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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8401–8450 of 17610 papers

TitleStatusHype
TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models—0
Triangular Transfer: Freezing the Pivot for Triangular Machine Translation—0
TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents—0
TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners—0
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER—0
Trojan Detection Through Pattern Recognition for Large Language Models—0
TrojanRobot: Physical-World Backdoor Attacks Against VLM-based Robotic Manipulation—0
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning—0
TRRG: Towards Truthful Radiology Report Generation With Cross-modal Disease Clue Enhanced Large Language Model—0
Truecasing German user-generated conversational text—0
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking—0
Trust but Verify: Programmatic VLM Evaluation in the Wild—0
Trust, Experience, and Innovation: Key Factors Shaping American Attitudes About AI—0
Trustless Autonomy: Understanding Motivations, Benefits and Governance Dilemma in Self-Sovereign Decentralized AI Agents—0
Trust No AI: Prompt Injection Along The CIA Security Triad—0
Truth Machines: Synthesizing Veracity in AI Language Models—0
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task—0
t-SOT FNT: Streaming Multi-talker ASR with Text-only Domain Adaptation Capability—0
T\"UB\.ITAK-B\.ILGEM German-English Machine Translation Systems for W13—0
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection—0
Tunable Distortion Limits and Corpus Cleaning for SMT—0
Tunable Soft Prompts are Messengers in Federated Learning—0
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect—0
Tuning a Grammar Correction System for Increased Precision—0
Tuning Language Models by Mixture-of-Depths Ensemble—0
Tuning Large language model for End-to-end Speech Translation—0
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs—0
Turkish Resources for Visual Word Recognition—0
Turn-Level Empathy Prediction Using Psychological Indicators—0
Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion—0
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model—0
TwIPS: A Large Language Model Powered Texting Application to Simplify Conversational Nuances for Autistic Users—0
Twitter Translation using Translation-Based Cross-Lingual Retrieval—0
TWIZ-v2: The Wizard of Multimodal Conversational-Stimulus—0
Two Discourse Driven Language Models for Semantics—0
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment—0
Two Improvements to Left-to-Right Decoding for Hierarchical Phrase-based Machine Translation—0
Two-in-One: A Model Hijacking Attack Against Text Generation Models—0
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study—0
Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation—0
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia—0
Two-Step Machine Translation with Lattices—0
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions—0
Tx-LLM: A Large Language Model for Therapeutics—0
Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models—0
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model—0
UBC-NLP at IEST 2018: Learning Implicit Emotion With an Ensemble of Language Models—0
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus—0
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training—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