 17610 papers

TitleStatusHype
DReSS: Data-driven Regularized Structured Streamlining for Large Language Models—0
From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors—0
Leveraging Multimodal LLM for Inspirational User Interface SearchCode0
BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone Disambiguation -- Challenges and Insights—0
DINT Transformer—0
2SSP: A Two-Stage Framework for Structured Pruning of LLMsCode1
Learning Free Token Reduction for Multi-Modal Large Language Models—0
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI AssistantCode0
Large Language Models for Single-Step and Multi-Step Flight Trajectory Prediction—0
Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models—0
Implementation of a Generative AI Assistant in K-12 Education: The CyberScholar Initiative—0
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language ModelCode2
Language Modelling for Speaker Diarization in Telephonic Interviews—0
RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token ReprogrammingsCode1
"Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism—0
Optimizing Large Language Model Training Using FP4 Quantization—0
Document Screenshot Retrievers are Vulnerable to Pixel Poisoning AttacksCode0
Multiple Abstraction Level Retrieve Augment Generation—0
AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse AutoencodersCode2
Large Language Model Critics for Execution-Free Evaluation of Code ChangesCode0
Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling—0
An LLM Benchmark for Addressee Recognition in Multi-modal Multi-party Dialogue—0
VLMaterial: Procedural Material Generation with Large Vision-Language Models—0
Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare—0
Atla Selene Mini: A General Purpose Evaluation ModelCode1
BiFold: Bimanual Cloth Folding with Language Guidance—0
Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction—0
Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation—0
PRISMe: A Novel LLM-Powered Tool for Interactive Privacy Policy Assessment—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Is It Navajo? Accurate Language Detection in Endangered Athabaskan LanguagesCode0
Classification Error Bound for Low Bayes Error Conditions in Machine Learning—0
CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-IdentificationCode0
MEL: Legal Spanish Language Model—0
Integration of LLM Quality Assurance into an NLG System—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
Complete Chess Games Enable LLM Become A Chess Master—0
Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning—0
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning—0
ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from TransformerCode1
Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets—0
Ocean-OCR: Towards General OCR Application via a Vision-Language ModelCode1
Semantic Layered Embedding Diffusion in Large Language Models for Multi-Contextual Consistency—0
Multi-Grained Patch Training for Efficient LLM-based RecommendationCode0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy—0
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding—0
Diffusion based Text-to-Music Generation with Global and Local Text based Conditioning—0
LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration—0
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher EducationCode0
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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