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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6351–6400 of 17610 papers

TitleStatusHype
Can Multimodal Large Language Model Think Analogically?—0
Privacy Leakage Overshadowed by Views of AI: A Study on Human Oversight of Privacy in Language Model Agent—0
Interacting Large Language Model Agents. Interpretable Models and Social Learning—0
Can Large Language Model Predict Employee Attrition?—0
A Mechanistic Explanatory Strategy for XAI—0
PRIMO: Progressive Induction for Multi-hop Open Rule Generation—0
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks—0
SPRING Lab IITM's submission to Low Resource Indic Language Translation Shared Task—0
Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment AnalysisCode0
ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents—0
LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering—0
RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models—0
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in TransformersCode0
Unified Generative and Discriminative Training for Multi-modal Large Language Models—0
Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations—0
Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model—0
Enhancing AAC Software for Dysarthric Speakers in e-Health Settings: An Evaluation Using TORGO—0
Enhancing the Traditional Chinese Medicine Capabilities of Large Language Model through Reinforcement Learning from AI Feedback—0
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning—0
DEREC-SIMPRO: unlock Language Model benefits to advance Synthesis in Data Clean Room—0
From Context to Action: Analysis of the Impact of State Representation and Context on the Generalization of Multi-Turn Web Navigation Agents—0
ALISE: Accelerating Large Language Model Serving with Speculative Scheduling—0
EchoNarrator: Generating natural text explanations for ejection fraction predictionsCode0
Morphological Typology in BPE Subword Productivity and Language Modeling—0
π_0: A Vision-Language-Action Flow Model for General Robot Control—0
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching—0
Towards Reliable Alignment: Uncertainty-aware RLHF—0
Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking—0
Representative Social Choice: From Learning Theory to AI Alignment—0
The NPU-HWC System for the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge—0
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees—0
Thought Space Explorer: Navigating and Expanding Thought Space for Large Language Model Reasoning—0
Stereo-Talker: Audio-driven 3D Human Synthesis with Prior-Guided Mixture-of-Experts—0
Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach—0
Weight decay induces low-rank attention layers—0
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning—0
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction—0
All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling—0
Beyond Ontology in Dialogue State Tracking for Goal-Oriented ChatbotCode0
COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General PreferencesCode0
Dynamic Information Sub-Selection for Decision Support—0
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration—0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking—0
Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model—0
A Theoretical Perspective for Speculative Decoding Algorithm—0
Toward Understanding In-context vs. In-weight Learning—0
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation—0
PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretation—0
Smaller Large Language Models Can Do Moral Self-Correction—0
Neural spell-checker: Beyond words with synthetic data generationCode0
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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