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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6401–6450 of 17610 papers

TitleStatusHype
Teaching a Language Model to Distinguish Between Similar Details using a Small Adversarial Training Set—0
Robotic State Recognition with Image-to-Text Retrieval Task of Pre-Trained Vision-Language Model and Black-Box Optimization—0
Learning and Transferring Sparse Contextual Bigrams with Linear Transformers—0
Multimodal Quantum Natural Language Processing: A Novel Framework for using Quantum Methods to Analyse Real DataCode0
MARCO: Multi-Agent Real-time Chat Orchestration—0
Rethinking Code Refinement: Learning to Judge Code EfficiencyCode0
Online Detecting LLM-Generated Texts via Sequential Hypothesis Testing by BettingCode0
Learning and Unlearning of Fabricated Knowledge in Language Models—0
MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding—0
PerSRV: Personalized Sticker Retrieval with Vision-Language ModelCode0
Reliable Semantic Understanding for Real World Zero-shot Object Goal Navigation—0
VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration—0
From melodic note sequences to pitches using word2vec—0
Are VLMs Really BlindCode0
Discrete Modeling via Boundary Conditional Diffusion Processes—0
CurateGPT: A flexible language-model assisted biocuration tool—0
Improving In-Context Learning with Small Language Model EnsemblesCode0
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
Democratizing Reward Design for Personal and Representative Value-Alignment—0
Anticipating Future with Large Language Model for Simultaneous Machine Translation—0
A Hierarchical Language Model For Interpretable Graph Reasoning—0
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents—0
FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation—0
Graph-based Uncertainty Metrics for Long-form Language Model OutputsCode0
Energy-Based Diffusion Language Models for Text Generation—0
Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce—0
BongLLaMA: LLaMA for Bangla Language—0
Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games—0
ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents—0
An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model—0
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring—0
Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training—0
Large Language Model Benchmarks in Medical Tasks—0
Segmenting Watermarked Texts From Language ModelsCode0
Large Language Model-Guided Prediction Toward Quantum Materials SynthesisCode0
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word SenseCode0
Large Language Model-assisted Speech and Pointing Benefits Multiple 3D Object Selection in Virtual Reality—0
Visualizing attention zones in machine reading comprehension models—0
Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback—0
Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models—0
Sequential Large Language Model-Based Hyper-parameter OptimizationCode0
MedGo: A Chinese Medical Large Language Model—0
Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation—0
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation—0
Chemical Language Model Linker: blending text and molecules with modular adaptersCode0
A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERTCode0
Autonomous Building Cyber-Physical Systems Using Decentralized Autonomous Organizations, Digital Twins, and Large Language Model—0
IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation—0
EDGE: Enhanced Grounded GUI Understanding with Enriched Multi-Granularity Synthetic Data—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