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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 23012350 of 17610 papers

TitleStatusHype
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledgeCode1
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and CollaborationCode1
Zero-shot audio captioning with audio-language model guidance and audio context keywordsCode1
Towards Open-Ended Visual Recognition with Large Language ModelCode1
Towards the Law of Capacity Gap in Distilling Language ModelsCode1
An Analysis and Mitigation of the Reversal CurseCode1
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language ModelsCode1
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image ClassificationCode1
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human PreferencesCode1
CFBenchmark: Chinese Financial Assistant Benchmark for Large Language ModelCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense RetrievalCode1
Chain of Images for Intuitively ReasoningCode1
u-LLaVA: Unifying Multi-Modal Tasks via Large Language ModelCode1
Speech language models lack important brain-relevant semanticsCode1
Massive Editing for Large Language Models via Meta LearningCode1
Beyond Size: How Gradients Shape Pruning Decisions in Large Language ModelsCode1
Multilingual Mathematical AutoformalizationCode1
Meta-Adapter: An Online Few-shot Learner for Vision-Language ModelCode1
DeepInception: Hypnotize Large Language Model to Be JailbreakerCode1
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI AgentsCode1
An Efficient Self-Supervised Cross-View Training For Sentence EmbeddingCode1
GateLoop: Fully Data-Controlled Linear Recurrence for Sequence ModelingCode1
EmojiLM: Modeling the New Emoji LanguageCode1
Collaborative Large Language Model for Recommender SystemsCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation RecognitionCode1
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot ClassificationCode1
Plug-and-Play Policy Planner for Large Language Model Powered Dialogue AgentsCode1
Large Language Model Can Interpret Latent Space of Sequential RecommenderCode1
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched PromptsCode1
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionCode1
LitCab: Lightweight Language Model Calibration over Short- and Long-form ResponsesCode1
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language ModelsCode1
TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language UnderstandingCode1
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4VCode1
LLMSTEP: LLM proofstep suggestions in LeanCode1
Benchingmaking Large Langage Models in Biomedical Triple ExtractionCode1
Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General HealthcareCode1
Real-time Animation Generation and Control on Rigged Models via Large Language ModelsCode1
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each BenchmarkCode1
InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction OperatorsCode1
Content-based Controls For Music Large Language ModelingCode1
Proving Test Set Contamination in Black Box Language ModelsCode1
PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream ApplicationsCode1
LightLM: A Lightweight Deep and Narrow Language Model for Generative RecommendationCode1
An Open Source Data Contamination Report for Large Language ModelsCode1
CompeteAI: Understanding the Competition Dynamics in Large Language Model-based AgentsCode1
SuperHF: Supervised Iterative Learning from Human FeedbackCode1
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression RecognitionCode1
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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