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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6901–6950 of 17610 papers

TitleStatusHype
CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches—0
當代非監督式方法之比較於節錄式語音摘要 (An Empirical Comparison of Contemporary Unsupervised Approaches for Extractive Speech Summarization) [In Chinese]—0
CAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring—0
CageViT: Convolutional Activation Guided Efficient Vision Transformer—0
Calculating Question Similarity is Enough: A New Method for KBQA Tasks—0
Calibrated Cache Model for Few-Shot Vision-Language Model Adaptation—0
Calibrating the Confidence of Large Language Models by Eliciting Fidelity—0
CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model—0
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design—0
CALM: Continuous Adaptive Learning for Language Modeling—0
CaLMFlow: Volterra Flow Matching using Causal Language Models—0
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering—0
CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation—0
Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation—0
CamemBERT 2.0: A Smarter French Language Model Aged to Perfection—0
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data—0
Camera Control at the Edge with Language Models for Scene Understanding—0
Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks—0
Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions?—0
Can AI Solve the Peer Review Crisis? A Large Scale Cross Model Experiment of LLMs' Performance and Biases in Evaluating over 1000 Economics Papers—0
CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM—0
Can a Single Model Master Both Multi-turn Conversations and Tool Use? CALM: A Unified Conversational Agentic Language Model—0
Can a student Large Language Model perform as well as it's teacher?—0
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care—0
CancerLLM: A Large Language Model in Cancer Domain—0
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?—0
Can Character-based Language Models Improve Downstream Task Performances In Low-Resource And Noisy Language Scenarios?—0
Can ChatGPT be Your Personal Medical Assistant?—0
Can ChatGPT pass the Vietnamese National High School Graduation Examination?—0
Can tweets predict article retractions? A comparison between human and LLM labelling—0
Can Chat GPT solve a Linguistics Exam?—0
Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis—0
Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?—0
Can Data Diversity Enhance Learning Generalization?—0
Candidate evaluation strategies for improved difficulty prediction of language tests—0
Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture—0
Can Discourse Relations be Identified Incrementally?—0
Can DNNs Learn to Lipread Full Sentences?—0
Can Entropy Explain Successor Surprisal Effects in Reading?—0
Can Foundational Large Language Models Assist with Conducting Pharmaceuticals Manufacturing Investigations?—0
Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?—0
Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach—0
Privacy Leakage Overshadowed by Views of AI: A Study on Human Oversight of Privacy in Language Model Agent—0
Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?—0
Can Language Model Moderators Improve the Health of Online Discourse?—0
Can Language Models Be Specific? How?—0
Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa—0
Can Language Models Learn to Listen?—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