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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7676–7700 of 17610 papers

TitleStatusHype
On the Power of Convolution Augmented Transformer—0
Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course—0
Biomedical Nested NER with Large Language Model and UMLS Heuristics—0
Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System ResponsesCode0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
AI Safety in Generative AI Large Language Models: A Survey—0
Leveraging Task-Specific Knowledge from LLM for Semi-Supervised 3D Medical Image Segmentation—0
SHINE: Saliency-aware HIerarchical NEgative Ranking for Compositional Temporal GroundingCode0
Romanization Encoding For Multilingual ASR—0
Testing learning hypotheses using neural networks by manipulating learning data—0
Statistical investigations into the geometry and homology of random programs—0
Spontaneous Reward Hacking in Iterative Self-Refinement—0
Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR—0
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition—0
MobileFlow: A Multimodal LLM For Mobile GUI Agent—0
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter—0
PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit PostsCode0
Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models—0
Written Term Detection Improves Spoken Term DetectionCode0
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context—0
Efficient Controlled Language Generation with Low-Rank Autoregressive Reward Models—0
Aligning Model Evaluations with Human Preferences: Mitigating Token Count Bias in Language Model Assessments—0
ConText at WASSA 2024 Empathy and Personality Shared Task: History-Dependent Embedding Utterance Representations for Empathy and Emotion Prediction in Conversations—0
Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant ContentCode0
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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