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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 5351–5400 of 17610 papers

TitleStatusHype
Towards Multi-Task Multi-Modal Models: A Video Generative Perspective—0
A Survey of Multimodal Large Language Model from A Data-centric PerspectiveCode2
Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search—0
Semantic Importance-Aware Communications with Semantic Correction Using Large Language Models—0
Revisit, Extend, and Enhance Hessian-Free Influence Functions—0
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of ExemplarsCode0
MoEUT: Mixture-of-Experts Universal TransformersCode2
Theoretical Analysis of Weak-to-Strong Generalization—0
How Well Do Deep Learning Models Capture Human Concepts? The Case of the Typicality Effect—0
M^3GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and GenerationCode1
A transfer learning framework for weak-to-strong generalization—0
C3LLM: Conditional Multimodal Content Generation Using Large Language Models—0
Evolutionary Large Language Model for Automated Feature TransformationCode1
Finetuning Large Language Model for Personalized RankingCode1
Large Language Model Pruning—0
Large Language Model (LLM) for Standard Cell Layout Design Optimization—0
Large Language Model Sentinel: LLM Agent for Adversarial Purification—0
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign UsersCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Enhancing Augmentative and Alternative Communication with Card Prediction and Colourful Semantics—0
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation—0
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMsCode0
Scaling Laws for Discriminative Classification in Large Language Models—0
LM4LV: A Frozen Large Language Model for Low-level Vision TasksCode2
NuwaTS: a Foundation Model Mending Every Incomplete Time Series—0
Composed Image Retrieval for Remote SensingCode2
Sparse Matrix in Large Language Model Fine-tuningCode1
RAEE: A Robust Retrieval-Augmented Early Exiting Framework for Efficient Inference—0
Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems—0
iREPO: implicit Reward Pairwise Difference based Empirical Preference Optimization—0
Emergence of a High-Dimensional Abstraction Phase in Language TransformersCode0
Off-the-shelf ChatGPT is a Good Few-shot Human Motion Predictor—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
SEP: Self-Enhanced Prompt Tuning for Visual-Language ModelCode0
Sparse maximal update parameterization: A holistic approach to sparse training dynamicsCode2
DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZCode5
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision ModelsCode2
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection—0
GECKO: Generative Language Model for English, Code and Korean—0
V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLMCode0
Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning—0
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs—0
Aya 23: Open Weight Releases to Further Multilingual Progress—0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
AutoCoder: Enhancing Code Large Language Model with AIEV-InstructCode4
Agentic Skill DiscoveryCode1
Extracting Prompts by Inverting LLM OutputsCode2
BiMix: A Bivariate Data Mixing Law for Language Model Pretraining—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified