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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9401–9450 of 17610 papers

TitleStatusHype
Who's to say what's funny? A computer using Language Models and Deep Learning, That's Who!—0
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings—0
ViLAaD: Enhancing "Attracting and Dispersing'' Source-Free Domain Adaptation with Vision-and-Language Model—0
Universality and Limitations of Prompt Tuning—0
Universal Language Model Fine-tuning for Patent Classification—0
Unsupervised Human Preference Learning—0
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One—0
Who Writes the Review, Human or AI?—0
ViLLM-Eval: A Comprehensive Evaluation Suite for Vietnamese Large Language Models—0
ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models—0
You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet—0
ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation—0
You Only Forward Once: Prediction and Rationalization in A Single Forward Pass—0
Vi-Mistral-X: Building a Vietnamese Language Model with Advanced Continual Pre-training—0
VinaLLaMA: LLaMA-based Vietnamese Foundation Model—0
You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with a Multi-Agent Conversations—0
Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese—0
VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation—0
Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder—0
ViPer: Visual Personalization of Generative Models via Individual Preference Learning—0
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore—0
Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seeking—0
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model—0
Universal Language Modelling agent—0
Why Are Positional Encodings Nonessential for Deep Autoregressive Transformers? Revisiting a Petroglyph—0
Virtual Scientific Companion for Synchrotron Beamlines: A Prototype—0
Universal language model with the intervention of quantum theory—0
Writing user personas with Large Language Models: Testing phase 6 of a Thematic Analysis of semi-structured interviews—0
Vision and Intention Boost Large Language Model in Long-Term Action Anticipation—0
Why do LLaVA Vision-Language Models Reply to Images in English?—0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
Vision-centric Token Compression in Large Language Model—0
Universal Self-Consistency for Large Language Model Generation—0
VisionGPT: Vision-Language Understanding Agent Using Generalized Multimodal Framework—0
Vision-Integrated LLMs for Autonomous Driving Assistance : Human Performance Comparison and Trust Evaluation—0
Vision-Language Adaptive Mutual Decoder for OOV-STR—0
Universal Sentence Representation Learning with Conditional Masked Language Model—0
Vision-language Assisted Attribute Learning—0
Universal Sentence Representations Learning with Conditional Masked Language Model—0
Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction—0
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Early Lung Cancer Detection—0
Why do small language models underperform? Studying Language Model Saturation via the Softmax Bottleneck—0
Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces—0
Unified Multi-Task Learning & Model Fusion for Efficient Language Model Guardrailing—0
Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives—0
Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment—0
Vision-Language Modeling with Regularized Spatial Transformer Networks for All Weather Crosswind Landing of Aircraft—0
Vision-Language Model IP Protection via Prompt-based Learning—0
Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime—0
UMDFood: Vision-language models boost food composition compilation—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