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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6151–6200 of 17610 papers

TitleStatusHype
Architectural Foundations for the Large Language Model Infrastructures—0
Are 2D-LSTM really dead for offline text recognition?—0
Are All Languages Equally Hard to Language-Model?—0
Areas of Attention for Image Captioning—0
A recurrent neural network without chaos—0
A Recursive Recurrent Neural Network for Statistical Machine Translation—0
Are discrete units necessary for Spoken Language Modeling?—0
Scalable Acceleration for Classification-Based Derivative-Free Optimization—0
Are Human Conversations Special? A Large Language Model Perspective—0
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model—0
Are Language Model Logits Calibrated?—0
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?—0
Are Large Language Models the New Interface for Data Pipelines?—0
Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding—0
Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems—0
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan—0
Are Prompt-based Models Clueless?—0
Are Retrials All You Need? Enhancing Large Language Model Reasoning Without Verbalized Feedback—0
A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions—0
A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing—0
A review of on-device fully neural end-to-end automatic speech recognition algorithms—0
The Limitations of Stylometry for Detecting Machine-Generated Fake News—0
Are we there yet? Exploring clinical domain knowledge of BERT models—0
ARGUABLY@SMM4H’22: Classification of Health Related Tweets using Ensemble, Zero-Shot and Fine-Tuned Language Model—0
Argumentation Element Annotation Modeling using XLNet—0
Argumentative texts and clause types—0
Arib@QALB-2015 Shared Task: A Hybrid Cascade Model for Arabic Spelling Error Detection and Correction—0
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models—0
Arithmetic with Language Models: from Memorization to Computation—0
Efficient Controlled Language Generation with Low-Rank Autoregressive Reward Models—0
A Roadmap for Big Model—0
A Robotic Agent in a Virtual Environment that Performs Situated Incremental Understanding of Navigational Utterances—0
ARO: Large Language Model Supervised Robotics Text2Skill Autonomous Learning—0
Diminished Diversity-of-Thought in a Standard Large Language Model—0
Artificial Intuition: Efficient Classification of Scientific Abstracts—0
Artificial Text Detection with Multiple Training Strategies—0
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling—0
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition—0
A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models—0
A Scalable Distributed Syntactic, Semantic, and Lexical Language Model—0
A Scalable Hierarchical Distributed Language Model—0
ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM—0
A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning—0
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding—0
A self-supervised framework for learning whole slide representations—0
A Self-supervised Joint Training Framework for Document Reranking—0
A self-supervised text-vision framework for automated brain abnormality detection—0
A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT—0
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning—0
A Semi Supervised Dialog Act Tagging for Telugu—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