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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 19512000 of 17610 papers

TitleStatusHype
VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language ModelsCode1
Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU HeterogeneityCode1
Evaluating Retrieval Quality in Retrieval-Augmented GenerationCode1
FineRec:Exploring Fine-grained Sequential RecommendationCode1
Length Generalization of Causal Transformers without Position EncodingCode1
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and NegativesCode1
MemLLM: Finetuning LLMs to Use An Explicit Read-Write MemoryCode1
VG4D: Vision-Language Model Goes 4D Video RecognitionCode1
Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question AnsweringCode1
Forcing Diffuse Distributions out of Language ModelsCode1
Knowledge-enhanced Visual-Language Pretraining for Computational PathologyCode1
Memory Sharing for Large Language Model based AgentsCode1
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMsCode1
The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential RecommendationCode1
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied AgentsCode1
Scalable Language Model with Generalized Continual LearningCode1
OpenBias: Open-set Bias Detection in Text-to-Image Generative ModelsCode1
High-Dimension Human Value Representation in Large Language ModelsCode1
Lossless Acceleration of Large Language Model via Adaptive N-gram Parallel DecodingCode1
ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain ModelingCode1
Rethinking How to Evaluate Language Model JailbreakCode1
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language ModelsCode1
Does Transformer Interpretability Transfer to RNNs?Code1
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language ModelsCode1
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model MergingCode1
Retrieval-Augmented Open-Vocabulary Object DetectionCode1
Xiwu: A Basis Flexible and Learnable LLM for High Energy PhysicsCode1
SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal BudgetCode1
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?Code1
Image-Text Co-Decomposition for Text-Supervised Semantic SegmentationCode1
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language ModelsCode1
CONFLARE: CONFormal LArge language model REtrievalCode1
CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question AnsweringCode1
nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion StatesCode1
LawInstruct: A Resource for Studying Language Model Adaptation to the Legal DomainCode1
Large Language Models for Orchestrating Bimanual RobotsCode1
Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and OptimizationCode1
Query Performance Prediction using Relevance Judgments Generated by Large Language ModelsCode1
Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model AlignmentCode1
LITE: Modeling Environmental Ecosystems with Multimodal Large Language ModelsCode1
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain GeneralizationCode1
Extensive Self-Contrast Enables Feedback-Free Language Model AlignmentCode1
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language ModelCode1
Instruction-Driven Game Engines on Large Language ModelsCode1
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction TasksCode1
Configurable Safety Tuning of Language Models with Synthetic Preference DataCode1
DiLM: Distilling Dataset into Language Model for Text-level Dataset DistillationCode1
Latxa: An Open Language Model and Evaluation Suite for BasqueCode1
STaR-GATE: Teaching Language Models to Ask Clarifying QuestionsCode1
The New Agronomists: Language Models are Experts in Crop ManagementCode1
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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