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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 47514800 of 17610 papers

TitleStatusHype
CUDA-Accelerated Soft Robot Neural Evolution with Large Language Model Supervision0
Cue-bot: A Conversational Agent for Assistive Technology0
CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages0
Cued@wmt19:ewc&lms0
CUED@WMT19:EWC\&LMs0
CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model0
Cue Me In: Content-Inducing Approaches to Interactive Story Generation0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification0
CUIfy the XR: An Open-Source Package to Embed LLM-powered Conversational Agents in XR0
Cumulative Progress in Language Models for Information Retrieval0
CUNI in WMT14: Chimera Still Awaits Bellerophon0
CurateGPT: A flexible language-model assisted biocuration tool0
Current Topological and Machine Learning Applications for Bias Detection in Text0
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding0
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding0
Curriculum-Based Neighborhood Sampling For Sequence Prediction0
Curriculum Design for Code-switching: Experiments with Language Identification and Language Modeling with Deep Neural Networks0
Customer Sentiment Analysis using Weak Supervision for Customer-Agent Chat0
Customising General Large Language Models for Specialised Emotion Recognition Tasks0
Customizing a Large Language Model for VHDL Design of High-Performance Microprocessors0
Customizing Contextualized Language Models forLegal Document Reviews0
Customizing Language Model Responses with Contrastive In-Context Learning0
Customizing Large Language Model Generation Style using Parameter-Efficient Finetuning0
Customizing Speech Recognition Model with Large Language Model Feedback0
Concealed Data Poisoning Attacks on NLP Models0
Cut the CARP: Fishing for zero-shot story evaluation0
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems0
Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation0
Cutting the Long Tail: Hybrid Language Models for Translation Style Adaptation0
CVE-driven Attack Technique Prediction with Semantic Information Extraction and a Domain-specific Language Model0
CVE-LLM : Automatic vulnerability evaluation in medical device industry using large language models0
CxLM: A Construction and Context-aware Language Model0
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features0
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment0
Cycle-consistency training for end-to-end speech recognition0
CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain0
Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker0
CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning0
D3PO: Preference-Based Alignment of Discrete Diffusion Models0
D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents0
DACT-BERT: Increasing the efficiency and interpretability of BERT by using adaptive computation time.0
DaG LLM ver 1.0: Pioneering Instruction-Tuned Language Modeling for Korean NLP0
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase0
Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic0
DamascusTeam at NLP4IF2021: Fighting the Arabic COVID-19 Infodemic on Twitter Using AraBERT0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning0
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition0
DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity Recognition0
DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified