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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 32013250 of 17610 papers

TitleStatusHype
Learning Cross-modal Context Graph for Visual GroundingCode1
GraphXForm: Graph transformer for computer-aided molecular designCode1
Learning How to Ask: Querying LMs with Mixtures of Soft PromptsCode1
Collective Constitutional AI: Aligning a Language Model with Public InputCode1
Asynchronous Local-SGD Training for Language ModelingCode1
CoLLM: A Large Language Model for Composed Image RetrievalCode1
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text PretrainingCode1
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed GraphsCode1
Leveraging Natural Supervision for Language Representation Learning and GenerationCode1
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable RecommendationCode1
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-trainingCode1
GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-trainingCode1
LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned ProportionsCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
Polynomial, trigonometric, and tropical activationsCode1
Grounding Language Models for Visual Entity RecognitionCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
Grounded Compositional Outputs for Adaptive Language ModelingCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
LEAM: A Prompt-only Large Language Model-enabled Antenna Modeling MethodCode1
Prompsit's submission to WMT 2018 Parallel Corpus Filtering shared taskCode1
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
Layer-wise Pruning of Transformer Attention Heads for Efficient Language ModelingCode1
Cross-Thought for Sentence Encoder Pre-trainingCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
PromptBoosting: Black-Box Text Classification with Ten Forward PassesCode1
Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-RankingCode1
Learning Approximate Inference Networks for Structured PredictionCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
Lawformer: A Pre-trained Language Model for Chinese Legal Long DocumentsCode1
LAVENDER: Unifying Video-Language Understanding as Masked Language ModelingCode1
LAVCap: LLM-based Audio-Visual Captioning using Optimal TransportCode1
LaunchpadGPT: Language Model as Music Visualization Designer on LaunchpadCode1
GypSum: Learning Hybrid Representations for Code SummarizationCode1
LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language ModelsCode1
SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing TasksCode1
Hallucinations in Large Multilingual Translation ModelsCode1
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward HackingCode1
C-STS: Conditional Semantic Textual SimilarityCode1
Handwritten Mathematical Expression Recognition with Bidirectionally Trained TransformerCode1
Learning Associative Inference Using Fast Weight MemoryCode1
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language ModelCode1
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language ModelCode1
BiasEdit: Debiasing Stereotyped Language Models via Model EditingCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Common Sense Enhanced Knowledge-based Recommendation with Large Language ModelCode1
Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learningCode1
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-ThoughtCode1
Knowledge-Augmented Language Models for Cause-Effect Relation ClassificationCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
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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