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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1401–1450 of 17610 papers

TitleStatusHype
Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication—0
Chronologically Consistent Large Language Models—0
MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model TrainingCode0
NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research—0
Protecting multimodal large language models against misleading visualizationsCode0
Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM AgentsCode1
Large Language Model Strategic Reasoning Evaluation through Behavioral Game Theory—0
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models—0
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language ModelsCode1
From Retrieval to Generation: Comparing Different Approaches—0
Sparse Auto-Encoder Interprets Linguistic Features in Large Language Models—0
M-LLM Based Video Frame Selection for Efficient Video Understanding—0
Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training—0
KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Model—0
AsymLoRA: Harmonizing Data Conflicts and Commonalities in MLLMsCode3
Conformal Tail Risk Control for Large Language Model Alignment—0
DiffCSS: Diverse and Expressive Conversational Speech Synthesis with Diffusion Models—0
Collaborative Stance Detection via Small-Large Language Model Consistency VerificationCode0
Playing Pokémon Red via Deep Reinforcement LearningCode1
GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration—0
Do Sparse Autoencoders Generalize? A Case Study of Answerability—0
UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook—0
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model—0
SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language ModelCode1
Conformal Linguistic Calibration: Trading-off between Factuality and Specificity—0
I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning—0
TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data ConsistencyCode0
Nexus: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision—0
AgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web PlatformsCode2
Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions—0
On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation—0
Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models—0
The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training—0
Kanana: Compute-efficient Bilingual Language Models—0
ANPMI: Assessing the True Comprehension Capabilities of LLMs for Multiple Choice Questions—0
Improving Representation Learning of Complex Critical Care Data with ICU-BERT—0
A City of Millions: Mapping Literary Social Networks At ScaleCode0
Evaluating Gender Bias in German Machine TranslationCode0
Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems—0
VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search—0
from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors—0
Enhancing DNA Foundation Models to Address Masking Inefficiencies—0
AMPO: Active Multi-Preference Optimization—0
MindMem: Multimodal for Predicting Advertisement Memorability Using LLMs and Deep Learning—0
olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language ModelsCode11
Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model GeneralizationCode0
Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models—0
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model ReasoningCode0
Broadening Discovery through Structural Models: Multimodal Combination of Local and Structural Properties for Predicting Chemical Features—0
LDGen: Enhancing Text-to-Image Synthesis via Large Language Model-Driven Language Representation—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