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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9801–9850 of 17610 papers

TitleStatusHype
VisorGPT: Learning Visual Prior via Generative Pre-TrainingCode1
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on WikipediaCode3
When the Music Stops: Tip-of-the-Tongue Retrieval for MusicCode0
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text GenerationCode2
Hierarchical Prompting Assists Large Language Model on Web NavigationCode3
Dr.ICL: Demonstration-Retrieved In-context Learning—0
Improving Factuality and Reasoning in Language Models through Multiagent DebateCode2
Automatic Model Selection with Large Language Models for ReasoningCode1
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information ExtractionCode1
Robust Prompt Optimization for Large Language Models Against Distribution Shifts—0
Textually Pretrained Speech Language ModelsCode1
Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes—0
A Study of Generative Large Language Model for Medical Research and HealthcareCode1
Can LLMs facilitate interpretation of pre-trained language models?—0
How Language Model Hallucinations Can SnowballCode1
Evaluating Pragmatic Abilities of Image Captioners on A3DS—0
Bidirectional Transformer Reranker for Grammatical Error CorrectionCode0
A Frustratingly Simple Decoding Method for Neural Text GenerationCode1
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head CheckpointsCode1
ConQueR: Contextualized Query Reduction using Search LogsCode0
Distilling ChatGPT for Explainable Automated Student Answer AssessmentCode0
Extrapolating Multilingual Understanding Models as Multilingual Generators—0
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous SourcesCode1
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation—0
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language ModelCode0
RecurrentGPT: Interactive Generation of (Arbitrarily) Long TextCode3
PrOnto: Language Model Evaluations for 859 LanguagesCode0
Lion: Adversarial Distillation of Proprietary Large Language ModelsCode2
LMGQS: A Large-scale Dataset for Query-focused Summarization—0
Towards Unsupervised Recognition of Token-level Semantic Differences in Related DocumentsCode0
The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis—0
Word Embeddings Are Steers for Language ModelsCode1
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language ExplanationsCode0
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple PredictionCode1
Making Language Models Better Tool Learners with Execution FeedbackCode1
Text-based Person Search without Parallel Image-Text Data—0
Observations on LLMs for Telecom Domain: Capabilities and Limitations—0
Training Diffusion Models with Reinforcement LearningCode2
Federated Learning of Medical Concepts Embedding using BEHRTCode0
Explaining Emergent In-Context Learning as Kernel Regression—0
GPT-SW3: An Autoregressive Language Model for the Nordic Languages—0
Enhance Reasoning Ability of Visual-Language Models via Large Language Models—0
Direct Fact Retrieval from Knowledge Graphs without Entity Linking—0
A Pilot Study on Dialogue-Level Dependency Parsing for Chinese—0
Infor-Coef: Information Bottleneck-based Dynamic Token Downsampling for Compact and Efficient language model—0
Description-Based Text Similarity—0
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing—0
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMsCode1
Multi-Head State Space Model for Speech Recognition—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