SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7901–7950 of 17610 papers

TitleStatusHype
Elliptical AttentionCode0
From Single Agent to Multi-Agent: Improving Traffic Signal Control—0
Block-level Text Spotting with LLMs—0
Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach—0
Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets—0
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration—0
Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided DecodingCode0
In-Context Former: Lightning-fast Compressing Context for Large Language Model—0
Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual ErrorsCode0
Efficient and Long-Tailed Generalization for Pre-trained Vision-Language ModelCode0
Refine Large Language Model Fine-tuning via Instruction Vector—0
Applying Ensemble Methods to Model-Agnostic Machine-Generated Text Detection—0
GPT Czech Poet: Generation of Czech Poetic Strophes with Language Models—0
Automatic benchmarking of large multimodal models via iterative experiment programmingCode0
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?Code0
In-Context Learning of Energy Functions—0
Generative Artificial Intelligence-Guided User Studies: An Application for Air Taxi Services—0
LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization—0
Problem-Solving in Language Model NetworksCode0
Stealth edits to large language modelsCode0
LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation—0
Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages—0
QueerBench: Quantifying Discrimination in Language Models Toward Queer IdentitiesCode0
Large Language Model as a Universal Clinical Multi-task Decoder—0
QOG:Question and Options Generation based on Language Model—0
Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model AlignmentCode0
PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems—0
The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge—0
MCSD: An Efficient Language Model with Diverse Fusion—0
MaskPure: Improving Defense Against Text Adversaries with Stochastic PurificationCode0
PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models—0
UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models—0
VideoLLM-online: Online Video Large Language Model for Streaming Video—0
What Kinds of Tokens Benefit from Distant Text? An Analysis on Long Context Language Modeling—0
Unifying Multimodal Retrieval via Document Screenshot Embedding—0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information DisclosureCode0
CoSQA+: Pioneering the Multi-Choice Code Search Benchmark with Test-Driven AgentsCode0
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression—0
HARE: HumAn pRiors, a key to small language model Efficiency—0
Generative Visual Instruction TuningCode0
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models—0
SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users—0
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual UnderstandingCode0
CItruS: Chunked Instruction-aware State Eviction for Long Sequence ModelingCode0
Adversarial Style Augmentation via Large Language Model for Robust Fake News DetectionCode0
Mitigating Large Language Model Hallucination with Faithful Finetuning—0
SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical AlterationsCode0
Large Language Models and Knowledge Graphs for Astronomical Entity Disambiguation—0
Knowledge-to-Jailbreak: Investigating Knowledge-driven Jailbreaking Attacks for Large Language ModelsCode0
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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