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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 77017750 of 17610 papers

TitleStatusHype
Taming LLMs with Negative Samples: A Reference-Free Framework to Evaluate Presentation Content with Actionable Feedback0
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution0
Taming the Beast: Learning to Control Neural Conversational Models0
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References0
TAPHSIR: Towards AnaPHoric Ambiguity Detection and ReSolution In Requirements0
TapType: Ten-finger text entry on everyday surfaces via Bayesian inference0
Target-Aware Data Augmentation for Stance Detection0
Target-Aware Language Modeling via Granular Data Sampling0
Target-Centric Features for Translation Quality Estimation0
Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion0
Target-driven Attack for Large Language Models0
Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge0
Target Foresight Based Attention for Neural Machine Translation0
Target Prompting for Information Extraction with Vision Language Model0
Target-Side Generation of Prepositions for SMT0
TA-SBERT: Token Attention Sentence-BERT for Improving Sentence Representation0
Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling0
Task-Agnostic and Adaptive-Size BERT Compression0
Task-Agnostic Language Model Watermarking via High Entropy Passthrough Layers0
Task Alternation in Parallel Sentence Retrieval for Twitter Translation0
TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection0
Task Facet Learning: A Structured Approach to Prompt Optimization0
Task formulation for Extracting Social Determinants of Health from Clinical Narratives0
Task Supportive and Personalized Human-Large Language Model Interaction: A User Study0
Taste of Two Different Flavours: Which Manipuri Script works better for English-Manipuri Language pair SMT Systems?0
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data0
Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training0
TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling0
Taxonomy-Aware Evaluation of Vision-Language Models0
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language0
SSDTrain: An Activation Offloading Framework to SSDs for Faster Large Language Model Training0
TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine0
TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing0
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction0
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise0
Teaching a Language Model to Distinguish Between Similar Details using a Small Adversarial Training Set0
Teaching a Massive Open Online Course on Natural Language Processing0
Teaching Language Models to Self-Improve by Learning from Language Feedback0
Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning0
Teach Me What to Say and I Will Learn What to Pick: Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models0
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings0
Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach0
TEAM HUB@LT-EDI-EACL2021: Hope Speech Detection Based On Pre-trained Language Model0
Team JUST at the MADAR Shared Task on Arabic Fine-Grained Dialect Identification0
Team “NoConflict” at CASE 2021 Task 1: Pretraining for Sentence-Level Protest Event Detection0
Teamwork Dimensions Classification Using BERT0
TeaserGen: Generating Teasers for Long Documentaries0
Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation0
Technical Report -- Competition Solution for Prompt Tuning using Pretrained Language Model0
Technical Report: Evaluating Goal Drift in Language Model Agents0
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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