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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4951–5000 of 17610 papers

TitleStatusHype
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition—0
Dense Recurrent Neural Network with Attention Gate—0
Dependency Annotation of Ottoman Turkish with Multilingual BERT—0
Dependency-Based Bilingual Language Models for Reordering in Statistical Machine Translation—0
Dependency-Based Decipherment for Resource-Limited Machine Translation—0
Dependency-Based Word Embeddings—0
Dependency grammars as Haskell programs—0
Dependency Language Models for Sentence Completion—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
Dependency Parsing with Graph Rewriting—0
DePlot: One-shot visual language reasoning by plot-to-table translation—0
Deploying Multi-task Online Server with Large Language Model—0
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach—0
DePrompt: Desensitization and Evaluation of Personal Identifiable Information in Large Language Model Prompts—0
DEPT: Decoupled Embeddings for Pre-training Language Models—0
Depth-Gated LSTM—0
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents—0
DEREC-SIMPRO: unlock Language Model benefits to advance Synthesis in Data Clean Room—0
Derivational Smoothing for Syntactic Distributional Semantics—0
Derivation of Document Vectors from Adaptation of LSTM Language Model—0
Deriving Adjectival Scales from Continuous Space Word Representations—0
Deriving Neural Architectures from Sequence and Graph Kernels—0
Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment—0
Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs—0
DeServe: Towards Affordable Offline LLM Inference via Decentralization—0
Designing and Evaluating Multi-Chatbot Interface for Human-AI Communication: Preliminary Findings from a Persuasion Task—0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
Design of AI-Powered Tool for Self-Regulation Support in Programming Education—0
Design of an Input Method for Taiwanese Hokkien using Unsupervized Word Segmentation for Language Modeling—0
Design principles of an open-source language modeling microservice package for AAC text-entry applications—0
Designs and Implementations in Neural Network-based Video Coding—0
DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding—0
Detailed Human-Centric Text Description-Driven Large Scene Synthesis—0
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment—0
DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection—0
Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model—0
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention—0
Detecting annotation noise in automatically labelled data—0
Detecting Bias in Large Language Models: Fine-tuned KcBERT—0
Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text—0
Detecting Cloud Presence in Satellite Images Using the RGB-based CLIP Vision-Language Model—0
Detecting Conceptual Abstraction in LLMs—0
Detecting Domain Dedicated Polar Words—0
Detecting ESG topics using domain-specific language models and data augmentation approaches—0
Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning—0
Detecting Machine-Translated Paragraphs by Matching Similar Words—0
Detecting Natural Language Biases with Prompt-based Learning—0
Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model—0
Detecting over/under-translation errors for determining adequacy in human translations—0
Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large 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