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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17001–17050 of 17610 papers

TitleStatusHype
Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models—0
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation—0
DATScore: Evaluating Translation with Data Augmented Translations—0
Davinci the Dualist: the mind-body divide in large language models and in human learners—0
DAWSON: Data Augmentation using Weak Supervision On Natural Language—0
DBMS-KU Interpolation for WMT19 News Translation Task—0
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions—0
DCU-ADAPT: Learning Edit Operations for Microblog Normalisation with the Generalised Perceptron—0
DCU-Lingo24 Participation in WMT 2014 Hindi-English Translation task—0
DCU-Symantec at the WMT 2013 Quality Estimation Shared Task—0
DDPT: Diffusion-Driven Prompt Tuning for Large Language Model Code Generation—0
De-amplifying Bias from Differential Privacy in Language Model Fine-tuning—0
Deanthropomorphising NLP: Can a Language Model Be Conscious?—0
Debate-Driven Multi-Agent LLMs for Phishing Email Detection—0
Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement—0
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning—0
Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation—0
Debias your Large Multi-Modal Model at Test-Time with Non-Contrastive Visual Attribute Steering—0
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks—0
Deception in Reinforced Autonomous Agents—0
DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation—0
Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages—0
Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games—0
Deciphering Foreign Language by Combining Language Models and Context Vectors—0
Deciphering Related Languages—0
Decipherment Complexity in 1:1 Substitution Ciphers—0
Decipherment of Substitution Ciphers with Neural Language Models—0
Decipherment with a Million Random Restarts—0
Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization—0
Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models—0
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder—0
Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation—0
Decoding, Fast and Slow: A Case Study on Balancing Trade-Offs in Incremental, Character-level Pragmatic Reasoning—0
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs—0
Decoding Running Key Ciphers—0
Decoding the Diversity: A Review of the Indic AI Research Landscape—0
The Highs and Lows of Simple Lexical Domain Adaptation Approaches for Neural Machine Translation—0
Decoding with Large-Scale Neural Language Models Improves Translation—0
Decolonial AI Alignment: Openness, Viśesa-Dharma, and Including Excluded Knowledges—0
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation—0
Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors—0
Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes—0
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey—0
Deconstructing What Makes a Good Optimizer for Language Models—0
DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models—0
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering—0
Decoupled Context Processing for Context Augmented Language Modeling—0
Decoupled Structure for Improved Adaptability of End-to-End Models—0
Decoupling SQL Query Hardness Parsing for Text-to-SQL—0
Deduplicating Training Data Makes Language Models Better—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