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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4451–4500 of 17610 papers

TitleStatusHype
ConditionNET: Learning Preconditions and Effects for Execution Monitoring—0
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT—0
Confabulation: The Surprising Value of Large Language Model Hallucinations—0
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation—0
Confidence-based Rewriting of Machine Translation Output—0
Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges—0
Confident Adaptive Language Modeling—0
Confidential Computing on NVIDIA Hopper GPUs: A Performance Benchmark Study—0
ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining—0
CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling—0
Conformal Linguistic Calibration: Trading-off between Factuality and Specificity—0
Conformal Tail Risk Control for Large Language Model Alignment—0
Conformer LLMs -- Convolution Augmented Large Language Models—0
ConfusedPilot: Confused Deputy Risks in RAG-based LLMs—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
CoNLL 2014 Shared Task: Grammatical Error Correction with a Syntactic N-gram Language Model from a Big Corpora—0
Connecting and Comparing Language Model Interpolation Techniques—0
Connecting Language and Vision to Actions—0
Connecting Large Language Model Agent to High Performance Computing Resource—0
Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guide—0
Connecting Speech Encoder and Large Language Model for ASR—0
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling—0
Connecting the Dots: Towards Human-Level Grammatical Error Correction—0
ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation—0
ConServe: Harvesting GPUs for Low-Latency and High-Throughput Large Language Model Serving—0
Consistency Analysis of ChatGPT—0
Consistency and Coherency Enhanced Story Generation—0
Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering—0
Consistency Checks for Language Model Forecasters—0
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles—0
Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy—0
Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs—0
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings—0
Guiding Large Language Models to Generate Computer-Parsable Content—0
Constraints First: A New MDD-based Model to Generate Sentences Under Constraints—0
Construct a Sentence with Multiple Specified Words—0
AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering—0
Filtering Discomforting Recommendations with Large Language Models—0
Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks—0
Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model—0
Construction contract risk identification based on knowledge-augmented language model—0
Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training—0
Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model—0
Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing—0
CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection—0
Contamination Report for Multilingual Benchmarks—0
CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models—0
ConText at WASSA 2024 Empathy and Personality Shared Task: History-Dependent Embedding Utterance Representations for Empathy and Emotion Prediction in Conversations—0
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
Context-Aware Clustering using Large Language Models—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