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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10551–10600 of 17610 papers

TitleStatusHype
Synslator: An Interactive Machine Translation Tool with Online Learning—0
Optimizing Large Language Models to Expedite the Development of Smart Contracts—0
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback—0
Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models—0
Question-focused Summarization by Decomposing Articles into Facts and Opinions and Retrieving Entities—0
Tree-GPT: Modular Large Language Model Expert System for Forest Remote Sensing Image Understanding and Interactive Analysis—0
ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations—0
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity—0
From task structures to world models: What do LLMs know?—0
Functional Interpolation for Relative Positions Improves Long Context Transformers—0
Lending Interaction Wings to Recommender Systems with Conversational Agents—0
Understanding prompt engineering may not require rethinking generalization—0
Keyword Augmented Retrieval: Novel framework for Information Retrieval integrated with speech interface—0
Quantized Transformer Language Model Implementations on Edge Devices—0
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction—0
Neural Language Model Pruning for Automatic Speech Recognition—0
TRAM: Bridging Trust Regions and Sharpness Aware MinimizationCode0
Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity—0
A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions—0
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards—0
Discovering Knowledge-Critical Subnetworks in Pretrained Language ModelsCode0
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference—0
Learning to Prompt Your Domain for Vision-Language Models—0
Heterogeneous Federated Learning Using Knowledge Codistillation—0
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models—0
Zero Resource Code-switched Speech Benchmark Using Speech Utterance Pairs For Multiple Spoken LanguagesCode0
Who's Harry Potter? Approximate Unlearning in LLMs—0
An evolutionary model of personality traits related to cooperative behavior using a large language model—0
HPC-GPT: Integrating Large Language Model for High-Performance Computing—0
Can a student Large Language Model perform as well as it's teacher?—0
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context RetrievalCode0
TWIZ-v2: The Wizard of Multimodal Conversational-Stimulus—0
Dodo: Dynamic Contextual Compression for Decoder-only LMs—0
Tuning Large language model for End-to-end Speech Translation—0
Nugget: Neural Agglomerative Embeddings of TextCode0
Syllable-level lyrics generation from melody exploiting character-level language model—0
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model—0
PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels—0
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models—0
Language Model Decoding as Direct Metrics Optimization—0
Improving Emotional Expression and Cohesion in Image-Based Playlist Description and Music Topics: A Continuous Parameterization Approach—0
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models—0
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model—0
Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals—0
Adaptive-Solver Framework for Dynamic Strategy Selection in Large Language Model ReasoningCode0
Comics for Everyone: Generating Accessible Text Descriptions for Comic Strips—0
Source Attribution for Large Language Model-Generated Data—0
Meta Semantic Template for Evaluation of Large Language Models—0
Parameter-Efficient Tuning Helps Language Model Alignment—0
SELF: Self-Evolution with Language Feedback—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