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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7951–8000 of 17610 papers

TitleStatusHype
The Moral Case for Using Language Model Agents for Recommendation—0
The Multilingual Divide and Its Impact on Global AI Safety—0
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs—0
The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy—0
The N-Grammys: Accelerating Autoregressive Inference with Learning-Free Batched Speculation—0
The Nordic Pile: A 1.2TB Nordic Dataset for Language Modeling—0
The NPU-HWC System for the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge—0
The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge—0
The NTNU System at the S&I Challenge 2025 SLA Open Track—0
The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia—0
The OpenGrm open-source finite-state grammar software libraries—0
Theoretical Analysis of Byte-Pair Encoding—0
Theoretical Analysis of Weak-to-Strong Generalization—0
Theoretical Benefit and Limitation of Diffusion Language Model—0
Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?—0
The OSU Realizer for SRST `18: Neural Sequence-to-Sequence Inflection and Incremental Locality-Based Linearization—0
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings—0
The Past, Present, and Future of Typological Databases in NLP—0
The Potential and Pitfalls of using a Large Language Model such as ChatGPT or GPT-4 as a Clinical Assistant—0
The potential of large language models for improving probability learning: A study on ChatGPT3.5 and first-year computer engineering students—0
The power of absolute discounting: all-dimensional distribution estimation—0
The Power of External Memory in Increasing Predictive Model Capacity—0
The Predictive Brain: Neural Correlates of Word Expectancy Align with Large Language Model Prediction Probabilities—0
The Qiyas Benchmark: Measuring ChatGPT Mathematical and Language Understanding in Arabic—0
Therapy as an NLP Task: Psychologists' Comparison of LLMs and Human Peers in CBT—0
The Real, the Better: Aligning Large Language Models with Online Human Behaviors—0
The Rediscovery Hypothesis: Language Models Need to Meet Linguistics—0
Correct and Optimal: the Regular Expression Inference Challenge—0
The Reliability Paradox: Exploring How Shortcut Learning Undermines Language Model Calibration—0
The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model—0
Thermodynamic Natural Gradient Descent—0
The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies—0
The Role of Context in Detecting the Target of Hate Speech—0
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning—0
The Role of n-gram Smoothing in the Age of Neural Networks—0
The RWTH Aachen German-English Machine Translation System for WMT 2015—0
The RWTH Aachen LVCSR system for IWSLT-2016 German Skype conversation recognition task—0
The RWTH Aachen Machine Translation System for WMT 2013—0
The RWTH Aachen Machine Translation System for WMT 2012—0
The RWTH Aachen University English-Romanian Machine Translation System for WMT 2016—0
The RWTH Aachen University Machine Translation Systems for WMT 2019—0
The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling—0
The Seemingly (Un)systematic Linking Element in Danish—0
The Self-Perception and Political Biases of ChatGPT—0
The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training—0
Optimal Inflationary Potentials—0
The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource—0
The Slovak Categorized News Corpus—0
The SMarT Classifier for Arabic Fine-Grained Dialect Identification—0
The Sociolinguistic Foundations of Language Modeling—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