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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 24012450 of 17610 papers

TitleStatusHype
Exploiting Novel GPT-4 APIsCode1
Exploring and Predicting Transferability across NLP TasksCode1
APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUsCode1
A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language ProcessingCode1
Explaining Answers with Entailment TreesCode1
2SSP: A Two-Stage Framework for Structured Pruning of LLMsCode1
ExpertQA: Expert-Curated Questions and Attributed AnswersCode1
Explaining Datasets in Words: Statistical Models with Natural Language ParametersCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model CommunicationCode1
Evolutionary Large Language Model for Automated Feature TransformationCode1
Excuse me, sir? Your language model is leaking (information)Code1
Event Causality Identification via Derivative Prompt Joint LearningCode1
Evaluation Benchmarks for Spanish Sentence RepresentationsCode1
Evaluation of large language models for discovery of gene set functionCode1
Emergent Representations of Program Semantics in Language Models Trained on ProgramsCode1
Evaluating Retrieval Quality in Retrieval-Augmented GenerationCode1
Evaluating Morphological Alignment of Tokenizers in 70 LanguagesCode1
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social MediaCode1
Mathfish: Evaluating Language Model Math Reasoning via Grounding in Educational CurriculaCode1
A Generalizable Approach to Learning OptimizersCode1
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language ModelCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
Evaluating the Robustness of Retrieval Pipelines with Query Variation GeneratorsCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Factorized Neural Transducer for Efficient Language Model AdaptationCode1
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics SimulationsCode1
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language ModelCode1
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
Establishing baselines for generative discovery of inorganic crystalsCode1
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language ModelCode1
Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model EvaluationCode1
Evaluating Language Model Context Windows: A "Working Memory" Test and Inference-time CorrectionCode1
Evaluating Language Model Finetuning Techniques for Low-resource LanguagesCode1
Evaluating Language Models for Mathematics through InteractionsCode1
Euphemistic Phrase Detection by Masked Language ModelCode1
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack FrameworkCode1
Espresso: A Fast End-to-end Neural Speech Recognition ToolkitCode1
EscapeBench: Pushing Language Models to Think Outside the BoxCode1
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM FamilyCode1
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market DomainCode1
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence GenerationCode1
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language TasksCode1
EvalCrafter: Benchmarking and Evaluating Large Video Generation ModelsCode1
Evolving Deep Neural NetworksCode1
ExaRanker: Explanation-Augmented Neural RankerCode1
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationCode1
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
Bring Your Own Data! Self-Supervised Evaluation for Large Language ModelsCode1
Entity Tracking in Language ModelsCode1
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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