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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9701–9750 of 17610 papers

TitleStatusHype
Language Models Implement Simple Word2Vec-style Vector ArithmeticCode1
The False Promise of Imitating Proprietary LLMsCode3
Masked and Permuted Implicit Context Learning for Scene Text RecognitionCode0
RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting—0
ChatBridge: Bridging Modalities with Large Language Model as a Language CatalystCode1
Improving Scheduled Sampling for Neural Transducer-based ASR—0
GenerateCT: Text-Conditional Generation of 3D Chest CT VolumesCode1
BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model—0
VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation—0
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text TranslationCode1
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought—0
Lexinvariant Language Models—0
Think Before You Act: Decision Transformers with Working MemoryCode1
Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language ModelsCode1
Large Language Models are Few-Shot Health Learners—0
SPRING: Studying the Paper and Reasoning to Play GamesCode1
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning—0
PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of PathologyCode1
Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization—0
MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop QuestionsCode1
Getting MoRE out of Mixture of Language Model Reasoning Experts—0
Meta-Learning Online Adaptation of Language ModelsCode1
Neural Summarization of Electronic Health Records—0
AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language ModelsCode0
Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLMCode0
LLMDet: A Third Party Large Language Models Generated Text Detection ToolCode1
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language ModelsCode1
PIVOINE: Instruction Tuning for Open-world Information ExtractionCode1
Alt-Text with Context: Improving Accessibility for Images on Twitter—0
This Land is Your, My Land: Evaluating Geopolitical Biases in Language ModelsCode0
Reasoning with Language Model is Planning with World ModelCode4
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image ManipulationCode0
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions—0
Self-Evolution Learning for Discriminative Language Model PretrainingCode0
Just CHOP: Embarrassingly Simple LLM Compression—0
Drafting Event Schemas using Language Models—0
Gorilla: Large Language Model Connected with Massive APIsCode6
Estimating Large Language Model Capabilities without Labeled Test DataCode0
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuningCode1
HuatuoGPT, towards Taming Language Model to Be a DoctorCode3
Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions—0
Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language ModelsCode1
Adapting Language Models to Compress ContextsCode2
ClusterLLM: Large Language Models as a Guide for Text ClusteringCode1
In-Context Demonstration Selection with Cross Entropy Difference—0
Allies: Prompting Large Language Model with Beam Search—0
ExpertPrompting: Instructing Large Language Models to be Distinguished ExpertsCode2
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
Emergent inabilities? Inverse scaling over the course of pretraining—0
Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering—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