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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9301–9350 of 17610 papers

TitleStatusHype
Language Model Adaptation to Specialized Domains through Selective Masking based on Genre and Topical CharacteristicsCode0
Uncovering Latent Human Wellbeing in Language Model Embeddings—0
Purifying Large Language Models by Ensembling a Small Language Model—0
Ploutos: Towards interpretable stock movement prediction with financial large language model—0
Modelling Political Coalition Negotiations Using LLM-based Agents—0
scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation—0
KMMLU: Measuring Massive Multitask Language Understanding in Korean—0
Large Language Model-driven Meta-structure Discovery in Heterogeneous Information NetworkCode0
Multi-dimensional Evaluation of Empathetic Dialog Responses—0
Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models—0
MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization—0
Autocorrect for Estonian texts: final report from project EKTB25—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models—0
Extensible Embedding: A Flexible Multipler For LLM's Context Length—0
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation ExtractionCode0
I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated ResponsesCode0
MMMModal -- Multi-Images Multi-Audio Multi-turn Multi-Modal—0
Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention—0
Question-Instructed Visual Descriptions for Zero-Shot Video Question AnsweringCode0
Understanding In-Context Learning with a Pelican Soup Framework—0
SPAR: Personalized Content-Based Recommendation via Long Engagement Attention—0
QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning—0
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models—0
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement—0
Towards Uncovering How Large Language Model Works: An Explainability Perspective—0
Speculative Streaming: Fast LLM Inference without Auxiliary Models—0
PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter—0
Instruction Diversity Drives Generalization To Unseen Tasks—0
An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model InferenceCode0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
Word Embeddings Revisited: Do LLMs Offer Something New?—0
Visually Dehallucinative Instruction Generation: Know What You Don't KnowCode0
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment—0
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models—0
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data—0
Language Models with Conformal Factuality Guarantees—0
Multi-Fidelity Methods for Optimization: A Survey—0
Quantized Embedding Vectors for Controllable Diffusion Language Models—0
A Federated Framework for LLM-based RecommendationCode0
Reward Generalization in RLHF: A Topological Perspective—0
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General IntelligenceCode0
Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization—0
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence—0
Generative AI in the Construction Industry: A State-of-the-art Analysis—0
DE-COP: Detecting Copyrighted Content in Language Models Training DataCode0
Any-Shift Prompting for Generalization over Distributions—0
Grounding Language Model with Chunking-Free In-Context Retrieval—0
Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks—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