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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4101–4150 of 17610 papers

TitleStatusHype
Baseline Models for Pronoun Prediction and Pronoun-Aware Translation—0
Anchor function: a type of benchmark functions for studying language models—0
edATLAS: An Efficient Disambiguation Algorithm for Texting in Languages with Abugida Scripts—0
Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation—0
Anchored Diffusion Language Model—0
Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors—0
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder—0
Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models—0
BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs)—0
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference—0
ED-FAITH: Evaluating Dialogue Summarization on Faithfulness—0
Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization—0
BAS: An Answer Selection Method Using BERT Language Model—0
Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization—0
Decipherment with a Million Random Restarts—0
BART-light: One Decoder Layer Is Enough—0
Anchor-based Robust Finetuning of Vision-Language Models—0
Decipherment of Substitution Ciphers with Neural Language Models—0
Decipherment Complexity in 1:1 Substitution Ciphers—0
BART for Post-Correction of OCR Newspaper Text—0
Deciphering Related Languages—0
Deciphering Foreign Language by Combining Language Models and Context Vectors—0
BART based semantic correction for Mandarin automatic speech recognition system—0
ACER: Automatic Language Model Context Extension via Retrieval—0
Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games—0
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning—0
Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages—0
DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation—0
An Autonomous Large Language Model Agent for Chemical Literature Data Mining—0
Deception in Reinforced Autonomous Agents—0
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks—0
Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language Model—0
Debias your Large Multi-Modal Model at Test-Time with Non-Contrastive Visual Attribute Steering—0
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models—0
Economic Rationality under Specialization: Evidence of Decision Bias in AI Agents—0
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference—0
Editing Arbitrary Propositions in LLMs without Subject Labels—0
Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation—0
BanglaHateBERT: BERT for Abusive Language Detection in Bengali—0
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination—0
Echo-Attention: Attend Once and Get N Attentions for Free—0
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning—0
A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images—0
Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement—0
Debate-Driven Multi-Agent LLMs for Phishing Email Detection—0
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla—0
Ad Text Classification with Transformer-Based Natural Language Processing Methods—0
ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning—0
Deanthropomorphising NLP: Can a Language Model Be Conscious?—0
De-amplifying Bias from Differential Privacy in Language Model Fine-tuning—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