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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9651–9700 of 17610 papers

TitleStatusHype
SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERTCode0
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection—0
When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment—0
ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering—0
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation—0
Distilling Event Sequence Knowledge From Large Language Models—0
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization—0
Small Language Model Can Self-correct—0
Parameter-Efficient Detoxification with Contrastive Decoding—0
Tracing the Genealogies of Ideas with Large Language Model Embeddings—0
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning—0
Evolving Code with A Large Language Model—0
InRanker: Distilled Rankers for Zero-shot Information RetrievalCode0
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language ModelCode0
Dynamic Behaviour of Connectionist Speech Recognition with Strong Latency Constraints—0
A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systems—0
PersianMind: A Cross-Lingual Persian-English Large Language Model—0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese—0
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein—0
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge—0
Distilling Vision-Language Models on Millions of Videos—0
How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes—0
Combating Adversarial Attacks with Multi-Agent DebateCode0
Investigating Data Contamination for Pre-training Language Models—0
LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization—0
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems—0
LEGOBench: Scientific Leaderboard Generation BenchmarkCode0
Towards Conversational Diagnostic AI—0
Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?—0
Knowledge Sharing in Manufacturing using Large Language Models: User Evaluation and Model Benchmarking—0
Less is More: A Closer Look at Semantic-based Few-Shot Learning—0
Generating Diverse and High-Quality Texts by Minimum Bayes Risk DecodingCode0
Enhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge FeaturesCode0
An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue AssistantCode0
Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings—0
ChatGPT, Let us Chat Sign Language: Experiments, Architectural Elements, Challenges and Research Directions—0
AugSumm: towards generalizable speech summarization using synthetic labels from large language modelCode0
Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models—0
How predictable is language model benchmark performance?—0
TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property PredictionCode0
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMsCode0
Language-Conditioned Robotic Manipulation with Fast and Slow Thinking—0
Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document Retrieval—0
The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model PerformanceCode0
Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet—0
FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference—0
An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning FrameworksCode0
A Content-Based Novelty Measure for Scholarly Publications: A Proof of ConceptCode0
Anatomy of Neural Language ModelsCode0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs—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