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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13601–13650 of 17610 papers

TitleStatusHype
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
The Sogou-TIIC Speech Translation System for IWSLT 2018—0
The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge—0
The Sound of Populism: Distinct Linguistic Features Across Populist Variants—0
The State of Large Language Models for African Languages: Progress and Challenges—0
The TALP--UPC Spanish--English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System—0
The Task-oriented Queries Benchmark (ToQB)—0
The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents—0
The Trade-offs of Domain Adaptation for Neural Language Models—0
The Turking Test: Can Language Models Understand Instructions?—0
The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support—0
The (Un)faithful Machine Translator—0
The University of Cambridge Russian-English System at WMT13—0
The University of Edinburgh’s English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task—0
The University of Edinburgh's Submissions to the WMT19 News Translation Task—0
The University of Edinburgh’s Submission to the IWSLT21 Simultaneous Translation Task—0
The University of Illinois submission to the WMT 2015 Shared Translation Task—0
The UPC Submission to the WMT 2012 Shared Task on Quality Estimation—0
The Use of a Large Language Model for Cyberbullying Detection—0
The Value of Nothing: Multimodal Extraction of Human Values Expressed by TikTok Influencers—0
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance?—0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022—0
Thick-Net: Parallel Network Structure for Sequential Modeling—0
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions—0
Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding—0
Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2—0
Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition—0
Thinking Like an Annotator: Generation of Dataset Labeling Instructions—0
Thinking Tokens for Language Modeling—0
Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving—0
Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection—0
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language—0
Thought Space Explorer: Navigating and Expanding Thought Space for Large Language Model Reasoning—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