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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10751–10800 of 17610 papers

TitleStatusHype
Real or Fake? Learning to Discriminate Machine from Human Generated Text—0
Real Time Adaptive Machine Translation for Post-Editing with cdec and TransCenter—0
Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?—0
Real-time Monitoring of Economic Shocks using Company Websites—0
Real-time Neural-based Input Method—0
Real-Time Optimized N-gram For Mobile Devices—0
Realtime query completion via deep language models—0
Real-Time Statistical Speech Translation—0
Real-time Verification and Refinement of Language Model Text Generation—0
Real-World Cooking Robot System from Recipes Based on Food State Recognition Using Foundation Models and PDDL—0
Real-world Deployment and Evaluation of PErioperative AI CHatbot (PEACH) -- a Large Language Model Chatbot for Perioperative Medicine—0
Real-World Offline Reinforcement Learning from Vision Language Model Feedback—0
REAPER: Reasoning based Retrieval Planning for Complex RAG Systems—0
Reasoning Circuits: Few-shot Multihop Question Generation with Structured Rationales—0
Reasoning in Transformers - Mitigating Spurious Correlations and Reasoning Shortcuts—0
Reasoning, Memorization, and Fine-Tuning Language Models for Non-Cooperative Games—0
Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering—0
Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning—0
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations—0
Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths—0
Reasoning with Latent Thoughts: On the Power of Looped Transformers—0
Reassessing Large Language Model Boolean Query Generation for Systematic Reviews—0
Re-assessing the Impact of SMT Techniques with Human Evaluation: a Case Study on English---Croatian—0
RE: A Study for Restorable Embeddings—0
Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain—0
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing—0
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods—0
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction—0
Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents—0
Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations—0
Recent advances in deep learning and language models for studying the microbiome—0
Recent Advances in Discrete Speech Tokens: A Review—0
Dissecting Transformer Length Extrapolation via the Lens of Receptive Field Analysis—0
Recipe for Zero-shot POS Tagging: Is It Useful in Realistic Scenarios?—0
Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models—0
RecMind: Large Language Model Powered Agent For Recommendation—0
RecoBERT: A Catalog Language Model for Text-Based Recommendations—0
Recognize Foreign Low-Frequency Words with Similar Pairs—0
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack—0
Recognizing Open-Vocabulary Relations between Objects in Images—0
Recognizing Semantic Relations by Combining Transformers and Fully Connected Models—0
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach—0
Recommendations by Concise User Profiles from Review Text—0
Recommender Algorithm for Supporting Self-Management of CVD Risk Factors in an Adult Population at Home—0
Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence—0
Reconsidering the Past: Optimizing Hidden States in Language Models—0
Recourse for reclamation: Chatting with generative language models—0
RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care—0
Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints—0
Recovering from Privacy-Preserving Masking with 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