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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14301–14350 of 17610 papers

TitleStatusHype
Intentional Biases in LLM Responses—0
InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent—0
Interacting Large Language Model Agents. Interpretable Models and Social Learning—0
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety—0
Interactive Design by Integrating a Large Pre-Trained Language Model and Building Information Modeling—0
Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission—0
Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model—0
Interactive Machine Teaching by Labeling Rules and Instances—0
Interactive Model with Structural Loss for Language-based Abductive Reasoning—0
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision—0
Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models—0
Interactive Robot Learning from Verbal Correction—0
Interactive Task Planning with Language Models—0
Inter-document Contextual Language model—0
InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction—0
Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints—0
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition—0
Interleaved Speech-Text Language Models are Simple Streaming Text to Speech Synthesizers—0
Interlocking Phrases in Phrase-based Statistical Machine Translation—0
Intermediate Loss Regularization for CTC-based Speech Recognition—0
Intermediate Self-supervised Learning for Machine Translation Quality Estimation—0
Internal and External Impacts of Natural Language Processing Papers—0
Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition—0
Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition—0
Internal Language Model Estimation based Adaptive Language Model Fusion for Domain Adaptation—0
Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition—0
Internal Language Model Estimation Through Explicit Context Vector Learning for Attention-based Encoder-decoder ASR—0
Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition—0
Internet-augmented language models through few-shot prompting for open-domain question answering—0
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output—0
InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model—0
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models—0
Interpolated Dirichlet Class Language Model for Speech Recognition Incorporating Long-distance N-grams—0
Interpolated Spectral NGram Language Models—0
Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks—0
Refine Large Language Model Fine-tuning via Instruction Vector—0
Interpretable Emoji Prediction via Label-Wise Attention LSTMs—0
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models—0
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning—0
Interpretable Network Structure for Modeling Contextual Dependency—0
Interpretable Sentence Representation with Variational Autoencoders and Attention—0
Interpretation Gaps in LLM-Assisted Comprehension of Privacy Documents—0
Interpreting A Pre-trained Model Is A Key For Model Architecture Optimization: A Case Study On Wav2Vec 2.0—0
Interpreting the linear structure of vision-language model embedding spaces—0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
Intersectional Bias in Causal Language Models—0
Intertwining CP and NLP: The Generation of Unreasonably Constrained Sentences—0
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics—0
Into-TTS : Intonation Template Based Prosody Control System—0
INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding—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