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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13011350 of 17610 papers

TitleStatusHype
DsDm: Model-Aware Dataset Selection with DatamodelsCode2
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPTCode2
Large Language Model Safety: A Holistic SurveyCode2
Motion-Agent: A Conversational Framework for Human Motion Generation with LLMsCode2
Ring Attention with Blockwise Transformers for Near-Infinite ContextCode2
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More EffectiveCode1
cosFormer: Rethinking Softmax in AttentionCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial InjectionCode1
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model InfillingCode1
Advanced Language Model-based Translator for English-Vietnamese TranslationCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading TimesCode1
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction TuningCode1
f-PO: Generalizing Preference Optimization with f-divergence MinimizationCode1
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language ModelsCode1
t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule GenerationCode1
Copy Is All You NeedCode1
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model GenerationCode1
Copy Suppression: Comprehensively Understanding an Attention HeadCode1
Free and Customizable Code Documentation with LLMs: A Fine-Tuning ApproachCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Foundation TransformersCode1
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language ModelsCode1
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task GenerationCode1
Counterfactual Data Augmentation for Neural Machine TranslationCode1
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional EncodingCode1
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language ModelsCode1
Fool Your (Vision and) Language Model With Embarrassingly Simple PermutationsCode1
FontCLIP: A Semantic Typography Visual-Language Model for Multilingual Font ApplicationsCode1
Forcing Diffuse Distributions out of Language ModelsCode1
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales DialogueCode1
AceGPT, Localizing Large Language Models in ArabicCode1
FonBund: A Library for Combining Cross-lingual Phonological Segment DataCode1
Forecasting Future World Events with Neural NetworksCode1
FocusLLM: Precise Understanding of Long Context by Dynamic CondensingCode1
FOLIO: Natural Language Reasoning with First-Order LogicCode1
Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-ModelingCode1
Control Prefixes for Parameter-Efficient Text GenerationCode1
FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual ModelsCode1
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image ClassificationCode1
Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree SearchCode1
Controlled Text Generation for Large Language Model with Dynamic Attribute GraphsCode1
LawInstruct: A Resource for Studying Language Model Adaptation to the Legal DomainCode1
FLEX: Unifying Evaluation for Few-Shot NLPCode1
Controllable Text Generation with Neurally-Decomposed OracleCode1
Unifying Segment Anything in Microscopy with Multimodal Large Language ModelCode1
Controlled Text Generation as Continuous Optimization with Multiple ConstraintsCode1
Controllable Sentence Simplification with a Unified Text-to-Text Transfer TransformerCode1
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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