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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 18011850 of 17610 papers

TitleStatusHype
Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search0
Vision-centric Token Compression in Large Language Model0
Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization0
Agent-Based Uncertainty Awareness Improves Automated Radiology Report Labeling with an Open-Source Large Language Model0
LLM Safety Alignment is Divergence Estimation in DisguiseCode0
Avoiding exp(R_max) scaling in RLHF through Preference-based ExplorationCode0
LIBRA: Measuring Bias of Large Language Model from a Local ContextCode0
A statistically consistent measure of Semantic Variability using Language Models0
Speculative Ensemble: Fast Large Language Model Ensemble via SpeculationCode1
INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation0
Doing More with Less -- Implementing Routing Strategies in Large Language Model-Based Systems: An Extended Survey0
OrcaLoca: An LLM Agent Framework for Software Issue Localization0
MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for Automating CFD Simulation and Post-ProcessingCode2
Enhancing Token Filtering Efficiency in Large Language Model Training with Collider0
Resolving Editing-Unlearning Conflicts: A Knowledge Codebook Framework for Large Language Model Updating0
Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach0
Can AI Solve the Peer Review Crisis? A Large Scale Cross Model Experiment of LLMs' Performance and Biases in Evaluating over 1000 Economics Papers0
Estimating the Probability of Sampling a Trained Neural Network at Random0
Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected0
Low-Rank Adapting Models for Sparse AutoencodersCode1
Towards the Worst-case Robustness of Large Language Models0
LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language ModelsCode4
s1: Simple test-time scalingCode9
Scaling Laws for Differentially Private Language Models0
Intrinsic Tensor Field Propagation in Large Language Models: A Novel Approach to Contextual Information Flow0
Improving LLM Unlearning Robustness via Random PerturbationsCode0
Structural Embedding Projection for Contextual Large Language Model Inference0
Importing Phantoms: Measuring LLM Package Hallucination Vulnerabilities0
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning0
SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions0
Offline Learning for Combinatorial Multi-armed Bandits0
Partially Rewriting a Transformer in Natural LanguageCode3
Scalable-Softmax Is Superior for AttentionCode1
An Efficient Approach for Machine Translation on Low-resource Languages: A Case Study in Vietnamese-Chinese0
Fine-tuning LLaMA 2 interference: a comparative study of language implementations for optimal efficiency0
Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking0
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering0
Investigating Tax Evasion Emergence Using Dual Large Language Model and Deep Reinforcement Learning Powered Agent-based Simulation0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability0
Exploring Audio Editing Features as User-Centric Privacy Defenses Against Large Language Model(LLM) Based Emotion Inference Attacks0
Loss Functions and Operators Generated by f-Divergences0
Vision-Language Model Selection and Reuse for Downstream Adaptation0
Differentially Private Steering for Large Language Model AlignmentCode0
Economic Rationality under Specialization: Evidence of Decision Bias in AI Agents0
CLEAR: Cue Learning using Evolution for Accurate Recognition Applied to Sustainability Data Extraction0
Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH0
WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-TrainingCode1
Can Generative LLMs Create Query Variants for Test Collections? An Exploratory StudyCode0
Prompt-oriented Output of Culture-Specific Items in Translated African Poetry by Large Language Model: An Initial Multi-layered Tabular Review0
DINT Transformer0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified