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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4601–4650 of 17610 papers

TitleStatusHype
SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing—0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions—0
Accompanied Singing Voice Synthesis with Fully Text-controlled Melody—0
Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?Code0
Helpful assistant or fruitful facilitator? Investigating how personas affect language model behaviorCode0
Investigating the Effects of Large-Scale Pseudo-Stereo Data and Different Speech Foundation Model on Dialogue Generative Spoken Language Model—0
Language Model Alignment in Multilingual Trolley ProblemsCode1
An End-to-End Speech Summarization Using Large Language Model—0
FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs—0
Why do LLaVA Vision-Language Models Reply to Images in English?—0
Fake News Detection and Manipulation Reasoning via Large Vision-Language Models—0
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning—0
A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document UnderstandingCode2
GPTCast: a weather language model for precipitation nowcastingCode1
Synthetic Multimodal Question Generation—0
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning—0
Neurocache: Efficient Vector Retrieval for Long-range Language ModelingCode0
TokenPacker: Efficient Visual Projector for Multimodal LLMCode3
Multi-Modal Video Dialog State Tracking in the Wild—0
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse AttentionCode9
AutoFlow: Automated Workflow Generation for Large Language Model AgentsCode2
Image-to-Text Logic Jailbreak: Your Imagination can Help You Do Anything—0
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving—0
Large Language Model Enhanced Knowledge Representation Learning: A Survey—0
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via AdaptersCode0
First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1—0
FoldGPT: Simple and Effective Large Language Model Compression Scheme—0
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational AgentsCode0
Bridging the Gap: Transfer Learning from English PLMs to Malaysian English—0
SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language ModelCode1
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented GenerationCode0
An Empirical Comparison of Generative Approaches for Product Attribute-Value IdentificationCode0
Memory^3: Language Modeling with Explicit Memory—0
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model AgentsCode3
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak AttacksCode0
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities—0
Meerkat: Audio-Visual Large Language Model for Grounding in Space and TimeCode1
Tree Search for Language Model AgentsCode3
RegMix: Data Mixture as Regression for Language Model Pre-trainingCode2
Revisiting Random Walks for Learning on GraphsCode1
ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization—0
Explaining Length Bias in LLM-Based Preference Evaluations—0
Needle in the Haystack for Memory Based Large Language Models—0
Optimization of Retrieval-Augmented Generation Context with Outlier Detection—0
IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script GenerationCode2
Explaining Chest X-ray Pathology Models using Textual Concepts—0
Scaling Technology Acceptance Analysis with Large Language Model (LLM) Annotation Systems—0
Characterizing Stereotypical Bias from Privacy-preserving Pre-Training—0
Learning Formal Mathematics From Intrinsic MotivationCode2
Financial Knowledge Large Language Model—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