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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12401–12450 of 17610 papers

TitleStatusHype
Video Imprint—0
Using LLMs to Model the Beliefs and Preferences of Targeted Populations—0
You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with a Multi-Agent Conversations—0
Video Language Model Pretraining with Spatio-temporal Masking—0
Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers—0
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media—0
VideoLLM-online: Online Video Large Language Model for Streaming Video—0
Video LLMs for Temporal Reasoning in Long Videos—0
Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device—0
VideoOrion: Tokenizing Object Dynamics in Videos—0
VideoPoet: A Large Language Model for Zero-Shot Video Generation—0
Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level—0
Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies—0
You Only Forward Once: Prediction and Rationalization in A Single Forward Pass—0
Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture—0
Using Large Language Models to Provide Explanatory Feedback to Human Tutors—0
Using large language models to produce literature reviews: Usages and systematic biases of microphysics parametrizations in 2699 publications—0
VidLA: Video-Language Alignment at Scale—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain—0
Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets—0
Vietnamese Text Accent Restoration with Statistical Machine Translation—0
Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine—0
Using Large Language Models for (De-)Formalization and Natural Argumentation Exercises for Beginner's Students—0
Using Large Language Model for End-to-End Chinese ASR and NER—0
Using Language Models to Detect Alarming Student Responses—0
ViLAaD: Enhancing "Attracting and Dispersing'' Source-Free Domain Adaptation with Vision-and-Language Model—0
You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet—0
UNITER: Learning UNiversal Image-TExt Representations—0
Using Language Models to Decipher the Motivation Behind Human Behaviors—0
Efficient Language Modeling with Sparse all-MLP—0
Efficient End-to-end Language Model Fine-tuning on Graphs—0
Efficient Large Scale Language Modeling with Mixtures of Experts—0
Efficient Left-to-Right Hierarchical Phrase-Based Translation with Improved Reordering—0
Efficient, Lexicon-Free OCR using Deep Learning—0
Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking—0
Efficient Indirect LLM Jailbreak via Multimodal-LLM Jailbreak—0
Efficient Long Context Language Model Retrieval with Compression—0
Efficient long-distance relation extraction with DG-SpanBERT—0
Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling—0
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer—0
Efficiently and Thoroughly Anonymizing a Transformer Language Model for Dutch Electronic Health Records: a Two-Step Method—0
Efficiently Building a Domain-Specific Large Language Model from Scratch: A Case Study of a Classical Chinese Large Language Model—0
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning—0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Efficient Machine Translation Domain Adaptation—0
Efficient Masked Autoencoders with Self-Consistency—0
Efficient MDI Adaptation for n-gram Language Models—0
Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search—0
Efficient Neural Query Auto Completion—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