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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7001–7050 of 17610 papers

TitleStatusHype
CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs—0
Catching the Common Cause: Extraction and Annotation of Causal Relations and their Participants—0
Positional Artefacts Propagate Through Masked Language Model Embeddings—0
Cat, Rat, Meow: On the Alignment of Language Model and Human Term-Similarity Judgments—0
Causal Distillation for Language Models—0
Causal Distillation: Transferring Structured Explanations from Large to Compact Language Models—0
Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing—0
Causal Inference with Large Language Model: A Survey—0
Causal Language Model for Zero-shot Constrained Keyphrase Generation—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Data Augmentations for Improved (Large) Language Model Generalization—0
caWaC -- A web corpus of Catalan and its application to language modeling and machine translation—0
CBAG: Conditional Biomedical Abstract Generation—0
CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering—0
FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking—0
Factored Agents: Decoupling In-Context Learning and Memorization for Robust Tool Use—0
Factored Language Model based on Recurrent Neural Network—0
Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge—0
FACTTRACK: Time-Aware World State Tracking in Story Outlines—0
Factual and Personalized Recommendations using Language Models and Reinforcement Learning—0
Factual Consistency Oriented Speech Recognition—0
Factual Dialogue Summarization via Learning from Large Language Models—0
A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System—0
FairHome: A Fair Housing and Fair Lending Dataset—0
Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation—0
Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling—0
FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large Language Model-Assisted Detection and Attribute Rebalancing—0
FairyLandAI: Personalized Fairy Tales utilizing ChatGPT and DALLE-3—0
Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals—0
Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph—0
Fake News Detection and Manipulation Reasoning via Large Vision-Language Models—0
FakeReasoning: Towards Generalizable Forgery Detection and Reasoning—0
Fake Sentence Detection as a Training Task for Sentence Encoding—0
Fake Sentence Detection as a Training Task for Sentence Encoding—0
Falcon2-11B Technical Report—0
FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal Unalignment for Large Language Model—0
Falcon Mamba: The First Competitive Attention-free 7B Language Model—0
FANAL -- Financial Activity News Alerting Language Modeling Framework—0
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking—0
ReAttention: Training-Free Infinite Context with Finite Attention Scope—0
FARM: Functional Group-Aware Representations for Small Molecules—0
FarSSiBERT: A Novel Transformer-based Model for Semantic Similarity Measurement of Persian Social Networks Informal Texts—0
Farzi Data: Autoregressive Data Distillation—0
FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Model—0
FAS-LLM: Large Language Model-Based Channel Prediction for OTFS-Enabled Satellite-FAS Links—0
FASST: Fast LLM-based Simultaneous Speech Translation—0
FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity—0
Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models—0
Fast and Accurate Preordering for SMT using Neural Networks—0
Fast and Memory-Efficient Neural Code Completion—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