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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7301–7350 of 17610 papers

TitleStatusHype
From Retrieval to Generation: Comparing Different Approaches—0
From Retrieval to Generation: Efficient and Effective Entity Set Expansion—0
From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge—0
From r to Q^*: Your Language Model is Secretly a Q-Function—0
From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach—0
From Show to Tell: A Survey on Deep Learning-based Image Captioning—0
From Single Agent to Multi-Agent: Improving Traffic Signal Control—0
From Superficial Patterns to Semantic Understanding: Fine-Tuning Language Models on Contrast Sets—0
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation—0
From task structures to world models: What do LLMs know?—0
From Text to Source: Results in Detecting Large Language Model-Generated Content—0
From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors—0
From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection—0
From visual words to a visual grammar: using language modelling for image classification—0
From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers—0
Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of Language Model Agents—0
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?—0
Frozen Language Model Helps ECG Zero-Shot Learning—0
Frozen Large Language Models Can Perceive Paralinguistic Aspects of Speech—0
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling—0
Frustratingly Short Attention Spans in Neural Language Modeling—0
FSMR: A Feature Swapping Multi-modal Reasoning Approach with Joint Textual and Visual Clues—0
FuLG: 150B Romanian Corpus for Language Model Pretraining—0
FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers—0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model—0
Handwriting recognition and automatic scoring for descriptive answers in Japanese language tests—0
Fully Convolutional Recurrent Network for Handwritten Chinese Text Recognition—0
Fully Convolutional Speech Recognition—0
FunBench: Benchmarking Fundus Reading Skills of MLLMs—0
Functional Interpolation for Relative Positions Improves Long Context Transformers—0
Functionality understanding and segmentation in 3D scenes—0
FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions—0
Leveraging Language Model Capabilities for Sound Event Detection—0
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation—0
Efficiently Fusing Pretrained Acoustic and Linguistic Encoders for Low-resource Speech Recognition—0
Fusion Models for Improved Visual Captioning—0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—0
Future Token Prediction -- Causal Language Modelling with Per-Token Semantic State Vector for Multi-Token Prediction—0
Future Vector Enhanced LSTM Language Model for LVCSR—0
FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation—0
Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection—0
gaBERT — an Irish Language Model—0
gaBERT — an Irish Language Model—0
GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments—0
GAIA: A General AI Assistant for Intelligent Accelerator Operations—0
GAIA -- A Large Language Model for Advanced Power Dispatch—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