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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7051–7100 of 17610 papers

TitleStatusHype
Knowledge Tagging with Large Language Model based Multi-Agent System—0
On the Role of Context in Reading Time PredictionCode0
The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal—0
Stable Language Model Pre-training by Reducing Embedding Variability—0
OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering—0
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot SystemsCode0
Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift GeneralizationCode0
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better—0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model—0
FreeRide: Harvesting Bubbles in Pipeline Parallelism—0
Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination—0
Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization—0
Cross-Refine: Improving Natural Language Explanation Generation by Learning in TandemCode0
Mapping Biomedical Ontology Terms to IDs: Effect of Domain Prevalence on Prediction Accuracy—0
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM—0
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models—0
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials—0
MAGDA: Multi-agent guideline-driven diagnostic assistance—0
MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model—0
MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context UnderstandingCode0
Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles—0
Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models—0
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review—0
INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding—0
HierLLM: Hierarchical Large Language Model for Question Recommendation—0
DiPT: Enhancing LLM reasoning through diversified perspective-taking—0
User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study—0
WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical CaseCode0
On the Relationship between Truth and Political Bias in Language ModelsCode0
Regression with Large Language Models for Materials and Molecular Property Prediction—0
SongCreator: Lyrics-based Universal Song Generation—0
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning—0
Leveraging Content and Acoustic Representations for Speech Emotion RecognitionCode0
LegiLM: A Fine-Tuned Legal Language Model for Data ComplianceCode0
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language ModelingCode0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
FairHome: A Fair Housing and Fair Lending Dataset—0
Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models—0
DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning—0
A Small Claims Court for the NLP: Judging Legal Text Classification Strategies With Small Datasets—0
Doppelgänger's Watch: A Split Objective Approach to Large Language Models—0
ELMS: Elasticized Large Language Models On Mobile Devices—0
Interactive Machine Teaching by Labeling Rules and Instances—0
A Hetero-functional Graph Resilience Analysis for Convergent Systems-of-Systems—0
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?Code0
STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting—0
POINTS: Improving Your Vision-language Model with Affordable Strategies—0
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework—0
MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—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