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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 9151–9200 of 17610 papers

TitleStatusHype
Fantastic Semantics and Where to Find Them: Investigating Which Layers of Generative LLMs Reflect Lexical SemanticsCode0
Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval—0
AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks—0
Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning—0
Chaining thoughts and LLMs to learn DNA structural biophysicsCode0
Towards Accurate Lip-to-Speech Synthesis in-the-Wild—0
SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code—0
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation TrainingCode0
Enhancing Jailbreak Attacks with Diversity Guidance—0
Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation ModelCode0
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs—0
BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs)—0
FAC^2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition—0
A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration—0
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models—0
VIXEN: Visual Text Comparison Network for Image Difference CaptioningCode0
Leveraging pre-trained language models for code generationCode0
LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction—0
The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?—0
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model—0
PaECTER: Patent-level Representation Learning using Citation-informed Transformers—0
PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval—0
Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Information—0
MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery—0
Prospect Personalized Recommendation on Large Language Model-based Agent PlatformCode0
Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem—0
Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScoreCode0
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling—0
Merino: Entropy-driven Design for Generative Language Models on IoT Devices—0
Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners—0
Data Interpreter: An LLM Agent For Data Science—0
Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization—0
ICE-SEARCH: A Language Model-Driven Feature Selection Approach—0
A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems—0
Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction—0
VCD: Knowledge Base Guided Visual Commonsense Discovery in Images—0
MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical ReasoningCode0
Large Language Model for Participatory Urban Planning—0
Towards Optimal Learning of Language Models—0
Prediction-Powered Ranking of Large Language ModelsCode0
Stable LM 2 1.6B Technical ReportCode0
OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web—0
A Neural Rewriting System to Solve Algorithmic Problems—0
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents—0
Acquiring Linguistic Knowledge from Multimodal Input—0
Automated Statistical Model Discovery with Language Models—0
ESG Sentiment Analysis: comparing human and language model performance including GPT—0
A Comprehensive Evaluation of Quantization Strategies for Large Language ModelsCode0
GROUNDHOG: Grounding Large Language Models to Holistic Segmentation—0
Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning—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