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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10701–10750 of 17610 papers

TitleStatusHype
Radiology-GPT: A Large Language Model for Radiology—0
Radiology-Llama2: Best-in-Class Large Language Model for Radiology—0
Radio: Rate-Distortion Optimization for Large Language Model Compression—0
RadLex Normalization in Radiology Reports—0
RadLing: Towards Efficient Radiology Report Understanding—0
RadOnc-GPT: A Large Language Model for Radiation Oncology—0
RadPhi-3: Small Language Models for Radiology—0
RAEE: A Robust Retrieval-Augmented Early Exiting Framework for Efficient Inference—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
RAG-based Question Answering over Heterogeneous Data and Text—0
RAG Does Not Work for Enterprises—0
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation—0
RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines—0
Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation—0
RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis—0
Random Feature Attention—0
Random Language Model—0
Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Information—0
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection—0
Rank and run-time aware compression of NLP Applications—0
Ranking LLMs by compression—0
Confidence Diagram of Nonparametric Ranking for Uncertainty Assessment in Large Language Models Evaluation—0
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking—0
RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot—0
Rapid Biomedical Research Classification: The Pandemic PACT Advanced Categorisation Engine—0
Rapid Word Learning Through Meta In-Context Learning—0
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings—0
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings—0
Rationale Behind Essay Scores: Enhancing S-LLM's Multi-Trait Essay Scoring with Rationale Generated by LLMs—0
Rationalization Models for Text-to-SQL—0
Rationalizing Predictions by Adversarial Information Calibration—0
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model—0
R-Bench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation—0
R-BERT-CNN: Drug-target interactions extraction from biomedical literature—0
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models—0
Towards Data-Centric Automatic R&D—0
RDBE: Reasoning Distillation-Based Evaluation Enhances Automatic Essay Scoring—0
Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation—0
Reactor Mk.1 performances: MMLU, HumanEval and BBH test results—0
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing—0
Readability Classification for German using Lexical, Syntactic, and Morphological Features—0
Readability Controllable Biomedical Document Summarization—0
Reading Is Believing: Revisiting Language Bottleneck Models for Image Classification—0
Read Like a Radiologist: Efficient Vision-Language Model for 3D Medical Imaging Interpretation—0
Real2Code: Reconstruct Articulated Objects via Code Generation—0
ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation—0
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding—0
Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model—0
Real Life Application of a Question Answering System Using BERT Language Model—0
ReALM: Reference Resolution As Language Modeling—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