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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 28512900 of 17610 papers

TitleStatusHype
ALISE: Accelerating Large Language Model Serving with Speculative Scheduling0
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge GraphsCode2
Towards Reliable Alignment: Uncertainty-aware RLHF0
The NPU-HWC System for the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge0
From Context to Action: Analysis of the Impact of State Representation and Context on the Generalization of Multi-Turn Web Navigation Agents0
A Theoretical Perspective for Speculative Decoding Algorithm0
Learning and Transferring Sparse Contextual Bigrams with Linear Transformers0
Neural spell-checker: Beyond words with synthetic data generationCode0
Dynamic Information Sub-Selection for Decision Support0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking0
Smaller Large Language Models Can Do Moral Self-Correction0
Toward Understanding In-context vs. In-weight Learning0
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction0
All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling0
Teaching a Language Model to Distinguish Between Similar Details using a Small Adversarial Training Set0
Robotic State Recognition with Image-to-Text Retrieval Task of Pre-Trained Vision-Language Model and Black-Box Optimization0
MutaPLM: Protein Language Modeling for Mutation Explanation and EngineeringCode4
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning0
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration0
Real-Time Personalization for LLM-based Recommendation with Customized In-Context LearningCode1
COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General PreferencesCode0
Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model0
PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretation0
Beyond Ontology in Dialogue State Tracking for Goal-Oriented ChatbotCode0
Online Intrinsic Rewards for Decision Making Agents from Large Language Model FeedbackCode1
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation0
Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection LayersCode1
Multimodal Quantum Natural Language Processing: A Novel Framework for using Quantum Methods to Analyse Real DataCode0
CurateGPT: A flexible language-model assisted biocuration tool0
Rethinking Code Refinement: Learning to Judge Code EfficiencyCode0
PerSRV: Personalized Sticker Retrieval with Vision-Language ModelCode0
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents0
Discrete Modeling via Boundary Conditional Diffusion Processes0
A Hierarchical Language Model For Interpretable Graph Reasoning0
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM GuidanceCode2
Anticipating Future with Large Language Model for Simultaneous Machine Translation0
VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration0
FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation0
SG-Bench: Evaluating LLM Safety Generalization Across Diverse Tasks and Prompt TypesCode1
Improving In-Context Learning with Small Language Model EnsemblesCode0
MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding0
Learning and Unlearning of Fabricated Knowledge in Language Models0
Abrupt Learning in Transformers: A Case Study on Matrix Completion0
f-PO: Generalizing Preference Optimization with f-divergence MinimizationCode1
Democratizing Reward Design for Personal and Representative Value-Alignment0
Reliable Semantic Understanding for Real World Zero-shot Object Goal Navigation0
Are VLMs Really BlindCode0
From melodic note sequences to pitches using word2vec0
Protecting Privacy in Multimodal Large Language Models with MLLMU-BenchCode2
Online Detecting LLM-Generated Texts via Sequential Hypothesis Testing by BettingCode0
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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