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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5151–5200 of 17610 papers

TitleStatusHype
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation—0
Language modelling techniques for analysing the impact of human genetic variation—0
Look Before You Leap: Using Serialized State Machine for Language Conditioned Robotic Manipulation—0
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining—0
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization—0
Frequency Autoregressive Image Generation with Continuous Tokens—0
Is Your Video Language Model a Reliable Judge?—0
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning—0
Unveiling Biases in AI: ChatGPT's Political Economy Perspectives and Human Comparisons—0
Wanda++: Pruning Large Language Models via Regional GradientsCode0
PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks—0
The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy—0
Measuring temporal effects of agent knowledge by date-controlled tool use—0
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model CompressionCode0
Know Thy Judge: On the Robustness Meta-Evaluation of LLM Safety Judges—0
TPC: Cross-Temporal Prediction Connection for Vision-Language Model Hallucination Reduction—0
KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney DiseaseCode0
Predictable Scale: Part I -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining—0
AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management—0
Better Process Supervision with Bi-directional Rewarding Signals—0
Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities—0
AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services—0
Fine-Tuning Florence2 for Enhanced Object Detection in Un-constructed Environments: Vision-Language Model Approach—0
From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design—0
Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues—0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
Human Preferences for Constructive Interactions in Language Model Alignment—0
PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary AlgorithmsCode0
Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design DecisionsCode0
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA—0
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
Optimizing open-domain question answering with graph-based retrieval augmented generation—0
Language Models can Self-Improve at State-Value Estimation for Better SearchCode0
Trust, Experience, and Innovation: Key Factors Shaping American Attitudes About AI—0
Iterative Value Function Optimization for Guided Decoding—0
RedChronos: A Large Language Model-Based Log Analysis System for Insider Threat Detection in Enterprises—0
Tabby: Tabular Data Synthesis with Language Models—0
Vision-Language Model IP Protection via Prompt-based Learning—0
Generator-Assistant Stepwise Rollback Framework for Large Language Model AgentCode0
AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation—0
DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models—0
Hierarchical Re-ranker Retriever (HRR)—0
Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks—0
ATLaS: Agent Tuning via Learning Critical Steps—0
Haste Makes Waste: Evaluating Planning Abilities of LLMs for Efficient and Feasible Multitasking with Time Constraints Between ActionsCode0
Can (A)I Change Your Mind?Code0
Adaptively profiling models with task elicitation—0
Hebbian learning the local structure of language—0
ReaderLM-v2: Small Language Model for HTML to Markdown and JSON—0
Jailbreaking Safeguarded Text-to-Image Models via Large Language Models—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