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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1020110250 of 17610 papers

TitleStatusHype
ChatGPT in the context of precision agriculture data analyticsCode0
Heaps' Law in GPT-Neo Large Language Model Emulated CorporaCode0
How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Model0
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related TasksCode0
Argumentation Element Annotation Modeling using XLNet0
AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems0
Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language ModelsCode0
Multimodal Foundation Models Exploit Text to Make Medical Image PredictionsCode0
Deep Natural Language Feature Learning for Interpretable PredictionCode0
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text GenerationCode0
Towards a Taxonomy of Large Language Model based Business Model Transformations0
Labor Space: A Unifying Representation of the Labor Market via Large Language Models0
On the steerability of large language models toward data-driven personas0
NLQxform: A Language Model-based Question to SPARQL TransformerCode0
Zero-shot Translation of Attention Patterns in VQA Models to Natural LanguageCode0
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?0
Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning0
Formal Aspects of Language Modeling0
Aspects of human memory and Large Language ModelsCode0
Conversations in Galician: a Large Language Model for an Underrepresented LanguageCode0
AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems0
Analysis of the User Perception of Chatbots in Education Using A Partial Least Squares Structural Equation Modeling Approach0
Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language ModelsCode0
OLaLa: Ontology Matching with Large Language Models0
Uncovering Intermediate Variables in Transformers using Circuit ProbingCode0
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse FinetuningCode0
Unifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors0
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase0
A Simple yet Efficient Ensemble Approach for AI-generated Text Detection0
Leveraging High-Level Synthesis and Large Language Models to Generate, Simulate, and Deploy a Uniform Random Number Generator Hardware Design0
Mini Minds: Exploring Bebeshka and Zlata Baby ModelsCode0
Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation0
ProPath: Disease-Specific Protein Language Model for Variant Pathogenicity0
LLM-enhanced Self-training for Cross-domain Constituency ParsingCode0
Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language0
Assessing the Promise and Pitfalls of ChatGPT for Automated Code GenerationCode0
CIRCLE: Multi-Turn Query Clarifications with Reinforcement Learning0
Can Chat GPT solve a Linguistics Exam?0
You Only Forward Once: Prediction and Rationalization in A Single Forward Pass0
Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision0
ProSG: Using Prompt Synthetic Gradients to Alleviate Prompt Forgetting of RNN-like Language Models0
Too Much Information: Keeping Training Simple for BabyLMs0
TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine0
Successor Features for Efficient Multisubject Controlled Text Generation0
Supermind Ideator: Exploring generative AI to support creative problem-solving0
Data-Free Distillation of Language Model by Text-to-Text Transfer0
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning0
Efficient Black-Box Adversarial Attacks on Neural Text DetectorsCode0
A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction0
UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified