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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7851–7875 of 17610 papers

TitleStatusHype
The Capacity for Moral Self-Correction in Large Language Models—0
The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving—0
The cell as a token: high-dimensional geometry in language models and cell embeddings—0
The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique—0
The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal—0
The CMU Machine Translation Systems at WMT 2013: Syntax, Synthetic Translation Options, and Pseudo-References—0
The Complexity of Learning Sparse Superposed Features with Feedback—0
The Consensus Game: Language Model Generation via Equilibrium Search—0
The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model—0
The Context-Dependent Additive Recurrent Neural Net—0
The Contribution of Lyrics and Acoustics to Collaborative Understanding of Mood—0
The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?—0
The COVID That Wasn't: Counterfactual Journalism Using GPT—0
The COVID That Wasn’t: Counterfactual Journalism Using GPT—0
The CRINGE Loss: Learning what language not to model—0
The Dark Side of Human Feedback: Poisoning Large Language Models via User Inputs—0
The Dark Side of the Language: Pre-trained Transformers in the DarkNet—0
The DCU-ICTCAS MT system at WMT 2014 on German-English Translation Task—0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning—0
The Detection of Distributional Discrepancy for Text Generation—0
The Developmental Landscape of In-Context Learning—0
The Differences Between Direct Alignment Algorithms are a Blur—0
The Dream Within Huang Long Cave: AI-Driven Interactive Narrative for Family Storytelling and Emotional Reflection—0
Emergent social conventions and collective bias in LLM populations—0
The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model—0
Show:102550
← PrevPage 315 of 705Next →
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