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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12151–12200 of 17610 papers

TitleStatusHype
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection—0
Who's Harry Potter? Approximate Unlearning in LLMs—0
Zero-Shot Learning of Language Models for Describing Human Actions Based on Semantic Compositionality of Actions—0
Who's to say what's funny? A computer using Language Models and Deep Learning, That's Who!—0
Who Writes the Review, Human or AI?—0
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore—0
Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seeking—0
Why Are Positional Encodings Nonessential for Deep Autoregressive Transformers? Revisiting a Petroglyph—0
Unsupervised Bias Detection in College Student Newspapers—0
Why do LLaVA Vision-Language Models Reply to Images in English?—0
Why do small language models underperform? Studying Language Model Saturation via the Softmax Bottleneck—0
Why Gradients Rapidly Increase Near the End of Training—0
Unsupervised ASR via Cross-Lingual Pseudo-Labeling—0
Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation—0
Why LLMs Cannot Think and How to Fix It—0
Why LLMs Hallucinate, and How to Get (Evidential) Closure: Perceptual, Intensional, and Extensional Learning for Faithful Natural Language Generation—0
Why Neural Translations are the Right Length—0
Why Not Grab a Free Lunch? Mining Large Corpora for Parallel Sentences to Improve Translation Modeling—0
Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models—0
Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet—0
Unsupervised Aspect-Level Sentiment Controllable Style Transfer—0
Why Would You Suggest That? Human Trust in Language Model Responses—0
Zero-Shot Listwise Document Reranking with a Large Language Model—0
Wiki-40B: Multilingual Language Model Dataset—0
Unsupervised Aspect-Based Multi-Document Abstractive Summarization—0
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia—0
Unsupervised and Few-shot Parsing from Pretrained Language Models—0
Unsupervised Adverbial Identification in Modern Chinese Literature—0
Unsupervised Adaptation for Statistical Machine Translation—0
Unsupervised Acquisition of Discrete Grammatical Categories—0
Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT—0
Will GPT-4 Run DOOM?—0
A Survey on Large Language Model-empowered Autonomous Driving—0
Unsupervised Accent Adaptation Through Masked Language Model Correction Of Discrete Self-Supervised Speech Units—0
Unseen Attack Detection in Software-Defined Networking Using a BERT-Based Large Language Model—0
Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance—0
Zero-shot Load Forecasting for Integrated Energy Systems: A Large Language Model-based Framework with Multi-task Learning—0
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model—0
Winning Solution For Meta KDD Cup' 24—0
WinoViz: Probing Visual Properties of Objects Under Different States—0
Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens—0
Unpacking Large Language Models with Conceptual Consistency—0
Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model—0
Unnatural language processing: How do language models handle machine-generated prompts?—0
WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking—0
Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM—0
Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems—0
Witscript 2: A System for Generating Improvised Jokes Without Wordplay—0
Witscript: A System for Generating Improvised Jokes in a Conversation—0
WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training—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