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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 3201–3250 of 17610 papers

TitleStatusHype
Can a large language model be a gaslighter?Code0
uto\!L: Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks—0
Preferential Normalizing Flows—0
Baichuan-Omni Technical ReportCode3
Zeroth-Order Fine-Tuning of LLMs in Random SubspacesCode1
Distributionally robust self-supervised learning for tabular dataCode0
Emergent social conventions and collective bias in LLM populations—0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective—0
Simultaneous Reward Distillation and Preference Learning: Get You a Language Model Who Can Do Both—0
Retraining-Free Merging of Sparse MoE via Hierarchical ClusteringCode1
Generation with Dynamic VocabularyCode0
SocialGaze: Improving the Integration of Human Social Norms in Large Language ModelsCode0
Lifelong Event Detection via Optimal Transport—0
VLM See, Robot Do: Human Demo Video to Robot Action Plan via Vision Language Model—0
MedMobile: A mobile-sized language model with expert-level clinical capabilitiesCode0
Hespi: A pipeline for automatically detecting information from hebarium specimen sheetsCode1
Parameter-Efficient Fine-Tuning of State Space ModelsCode1
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation—0
PEAR: A Robust and Flexible Automation Framework for Ptychography Enabled by Multiple Large Language Model AgentsCode1
Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference—0
SimpleStrat: Diversifying Language Model Generation with Stratification—0
Calibrated Cache Model for Few-Shot Vision-Language Model Adaptation—0
Do Unlearning Methods Remove Information from Language Model Weights?Code1
Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning—0
PoisonBench: Assessing Large Language Model Vulnerability to Data PoisoningCode1
Animating the Past: Reconstruct Trilobite via Video Generation—0
The Large Language Model GreekLegalRoBERTa—0
A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts—0
HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation PredictionCode0
Uncovering Overfitting in Large Language Model Editing—0
Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical ReasoningCode1
Mechanistic Permutability: Match Features Across Layers—0
Multi-Agent Collaborative Data Selection for Efficient LLM PretrainingCode1
Semantic Self-Consistency: Enhancing Language Model Reasoning via Semantic Weighting—0
Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art—0
AgroGPT: Efficient Agricultural Vision-Language Model with Expert TuningCode1
Bilinear MLPs enable weight-based mechanistic interpretabilityCode1
LecPrompt: A Prompt-based Approach for Logical Error Correction with CodeBERT—0
Language model developers should report train-test overlap—0
OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model PromptingCode1
TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked TextCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
Evolutionary Contrastive Distillation for Language Model Alignment—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Q-VLM: Post-training Quantization for Large Vision-Language ModelsCode2
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models—0
Plug-and-Play Performance Estimation for LLM Services without Relying on Labeled DataCode0
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models—0
OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring ModelingCode2
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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