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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4851–4900 of 17610 papers

TitleStatusHype
Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and ReactionCode2
Efficient and Long-Tailed Generalization for Pre-trained Vision-Language ModelCode0
Automatic benchmarking of large multimodal models via iterative experiment programmingCode0
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-ExpertsCode5
Refine Large Language Model Fine-tuning via Instruction Vector—0
QueerBench: Quantifying Discrimination in Language Models Toward Queer IdentitiesCode0
The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge—0
Large Language Model as a Universal Clinical Multi-task Decoder—0
MCSD: An Efficient Language Model with Diverse Fusion—0
Problem-Solving in Language Model NetworksCode0
In-Context Learning of Energy Functions—0
Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for EnsemblingCode2
RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image UnderstandingCode1
MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQLCode1
PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models—0
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMCode2
LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation—0
PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems—0
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
Stealth edits to large language modelsCode0
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression—0
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual UnderstandingCode0
Self-Train Before You TranscribeCode0
A General Framework for Load Forecasting based on Pre-trained Large Language Model—0
CItruS: Chunked Instruction-aware State Eviction for Long Sequence ModelingCode0
LiLiuM: eBay's Large Language Models for e-commerce—0
Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction modelsCode0
FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information DisclosureCode0
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization—0
What Kinds of Tokens Benefit from Distant Text? An Analysis on Long Context Language Modeling—0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers—0
Large Scale Transfer Learning for Tabular Data via Language ModelingCode2
UniGLM: Training One Unified Language Model for Text-Attributed Graph EmbeddingCode1
Mitigating Large Language Model Hallucination with Faithful Finetuning—0
SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language ModelCode1
Large Language Models and Knowledge Graphs for Astronomical Entity Disambiguation—0
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language ModelCode1
Self-training Large Language Models through Knowledge DetectionCode0
Unifying Multimodal Retrieval via Document Screenshot Embedding—0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
Dialogue Action Tokens: Steering Language Models in Goal-Directed Dialogue with a Multi-Turn PlannerCode1
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code IntelligenceCode9
ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPOCode2
DataComp-LM: In search of the next generation of training sets for language modelsCode7
Generative Visual Instruction TuningCode0
Promises, Outlooks and Challenges of Diffusion Language Modeling—0
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive ReasoningCode3
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models—0
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning AbilitiesCode2
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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