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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 38513900 of 17610 papers

TitleStatusHype
AutoScrum: Automating Project Planning Using Large Language ModelsCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory ArchitectureCode1
Mixture of Attention Heads: Selecting Attention Heads Per TokenCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
LLaVA-SpaceSGG: Visual Instruct Tuning for Open-vocabulary Scene Graph Generation with Enhanced Spatial RelationsCode1
BEND: Benchmarking DNA Language Models on biologically meaningful tasksCode1
Meerkat: Audio-Visual Large Language Model for Grounding in Space and TimeCode1
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity DatasetCode1
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLPCode1
C-STS: Conditional Semantic Textual SimilarityCode1
Deep contextualized word representationsCode1
Measuring and Controlling Instruction (In)Stability in Language Model DialogsCode1
Decoupled Visual Interpretation and Linguistic Reasoning for Math Problem SolvingCode1
Measuring Implicit Bias in Explicitly Unbiased Large Language ModelsCode1
Decoding Speculative DecodingCode1
CTRAN: CNN-Transformer-based Network for Natural Language UnderstandingCode1
Decouple knowledge from parameters for plug-and-play language modelingCode1
Decoding-Time Language Model Alignment with Multiple ObjectivesCode1
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
Towards Evaluating Generalist Agents: An Automated Benchmark in Open WorldCode1
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledgeCode1
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text GenerationCode1
Declaration-based Prompt Tuning for Visual Question AnsweringCode1
Benchmarking Language Model Creativity: A Case Study on Code GenerationCode1
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model EvaluationCode1
LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital TwinsCode1
ADEPT: A DEbiasing PrompT FrameworkCode1
Advancing High Resolution Vision-Language Models in BiomedicineCode1
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
DebUnc: Improving Large Language Model Agent Communication With Uncertainty MetricsCode1
Debiasing Methods in Natural Language Understanding Make Bias More AccessibleCode1
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language TechnologiesCode1
LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT PluginsCode1
Deciphering antibody affinity maturation with language models and weakly supervised learningCode1
MatSciBERT: A Materials Domain Language Model for Text Mining and Information ExtractionCode1
AVocaDo: Strategy for Adapting Vocabulary to Downstream DomainCode1
LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary CaptioningCode1
LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based RepresentationsCode1
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question ComplexityCode1
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test ConstructionCode1
Data-to-Text Generation with Iterative Text EditingCode1
Avoiding Inference Heuristics in Few-shot Prompt-based FinetuningCode1
MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning ProblemsCode1
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric ApproachCode1
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics EducationCode1
Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward PassesCode1
Show:102550
← PrevPage 78 of 353Next →

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