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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6851–6900 of 17610 papers

TitleStatusHype
ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical FeedbackCode2
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models—0
On the Vulnerability of LLM/VLM-Controlled RoboticsCode7
DE-COP: Detecting Copyrighted Content in Language Models Training DataCode0
Visually Dehallucinative Instruction Generation: Know What You Don't KnowCode0
OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language ModelsCode3
A Federated Framework for LLM-based RecommendationCode0
Generative Representational Instruction TuningCode4
Multi-Fidelity Methods for Optimization: A Survey—0
Generative AI in the Construction Industry: A State-of-the-art Analysis—0
Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4—0
Reward Generalization in RLHF: A Topological Perspective—0
Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization—0
Fast Vocabulary Transfer for Language Model CompressionCode1
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment—0
Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks—0
Quantized Embedding Vectors for Controllable Diffusion Language Models—0
Grounding Language Model with Chunking-Free In-Context Retrieval—0
Any-Shift Prompting for Generalization over Distributions—0
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General IntelligenceCode0
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence—0
Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model—0
A Language Model for Particle Tracking—0
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-raysCode0
Instruction Backdoor Attacks Against Customized LLMsCode1
Multi-Query Focused Disaster Summarization via Instruction-Based Prompting—0
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems—0
Massively Multi-Cultural Knowledge Acquisition & LM BenchmarkingCode1
Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text AnalysisCode1
AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and GuardrailsCode0
Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications—0
Rethinking Large Language Model Architectures for Sequential Recommendations—0
Large Language Model Simulator for Cold-Start Recommendation—0
Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies—0
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof DataCode1
Open-Vocabulary Segmentation with Unpaired Mask-Text SupervisionCode2
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven AgentsCode2
Large Language Model with Graph Convolution for Recommendation—0
FGeo-TP: A Language Model-Enhanced Solver for Geometry Problems—0
FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep Reinforcement LearningCode0
Switch EMA: A Free Lunch for Better Flatness and SharpnessCode1
AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems—0
Measuring and Controlling Instruction (In)Stability in Language Model DialogsCode1
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs—0
Visual Question Answering Instruction: Unlocking Multimodal Large Language Model To Domain-Specific Visual Multitasks—0
The Application of ChatGPT in Responding to Questions Related to the Boston Bowel Preparation Scale—0
VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree SearchCode3
Visually Dehallucinative Instruction GenerationCode0
Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models—0
LLMs and Stack Overflow Discussions: Reliability, Impact, and Challenges—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