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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4901–4950 of 17610 papers

TitleStatusHype
SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical AlterationsCode0
STEVE Series: Step-by-Step Construction of Agent Systems in Minecraft—0
Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels—0
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models—0
Fairer Preferences Elicit Improved Human-Aligned Large Language Model JudgmentsCode1
A Simple and Effective L_2 Norm-Based Strategy for KV Cache CompressionCode1
Language Modeling with Editable External KnowledgeCode1
VideoLLM-online: Online Video Large Language Model for Streaming Video—0
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference ContentCode0
mDPO: Conditional Preference Optimization for Multimodal Large Language ModelsCode2
Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression—0
Watch Every Step! LLM Agent Learning via Iterative Step-Level Process RefinementCode2
ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPOCode2
SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users—0
Adversarial Style Augmentation via Large Language Model for Robust Fake News DetectionCode0
Optimizing Instructions and Demonstrations for Multi-Stage Language Model ProgramsCode14
HARE: HumAn pRiors, a key to small language model Efficiency—0
Promises, Outlooks and Challenges of Diffusion Language Modeling—0
CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster InformaticsCode0
WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive Psychologist—0
Avoiding Copyright Infringement via Large Language Model UnlearningCode0
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuningCode0
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context LearningCode0
Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens—0
Large Language Models for Dysfluency Detection in Stuttered Speech—0
Optimization of Armv9 architecture general large language model inference performance based on Llama.cppCode0
Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice QuestionsCode0
City-LEO: Toward Transparent City Management Using LLM with End-to-End Optimization—0
Reminding Multimodal Large Language Models of Object-aware Knowledge with Retrieved Tags—0
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank AdaptationCode0
VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It—0
Mental Disorder Classification via Temporal Representation of Text—0
Intertwining CP and NLP: The Generation of Unreasonably Constrained Sentences—0
Augmenting Biomedical Named Entity Recognition with General-domain ResourcesCode0
CancerLLM: A Large Language Model in Cancer Domain—0
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models—0
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-trainingCode1
Reactor Mk.1 performances: MMLU, HumanEval and BBH test results—0
RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics—0
Task Facet Learning: A Structured Approach to Prompt Optimization—0
Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language RecognitionCode1
Large Language Model Enhanced Clustering for News Event Detection—0
MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data—0
Spuriousness-Aware Meta-Learning for Learning Robust ClassifiersCode0
PARSE-Ego4D: Personal Action Recommendation Suggestions for Egocentric Videos—0
PRISM: A Design Framework for Open-Source Foundation Model Safety—0
BEACON: Benchmark for Comprehensive RNA Tasks and Language ModelsCode2
Rapport-Driven Virtual Agent: Rapport Building Dialogue Strategy for Improving User Experience at First MeetingCode0
Datasets for Multilingual Answer Sentence Selection—0
Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models—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