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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8251–8300 of 17610 papers

TitleStatusHype
SelfCP: Compressing Over-Limit Prompt via the Frozen Large Language Model Itself—0
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models—0
The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model—0
Salutary Labeling with Zero Human Annotation—0
Matryoshka Multimodal Models—0
The Expressive Capacity of State Space Models: A Formal Language Perspective—0
Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation—0
TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion DetectionCode0
Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search—0
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions—0
Planning with Multi-Constraints via Collaborative Language AgentsCode0
Mixture of Latent Experts Using Tensor Products—0
Towards Multi-Task Multi-Modal Models: A Video Generative Perspective—0
Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models—0
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
Disentangling and Integrating Relational and Sensory Information in Transformer ArchitecturesCode0
DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic SpeakersCode0
Chain of Tools: Large Language Model is an Automatic Multi-tool Learner—0
Intruding with Words: Towards Understanding Graph Injection Attacks at the Text LevelCode0
How Well Do Deep Learning Models Capture Human Concepts? The Case of the Typicality Effect—0
A transfer learning framework for weak-to-strong generalization—0
C3LLM: Conditional Multimodal Content Generation Using Large Language Models—0
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of ExemplarsCode0
Revisit, Extend, and Enhance Hessian-Free Influence Functions—0
Semantic Importance-Aware Communications with Semantic Correction Using Large Language Models—0
Theoretical Analysis of Weak-to-Strong Generalization—0
Large Language Model Sentinel: LLM Agent for Adversarial Purification—0
SEP: Self-Enhanced Prompt Tuning for Visual-Language ModelCode0
Large Language Model Pruning—0
Scaling Laws for Discriminative Classification in Large Language Models—0
Off-the-shelf ChatGPT is a Good Few-shot Human Motion Predictor—0
RAEE: A Robust Retrieval-Augmented Early Exiting Framework for Efficient Inference—0
NuwaTS: a Foundation Model Mending Every Incomplete Time Series—0
Large Language Model (LLM) for Standard Cell Layout Design Optimization—0
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems—0
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs—0
V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLMCode0
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation—0
Emergence of a High-Dimensional Abstraction Phase in Language TransformersCode0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection—0
iREPO: implicit Reward Pairwise Difference based Empirical Preference Optimization—0
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMsCode0
GECKO: Generative Language Model for English, Code and Korean—0
Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning—0
Enhancing Augmentative and Alternative Communication with Card Prediction and Colourful Semantics—0
BiMix: A Bivariate Data Mixing Law for Language Model Pretraining—0
Efficient Medical Question Answering with Knowledge-Augmented Question GenerationCode0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
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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