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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5951–6000 of 17610 papers

TitleStatusHype
Krony-PT: GPT2 compressed with Kronecker Products—0
LMM-Regularized CLIP Embeddings for Image Classification—0
Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recovery—0
Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval—0
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts—0
Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Embracing Large Language Models in Traffic Flow Forecasting—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
Inference Scaling for Bridging Retrieval and Augmented Generation—0
Human-Centric NLP or AI-Centric Illusion?: A Critical Investigation—0
Bridging Vision and Language: Modeling Causality and Temporality in Video Narratives—0
Large Language Models for Medical Forecasting -- Foresight 2—0
Learning to Verify Summary Facts with Fine-Grained LLM FeedbackCode0
Optimizing Vision-Language Interactions Through Decoder-Only Models—0
Superhuman performance of a large language model on the reasoning tasks of a physician—0
WEPO: Web Element Preference Optimization for LLM-based Web Navigation—0
WHAT-IF: Exploring Branching Narratives by Meta-Prompting Large Language Models—0
B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal TokensCode0
EVLM: Self-Reflective Multimodal Reasoning for Cross-Dimensional Visual Editing—0
Solving the Inverse Alignment Problem for Efficient RLHF—0
Small Language Model as Data Prospector for Large Language Model—0
Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model—0
PickLLM: Context-Aware RL-Assisted Large Language Model Routing—0
Regulation of Language Models With Interpretability Will Likely Result In A Performance Trade-OffCode0
Language model driven: a PROTAC generation pipeline with dual constraints of structure and property—0
Learning Novel Skills from Language-Generated Demonstrations—0
When Text Embedding Meets Large Language Model: A Comprehensive Survey—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials—0
Dialogue Language Model with Large-Scale Persona Data Engineering—0
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks—0
Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering—0
CareBot: A Pioneering Full-Process Open-Source Medical Language Model—0
Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis—0
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework—0
Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning—0
Position-aware Guided Point Cloud Completion with CLIP Model—0
Multimodal Latent Language Modeling with Next-Token DiffusionCode0
LatentQA: Teaching LLMs to Decode Activations Into Natural Language—0
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs—0
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs—0
POINTS1.5: Building a Vision-Language Model towards Real World Applications—0
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning—0
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models—0
Preference Adaptive and Sequential Text-to-Image Generation—0
The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model—0
KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context—0
Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents—0
Neural Scaling Laws Rooted in the Data DistributionCode0
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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