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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8001–8050 of 17610 papers

TitleStatusHype
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios—0
Towards Open-Ended Visual Recognition with Large Language ModelCode1
REST: Retrieval-Based Speculative DecodingCode2
Anti-LM Decoding for Zero-shot In-context Machine TranslationCode0
LLatrieval: LLM-Verified Retrieval for Verifiable GenerationCode1
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledgeCode1
Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image RetrievalCode0
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference—0
On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models—0
Reducing the Need for Backpropagation and Discovering Better Optima With Explicit Optimizations of Neural Networks—0
Controlled Text Generation for Black-box Language Models via Score-based Progressive EditorCode0
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising—0
Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions—0
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games—0
On Elastic Language Models—0
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings—0
On the Discussion of Large Language Models: Symmetry of Agents and Interplay with Prompts—0
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided SearchCode0
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language ModelsCode1
The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4—0
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language ModelsCode4
To Tell The Truth: Language of Deception and Language ModelsCode0
Towards the Law of Capacity Gap in Distilling Language ModelsCode1
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning—0
An Analysis and Mitigation of the Reversal CurseCode1
ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models—0
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect—0
Tunable Soft Prompts are Messengers in Federated Learning—0
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models—0
Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding—0
Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model—0
Intentional Biases in LLM Responses—0
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models—0
Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study—0
Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in textCode0
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System—0
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image ClassificationCode1
Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit LogsCode0
How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Model—0
Heaps' Law in GPT-Neo Large Language Model Emulated CorporaCode0
Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking—0
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related TasksCode0
Model-as-a-Service (MaaS): A Survey—0
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense RetrievalCode1
Language Models can be Logical Solvers—0
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human PreferencesCode1
AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems—0
ChatGPT in the context of precision agriculture data analyticsCode0
Argumentation Element Annotation Modeling using XLNet—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