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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9951–10000 of 17610 papers

TitleStatusHype
Evaluating and Mitigating Discrimination in Language Model Decisions—0
Corporate Bankruptcy Prediction with Domain-Adapted BERT—0
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model—0
A Hardware Evaluation Framework for Large Language Model Inference—0
GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science—0
FG-MDM: Towards Zero-Shot Human Motion Generation via ChatGPT-Refined Descriptions—0
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models—0
Visually Grounded Language Learning: a review of language games, datasets, tasks, and models—0
Large Knowledge Model: Perspectives and Challenges—0
Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic—0
Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence—0
Scaling Laws for Adversarial Attacks on Language Model Activations—0
RINAS: Training with Dataset Shuffling Can Be General and Fast—0
Jellyfish: A Large Language Model for Data Preprocessing—0
Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings—0
Revisiting Topic-Guided Language ModelsCode0
The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model—0
TPPoet: Transformer-Based Persian Poem Generation using Minimal Data and Advanced Decoding Techniques—0
Semantics-aware Motion Retargeting with Vision-Language Models—0
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation—0
A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly—0
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language ModelsCode0
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with FakepediaCode0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication AwarenessCode0
Intelligent Virtual Assistants with LLM-based Process Automation—0
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment—0
VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding—0
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario—0
SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects—0
SymNoise: Advancing Language Model Fine-tuning with Symmetric Noise—0
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERTCode0
From Beginner to Expert: Modeling Medical Knowledge into General LLMs—0
Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis—0
Can We Learn Communication-Efficient Optimizers?Code0
Prompt Tuning for Zero-shot Compositional Learning—0
Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model—0
Manipulating the Label Space for In-Context Classification—0
Open-vocabulary object 6D pose estimation—0
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals—0
LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices—0
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—0
Zero-Shot Video Question Answering with Procedural Programs—0
Context Retrieval via Normalized Contextual Latent Interaction for Conversational AgentCode0
Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Time—0
Hyperparameter Optimization for Large Language Model Instruction-Tuning—0
Hashmarks: Privacy-Preserving Benchmarks for High-Stakes AI Evaluation—0
Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses—0
Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games—0
A Bayesian approach for prompt optimization in pre-trained language models—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