SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6301–6350 of 17610 papers

TitleStatusHype
Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning—0
Integrating Object Detection Modality into Visual Language Model for Enhanced Autonomous Driving Agent—0
An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking—0
A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion DiagnosisCode0
AgentOps: Enabling Observability of LLM Agents—0
Assessing the Answerability of Queries in Retrieval-Augmented Code Generation—0
LBPE: Long-token-first Tokenization to Improve Large Language Models—0
SSSD: Simply-Scalable Speculative Decoding—0
The Empirical Impact of Data Sanitization on Language Models—0
Recycled Attention: Efficient inference for long-context language modelsCode0
Towards Multi-Modal Mastery: A 4.5B Parameter Truly Multi-Modal Small Language Model—0
Real-World Offline Reinforcement Learning from Vision Language Model Feedback—0
Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM—0
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
When Does Classical Chinese Help? Quantifying Cross-Lingual Transfer in Hanja and KanbunCode0
VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models—0
Watermarking Language Models through Language Models—0
CUIfy the XR: An Open-Source Package to Embed LLM-powered Conversational Agents in XR—0
BendVLM: Test-Time Debiasing of Vision-Language EmbeddingsCode0
Benchmarking Large Language Models with Integer Sequence Generation Tasks—0
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model—0
Scaling Laws for Pre-training Agents and World Models—0
Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language ModelCode0
The N-Grammys: Accelerating Autoregressive Inference with Learning-Free Batched Speculation—0
Large Generative Model-assisted Talking-face Semantic Communication System—0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA—0
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-AwarenessCode0
Deploying Multi-task Online Server with Large Language Model—0
Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction—0
HumanVLM: Foundation for Human-Scene Vision-Language Model—0
ChatGPT in Research and Education: Exploring Benefits and Threats—0
AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution—0
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status—0
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI—0
Unified Pathological Speech Analysis with Prompt Tuning—0
The Evolution of RWKV: Advancements in Efficient Language Modeling—0
PersianRAG: A Retrieval-Augmented Generation System for Persian Language—0
Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities—0
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning—0
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension—0
GraphVL: Graph-Enhanced Semantic Modeling via Vision-Language Models for Generalized Class Discovery—0
Context Parallelism for Scalable Million-Token Inference—0
ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model—0
Exploring the Landscape for Generative Sequence Models for Specialized Data SynthesisCode0
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis—0
Wave Network: An Ultra-Small Language Model—0
Large Language Model Supply Chain: Open Problems From the Security Perspective—0
A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?—0
High-performance automated abstract screening with large language model ensembles—0
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers—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