SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8651–8700 of 17610 papers

TitleStatusHype
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
HLAT: High-quality Large Language Model Pre-trained on AWS TrainiumCode0
Generative Text Steganography with Large Language Model—0
Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering—0
From a Lossless (~1.5:1) Compression Algorithm for Llama2 7B Weights to Variable Precision, Variable Range, Compressed Numeric Data Types for CNNs and LLMs—0
Vocabulary-free Image Classification and Semantic SegmentationCode0
White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs—0
Unifying Global and Local Scene Entities Modelling for Precise Action SpottingCode0
UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark—0
Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model—0
PRODIS - a speech database and a phoneme-based language model for the study of predictability effects in Polish—0
Language Model Cascades: Token-level uncertainty and beyond—0
Negation Triplet Extraction with Syntactic Dependency and Semantic ConsistencyCode0
Learn Your Reference Model for Real Good Alignment—0
ChatShop: Interactive Information Seeking with Language Agents—0
Evolving Interpretable Visual Classifiers with Large Language Models—0
A Self-feedback Knowledge Elicitation Approach for Chemical Reaction PredictionsCode0
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection—0
DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection—0
Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission—0
Customising General Large Language Models for Specialised Emotion Recognition TasksCode0
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language RepresentationCode0
Compass: Large Multilingual Language Model for South-east Asia—0
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts—0
Self-Selected Attention Span for Accelerating Large Language Model Inference—0
JaFIn: Japanese Financial Instruction Dataset—0
Test Code Generation for Telecom Software Systems using Two-Stage Generative Model—0
TEXT2TASTE: A Versatile Egocentric Vision System for Intelligent Reading Assistance Using Large Language Model—0
ToNER: Type-oriented Named Entity Recognition with Generative Language ModelCode0
Leveraging Large Language Model as Simulated Patients for Clinical Education—0
On Speculative Decoding for Multimodal Large Language Models—0
LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous Space ExplorationCode0
ChimpVLM: Ethogram-Enhanced Chimpanzee Behaviour Recognition—0
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model—0
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision—0
Emerging Property of Masked Token for Effective Pre-training—0
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation—0
Measuring the Quality of Answers in Political Q&As with Large Language Models—0
CUDA-Accelerated Soft Robot Neural Evolution with Large Language Model Supervision—0
Language Model Prompt Selection via Simulation Optimization—0
Training a Vision Language Model as Smartphone Assistant—0
Thematic Analysis with Large Language Models: does it work with languages other than English? A targeted test in Italian—0
Toward a Theory of Tokenization in LLMs—0
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs—0
Pretraining and Updates of Domain-Specific LLM: A Case Study in the Japanese Business Domain—0
CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes—0
Post-Hoc Reversal: Are We Selecting Models Prematurely?Code0
On Unified Prompt Tuning for Request Quality Assurance in Public Code Review—0
RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data—0
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models—0
Show:102550
← PrevPage 174 of 353Next →
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