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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6801–6850 of 17610 papers

TitleStatusHype
AnyGPT: Unified Multimodal LLM with Discrete Sequence ModelingCode4
Ploutos: Towards interpretable stock movement prediction with financial large language model—0
Stealthy Attack on Large Language Model based RecommendationCode1
scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation—0
Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models—0
Large Language Model-driven Meta-structure Discovery in Heterogeneous Information NetworkCode0
Modelling Political Coalition Negotiations Using LLM-based Agents—0
MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
Momentor: Advancing Video Large Language Model with Fine-Grained Temporal ReasoningCode2
Integrating Pre-Trained Language Model with Physical Layer CommunicationsCode1
Extensible Embedding: A Flexible Multipler For LLM's Context Length—0
KMMLU: Measuring Massive Multitask Language Understanding in Korean—0
Multi-dimensional Evaluation of Empathetic Dialog Responses—0
PreAct: Prediction Enhances Agent's Planning AbilityCode1
BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models—0
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data AugmentationCode1
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model EvaluationCode1
Autocorrect for Estonian texts: final report from project EKTB25—0
ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language ModelsCode3
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation ExtractionCode0
Controlled Text Generation for Large Language Model with Dynamic Attribute GraphsCode1
Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention—0
MMMModal -- Multi-Images Multi-Audio Multi-turn Multi-Modal—0
LaCo: Large Language Model Pruning via Layer CollapseCode1
Offline Training of Language Model Agents with Functions as Learnable WeightsCode1
I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated ResponsesCode0
ZeroG: Investigating Cross-dataset Zero-shot Transferability in GraphsCode1
Dissecting Human and LLM PreferencesCode1
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
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
Word Embeddings Revisited: Do LLMs Offer Something New?—0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
Speculative Streaming: Fast LLM Inference without Auxiliary Models—0
Linear Transformers with Learnable Kernel Functions are Better In-Context ModelsCode2
Question-Instructed Visual Descriptions for Zero-Shot Video Question AnsweringCode0
PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter—0
QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning—0
Understanding In-Context Learning with a Pelican Soup Framework—0
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language ModelCode2
Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language ModelsCode1
Instruction Diversity Drives Generalization To Unseen Tasks—0
Towards Uncovering How Large Language Model Works: An Explainability Perspective—0
Direct Preference Optimization with an OffsetCode1
An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model InferenceCode0
SPAR: Personalized Content-Based Recommendation via Long Engagement Attention—0
Language Models with Conformal Factuality Guarantees—0
Analyzing the Roles of Language and Vision in Learning from Limited Data—0
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data—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