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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 651700 of 17610 papers

TitleStatusHype
Towards a Multimodal Large Language Model with Pixel-Level Insight for BiomedicineCode2
Predicting Human Brain States with TransformerCode2
Granite GuardianCode2
C^2LEVA: Toward Comprehensive and Contamination-Free Language Model EvaluationCode2
LinVT: Empower Your Image-level Large Language Model to Understand VideosCode2
FLAIR: VLM with Fine-grained Language-informed Image RepresentationsCode2
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation ModelsCode2
Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMsCode2
KV Shifting Attention Enhances Language ModelingCode2
MotionLLaMA: A Unified Framework for Motion Synthesis and ComprehensionCode2
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object DetectionCode2
Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against JailbreaksCode2
Large Language Model with Region-guided Referring and Grounding for CT Report GenerationCode2
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human expertsCode2
ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow DataCode2
GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AICode2
MC-LLaVA: Multi-Concept Personalized Vision-Language ModelCode2
BianCang: A Traditional Chinese Medicine Large Language ModelCode2
GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual GroundingCode2
SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction TuningCode2
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
TIPO: Text to Image with Text Presampling for Prompt OptimizationCode2
Tucano: Advancing Neural Text Generation for PortugueseCode2
The Super Weight in Large Language ModelsCode2
Concept Bottleneck Language Models For protein designCode2
LLM-PySC2: Starcraft II learning environment for Large Language ModelsCode2
End-to-End Navigation with Vision Language Models: Transforming Spatial Reasoning into Question-AnsweringCode2
PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-trainingCode2
V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference OptimizationCode2
RAGViz: Diagnose and Visualize Retrieval-Augmented GenerationCode2
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge GraphsCode2
GPT or BERT: why not both?Code2
What is Wrong with Perplexity for Long-context Language Modeling?Code2
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM GuidanceCode2
Protecting Privacy in Multimodal Large Language Models with MLLMU-BenchCode2
Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language ModelCode2
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language ModelsCode2
PAPILLON: Privacy Preservation from Internet-based and Local Language Model EnsemblesCode2
MiniPLM: Knowledge Distillation for Pre-Training Language ModelsCode2
Improve Vision Language Model Chain-of-thought ReasoningCode2
RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and StyleCode2
SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent EvaluationCode2
A Systematic Study of Cross-Layer KV Sharing for Efficient LLM InferenceCode2
Montessori-Instruct: Generate Influential Training Data Tailored for Student LearningCode2
On the Role of Attention Heads in Large Language Model SafetyCode2
Process Reward Model with Q-Value RankingsCode2
MLLM can see? Dynamic Correction Decoding for Hallucination MitigationCode2
WeatherDG: LLM-assisted Diffusion Model for Procedural Weather Generation in Domain-Generalized Semantic SegmentationCode2
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
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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