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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14351–14400 of 17610 papers

TitleStatusHype
ROMUL: Scale Adaptative Population Based Training—0
RoQLlama: A Lightweight Romanian Adapted Language Model—0
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models—0
Rosetta-PL: Propositional Logic as a Benchmark for Large Language Model Reasoning—0
Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word--Definition Alignment—0
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications—0
Routers in Vision Mixture of Experts: An Empirical Study—0
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model—0
RTM results for Predicting Translation Performance—0
Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records—0
Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System—0
Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin—0
Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation—0
Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models—0
Run LoRA Run: Faster and Lighter LoRA Implementations—0
RWKV-UI: UI Understanding with Enhanced Perception and Reasoning—0
R+X: Retrieval and Execution from Everyday Human Videos—0
S^2ALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning—0
S2vNTM: Semi-supervised vMF Neural Topic Modeling—0
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs—0
S^3: Increasing GPU Utilization during Generative Inference for Higher Throughput—0
S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Model with Spatio-Temporal Visual Representation—0
Sabiá-3 Technical Report—0
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection—0
Sadeed: Advancing Arabic Diacritization Through Small Language Model—0
SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability—0
Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness—0
SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety—0
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning—0
SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects—0
SAGEval: The frontiers of Satisfactory Agent based NLG Evaluation for reference-free open-ended text—0
SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm—0
SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features—0
SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills—0
LLM Whisperer: An Inconspicuous Attack to Bias LLM Responses—0
Salient Span Masking for Temporal Understanding—0
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation—0
Salutary Labeling with Zero Human Annotation—0
SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection—0
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models—0
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models—0
Sampling Informative Training Data for RNN Language Models—0
Efficient and Training-Free Control of Language Generation—0
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation—0
SAN: a robust end-to-end ASR model architecture—0
Sarang at DEFACTIFY 4.0: Detecting AI-Generated Text Using Noised Data and an Ensemble of DeBERTa Models—0
SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills—0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping—0
SaulLM-7B: A pioneering Large Language Model for Law—0
SAVAS: Collecting, Annotating and Sharing Audiovisual Language Resources for Automatic Subtitling—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