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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 81518200 of 17610 papers

TitleStatusHype
The Impact of Depth on Compositional Generalization in Transformer Language Models0
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design0
Emotional Theory of Mind: Bridging Fast Visual Processing with Slow Linguistic Reasoning0
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionCode1
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing0
Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models0
BTRec: BERT-Based Trajectory Recommendation for Personalized ToursCode0
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language ModelsCode1
Integrating Pre-trained Language Model into Neural Machine Translation0
Interpretable-by-Design Text Understanding with Iteratively Generated Concept BottleneckCode0
Generating Medical Prescriptions with Conditional TransformerCode0
Improving Input-label Mapping with Demonstration Replay for In-context Learning0
'Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion0
Skywork: A More Open Bilingual Foundation ModelCode3
Musical Form Generation0
Large Trajectory Models are Scalable Motion Predictors and PlannersCode2
ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common SenseCode0
LitCab: Lightweight Language Model Calibration over Short- and Long-form ResponsesCode1
EHRTutor: Enhancing Patient Understanding of Discharge Instructions0
Adapter Pruning using Tropical Characterization0
Counterfactually Probing Language Identity in Multilingual ModelsCode0
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content0
Unified Representation for Non-compositional and Compositional ExpressionsCode0
Robustifying Language Models with Test-Time Adaptation0
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise0
MILL: Mutual Verification with Large Language Models for Zero-Shot Query ExpansionCode0
TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language UnderstandingCode1
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4VCode1
PACuna: Automated Fine-Tuning of Language Models for Particle AcceleratorsCode0
Robust NL-to-Cypher Translation for KBQA: Harnessing Large Language Model with Chain of Prompts0
ProMap: Effective Bilingual Lexicon Induction via Language Model PromptingCode0
Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain0
Identifying Conspiracy Theories News based on Event Relation GraphCode0
Automating the Correctness Assessment of AI-generated Code for Security ContextsCode0
Translating away Translationese without Parallel Data0
Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies0
Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 20230
Elevating Code-mixed Text Handling through Auditory Information of WordsCode0
INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue SystemCode0
Benchingmaking Large Langage Models in Biomedical Triple ExtractionCode1
SOUL: Towards Sentiment and Opinion Understanding of LanguageCode0
Siamese-DETR for Generic Multi-Object TrackingCode0
LLMSTEP: LLM proofstep suggestions in LeanCode1
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each BenchmarkCode1
Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General HealthcareCode1
Real-time Animation Generation and Control on Rigged Models via Large Language ModelsCode1
The impact of responding to patient messages with large language model assistanceCode0
PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream ApplicationsCode1
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike WaysCode0
Proving Test Set Contamination in Black Box 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