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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9801–9850 of 17610 papers

TitleStatusHype
When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1—0
Unveiling Privacy Risks in LLM Agent Memory—0
When and why are log-linear models self-normalizing?—0
XFBoost: Improving Text Generation with Controllable Decoders—0
UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information—0
UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis—0
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance—0
UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities—0
Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems—0
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models—0
Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM—0
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation—0
UPM system for WMT 2012—0
UQAM-NTL: Named entity recognition in Twitter messages—0
Urban Air Mobility as a System of Systems: An LLM-Enhanced Holonic Approach—0
Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study—0
UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models—0
Zero-shot cross-lingual Meaning Representation Transfer: Annotation of Hungarian using the Prague Functional Generative Description—0
Urdu News Article Recommendation Model using Natural Language Processing Techniques—0
Word Order Matters when you Increase Masking—0
URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors—0
URL-BERT: Training Webpage Representations via Social Media Engagements—0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing—0
Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition—0
Use of probabilistic phrases in a coordination game: human versus GPT-4—0
User-Aware Prefix-Tuning is a Good Learner for Personalized Image Captioning—0
User Edits Classification Using Document Revision Histories—0
User Intent to Use DeepSeek for Healthcare Purposes and their Trust in the Large Language Model: Multinational Survey Study—0
UserLibri: A Dataset for ASR Personalization Using Only Text—0
User Persona Identification and New Service Adaptation Recommendation—0
User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study—0
User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning—0
Uses of Monolingual In-Domain Corpora for Cross-Domain Adaptation with Hybrid MT Approaches—0
Using a Cross-Language Information Retrieval System based on OHSUMED to Evaluate the Moses and KantanMT Statistical Machine Translation Systems—0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring—0
Using a Language Model in a Kiosk Recommender System at Fast-Food Restaurants—0
Controllable Speaking Styles Using a Large Language Model—0
Using a Large Language Model to generate a Design Structure Matrix—0
Using an LLM to Turn Sign Spottings into Spoken Language Sentences—0
Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses—0
Using ASR-Generated Text for Spoken Language Modeling—0
Using a Supertagged Dependency Language Model to Select a Good Translation in System Combination—0
Using a thousand optimization tasks to learn hyperparameter search strategies—0
Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering—0
Using cognitive psychology to understand GPT-3—0
Using Collocations and K-means Clustering to Improve the N-pos Model for Japanese IME—0
Using Context Vectors in Improving a Machine Translation System with Bridge Language—0
Using Cross-Lingual Part of Speech Tagging for Partially Reconstructing the Classic Language Family Tree Model—0
Unifying Multimodal Retrieval via Document Screenshot Embedding—0
Using Deep Object Features for Image Descriptions—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