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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6951–7000 of 17610 papers

TitleStatusHype
Can Large Language Model Predict Employee Attrition?—0
Can Large Language Models do Analytical Reasoning?—0
Can Large Language Models Predict Associations Among Human Attitudes?—0
Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?—0
Can LLM Assist in the Evaluation of the Quality of Machine Learning Explanations?—0
Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes—0
Can LLMs Compute with Reasons?—0
Can LLMs Explain Themselves Counterfactually?—0
Can LLMs facilitate interpretation of pre-trained language models?—0
Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games—0
Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?—0
Can Multimodal Large Language Model Think Analogically?—0
Can Offline Reinforcement Learning Help Natural Language Understanding?—0
Can Perplexity Predict Fine-Tuning Performance? An Investigation of Tokenization Effects on Sequential Language Models for Nepali—0
Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?—0
Can Sequence-to-Sequence Models Crack Substitution Ciphers?—0
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought—0
Can Symbol Grounding Improve Low-Level NLP? Word Segmentation as a Case Study—0
Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models—0
cantnlp@LT-EDI-2023: Homophobia/Transphobia Detection in Social Media Comments using Spatio-Temporally Retrained Language Models—0
Can Unconditional Language Models Recover Arbitrary Sentences?—0
Can VLMs be used on videos for action recognition? LLMs are Visual Reasoning Coordinators—0
Can We Reverse In-Context Knowledge Edits?—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
Can we trust the evaluation on ChatGPT?—0
Can Wikipedia Categories Improve Masked Language Model Pretraining?—0
Can You Trust Your Metric? Automatic Concatenation-Based Tests for Metric Validity—0
CapeLLM: Support-Free Category-Agnostic Pose Estimation with Multimodal Large Language Models—0
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model—0
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation—0
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model—0
CapText: Large Language Model-based Caption Generation From Image Context and Description—0
Capturing Topic Framing via Masked Language Modeling—0
CarbonChat: Large Language Model-Based Corporate Carbon Emission Analysis and Climate Knowledge Q&A System—0
Carbon Footprint Evaluation of Code Generation through LLM as a Service—0
Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification—0
CaRDiff: Video Salient Object Ranking Chain of Thought Reasoning for Saliency Prediction with Diffusion—0
CareBot: A Pioneering Full-Process Open-Source Medical Language Model—0
CART: Compositional Auto-Regressive Transformer for Image Generation—0
Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information—0
Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning—0
Cascaded Beam Search: Plug-and-Play Terminology-Forcing For Neural Machine Translation—0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition—0
Cascaded Semantic and Positional Self-Attention Network for Document Classification—0
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter—0
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios—0
CASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement—0
Casper: Prompt Sanitization for Protecting User Privacy in Web-Based Large Language Models—0
Catalysis distillation neural network for the few shot open catalyst challenge—0
CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans—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