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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 79518000 of 17610 papers

TitleStatusHype
Leveraging LLMs in Scholarly Knowledge Graph Question AnsweringCode0
A Speed Odyssey for Deployable Quantization of LLMs0
Characterizing Tradeoffs in Language Model Decoding with Informational Interpretations0
Video-LLaVA: Learning United Visual Representation by Alignment Before ProjectionCode4
Effective Large Language Model Adaptation for Improved Grounding and Citation Generation0
Language Generation from Brain RecordingsCode1
DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback0
Comparing Generalization in Learning with Limited Numbers of Exemplars: Transformer vs. RNN in Attractor Dynamics0
User Persona Identification and New Service Adaptation Recommendation0
Exponentially Faster Language ModellingCode2
Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"?Code0
Contrastive Chain-of-Thought PromptingCode1
VideoCon: Robust Video-Language Alignment via Contrast CaptionsCode1
Data Similarity is Not Enough to Explain Language Model PerformanceCode0
Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing0
Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers0
Accelerating Toeplitz Neural Network with Constant-time Inference ComplexityCode1
HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation0
Toucan: Token-Aware Character Level Language Modeling0
Grounding Gaps in Language Model Generations0
GENEVA: GENErating and Visualizing branching narratives using LLMs0
German FinBERT: A German Pre-trained Language Model0
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy0
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?0
Aligning Neural Machine Translation Models: Human Feedback in Training and Inference0
OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued PretrainingCode1
When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource LanguagesCode0
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence GenerationCode0
Assessing Translation capabilities of Large Language Models involving English and Indian Languages0
CLIMB: Curriculum Learning for Infant-inspired Model Building0
Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder0
SiRA: Sparse Mixture of Low Rank Adaptation0
Improving Deep Learning Optimization through Constrained Parameter RegularizationCode0
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?0
An Eye on Clinical BERT: Investigating Language Model Generalization for Diabetic Eye Disease PhenotypingCode0
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects0
PsyEval: A Suite of Mental Health Related Tasks for Evaluating Large Language ModelsCode0
Large Language Model-Driven Classroom Flipping: Empowering Student-Centric Peer Questioning with Flipped Interaction0
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and CollaborationCode1
Automated title and abstract screening for scoping reviews using the GPT-4 Large Language ModelCode0
Text Retrieval with Multi-Stage Re-Ranking ModelsCode0
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncodersCode0
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning0
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video UnderstandingCode2
Memory-efficient Stochastic methods for Memory-based TransformersCode0
Summarization-Based Document IDs for Generative Retrieval with Language ModelsCode0
How good are Large Language Models on African Languages?0
LLatrieval: LLM-Verified Retrieval for Verifiable GenerationCode1
Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for CodeCode4
A Survey of Confidence Estimation and Calibration in Large Language Models0
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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