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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9051–9100 of 17610 papers

TitleStatusHype
Ternary Singular Value Decomposition as a Better Parameterized Form in Linear MappingCode0
Through the Lens of Core Competency: Survey on Evaluation of Large Language Models—0
Text Injection for Capitalization and Turn-Taking Prediction in Speech Models—0
Generating Individual Trajectories Using GPT-2 Trained from Scratch on Encoded Spatiotemporal Data—0
CausalLM is not optimal for in-context learningCode1
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and TextCode1
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerceCode2
Pairing interacting protein sequences using masked language modelingCode1
Position: Key Claims in LLM Research Have a Long Tail of Footnotes—0
SpeechX: Neural Codec Language Model as a Versatile Speech Transformer—0
Neural Authorship Attribution: Stylometric Analysis on Large Language ModelsCode0
LLM Self Defense: By Self Examination, LLMs Know They Are Being TrickedCode1
Language is All a Graph NeedsCode2
ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots—0
AudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codesCode0
Bayesian Flow NetworksCode2
Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine—0
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models—0
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information ExtractionCode0
ZYN: Zero-Shot Reward Models with Yes-No Questions for RLAIFCode1
Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models—0
A Large Language Model Enhanced Conversational Recommender System—0
Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-ModelingCode1
LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach PromptsCode0
Learning to Guide Human Experts via Personalized Large Language Models—0
Self-Alignment with Instruction BacktranslationCode1
RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language ModelCode1
Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems—0
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search EngineCode1
Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval—0
"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets—0
Emotion-Conditioned Text Generation through Automatic Prompt Optimization—0
Exploring Multilingual Text Data DistillationCode0
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive LearningCode0
TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design—0
MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model—0
Fine-Tune Language Models as Multi-Modal Differential Equation SolversCode1
Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization—0
PTransIPs: Identification of phosphorylation sites enhanced by protein PLM embeddingsCode0
Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction—0
Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction—0
Continual Pre-Training of Large Language Models: How to (re)warm your model?Code6
AgentSims: An Open-Source Sandbox for Large Language Model EvaluationCode2
In-Context Alignment: Chat with Vanilla Language Models Before Fine-TuningCode1
On Monotonic Aggregation for Open-domain QACode0
Shepherd: A Critic for Language Model GenerationCode2
SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI ToolCode2
SILO Language Models: Isolating Legal Risk In a Nonparametric DatastoreCode1
Large Language Model Prompt Chaining for Long Legal Document Classification—0
I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection—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