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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8901–8950 of 17610 papers

TitleStatusHype
Softmax Bias Correction for Quantized Generative Models—0
DeViL: Decoding Vision features into LanguageCode1
DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion—0
AutoML-GPT: Large Language Model for AutoML—0
Business Process Text Sketch Automation Generation Using Large Language Model—0
A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture—0
Generative Social ChoiceCode0
Large AI Model Empowered Multimodal Semantic Communications—0
Studying the impacts of pre-training using ChatGPT-generated text on downstream tasks—0
LinkTransformer: A Unified Package for Record Linkage with Transformer Language ModelsCode1
Efficient RLHF: Reducing the Memory Usage of PPO—0
Image Hijacks: Adversarial Images can Control Generative Models at RuntimeCode1
BatchPrompt: Accomplish more with lessCode0
FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking—0
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt DependenceCode1
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical NotesCode1
Long-Term Ad Memorability: Understanding & Generating Memorable Ads—0
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction FollowingCode2
GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice—0
RepCodec: A Speech Representation Codec for Speech TokenizationCode1
SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills—0
SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language ModelsCode2
Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language ModelCode1
Enhancing Subtask Performance of Multi-modal Large Language Model—0
Developing Social Robots with Empathetic Non-Verbal Cues Using Large Language Models—0
ViLTA: Enhancing Vision-Language Pre-training through Textual Augmentation—0
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model—0
Quantifying and Analyzing Entity-level Memorization in Large Language Models—0
ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding—0
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties PredictionCode1
LLaSM: Large Language and Speech ModelCode2
Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness—0
DTrOCR: Decoder-only Transformer for Optical Character RecognitionCode2
Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model—0
Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection—0
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting—0
Radiology-Llama2: Best-in-Class Large Language Model for Radiology—0
Is it an i or an l: Test-time Adaptation of Text Line Recognition Models—0
LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks—0
Large Language Models on the Chessboard: A Study on ChatGPT's Formal Language Comprehension and Complex Reasoning Skills—0
LAMBO: Large AI Model Empowered Edge Intelligence—0
AutoDroid: LLM-powered Task Automation in AndroidCode2
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-ProfessionalsCode0
Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, and StabilityCode0
FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions—0
The Responsible Development of Automated Student Feedback with Generative AI—0
PeptideBERT: A Language Model based on Transformers for Peptide Property PredictionCode1
TextrolSpeech: A Text Style Control Speech Corpus With Codec Language Text-to-Speech ModelsCode1
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERCode1
Improving antibody language models with native pairingCode1
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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