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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 18011850 of 17610 papers

TitleStatusHype
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
CriticEval: Evaluating Large Language Model as CriticCode1
Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language ModelsCode1
Automated Spinal MRI Labelling from Reports Using a Large Language ModelCode1
ABNIRML: Analyzing the Behavior of Neural IR ModelsCode1
Discrete Flows: Invertible Generative Models of Discrete DataCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Dissecting Human and LLM PreferencesCode1
DISP-LLM: Dimension-Independent Structural Pruning for Large Language ModelsCode1
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and AttributionCode1
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot LearnersCode1
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-TrainingCode1
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue SystemsCode1
Guiding Attention for Self-Supervised Learning with TransformersCode1
Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry WritingCode1
Distillation and Refinement of Reasoning in Small Language Models for Document Re-rankingCode1
Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language ModelCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
AdaSplash: Adaptive Sparse Flash AttentionCode1
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language CorrectionsCode1
Distilling DETR with Visual-Linguistic Knowledge for Open-Vocabulary Object DetectionCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation ModelsCode1
Distilling the Knowledge of BERT for Sequence-to-Sequence ASRCode1
Grounded Multi-Hop VideoQA in Long-Form Egocentric VideosCode1
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningCode1
AMPERSAND: Argument Mining for PERSuAsive oNline DiscussionsCode1
Invariant Language ModelingCode1
Distributed Speculative Inference (DSI): Speculation Parallelism for Provably Faster Lossless Language Model InferenceCode1
Distributed Deep Learning in Open CollaborationsCode1
AMR Parsing via Graph-Sequence Iterative InferenceCode1
Automatic Controllable Product Copywriting for E-CommerceCode1
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing ConstraintsCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical ReasoningCode1
Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device AgentsCode1
DivScene: Benchmarking LVLMs for Object Navigation with Diverse Scenes and ObjectsCode1
dMel: Speech Tokenization made SimpleCode1
Grounded Compositional Outputs for Adaptive Language ModelingCode1
Grounding Language Models for Visual Entity RecognitionCode1
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense RetrievalCode1
Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption ContestCode1
DOBF: A Deobfuscation Pre-Training Objective for Programming LanguagesCode1
Automatic Evaluation of Attribution by Large Language ModelsCode1
A Surprisingly Robust Trick for Winograd Schema ChallengeCode1
Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3Code1
CrAM: A Compression-Aware MinimizerCode1
DocSCAN: Unsupervised Text Classification via Learning from NeighborsCode1
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized EncodingCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified