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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7851–7900 of 17610 papers

TitleStatusHype
Language Alignment via Nash-learning and Adaptive feedback—0
Reading Is Believing: Revisiting Language Bottleneck Models for Image Classification—0
Teaching LLMs to Abstain across Languages via Multilingual FeedbackCode0
video-SALMONN: Speech-Enhanced Audio-Visual Large Language ModelsCode0
CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans—0
Automated radiotherapy treatment planning guided by GPT-4Vision—0
Brain-Like Language Processing via a Shallow Untrained Multihead Attention NetworkCode0
Inferring Pluggable Types with Machine Learning—0
A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative GenerationCode0
Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation—0
GiusBERTo: A Legal Language Model for Personal Data De-identification in Italian Court of Auditors Decisions—0
LLM2FEA: Discover Novel Designs with Generative Evolutionary Multitasking—0
Open-Vocabulary Temporal Action Localization using Multimodal Guidance—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
Unsupervised Morphological Tree Tokenizer—0
Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems—0
HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation—0
Enhancing the LLM-Based Robot Manipulation Through Human-Robot Collaboration—0
A Learn-Then-Reason Model Towards Generalization in Knowledge Base Question Answering—0
A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes—0
Information Guided Regularization for Fine-tuning Language ModelsCode0
AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework—0
Healing Powers of BERT: How Task-Specific Fine-Tuning Recovers Corrupted Language Models—0
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model EvaluationCode0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Factual Dialogue Summarization via Learning from Large Language Models—0
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas—0
How Many Parameters Does it Take to Change a Light Bulb? Evaluating Performance in Self-Play of Conversational Games as a Function of Model Characteristics—0
CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM PipelinesCode0
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking—0
Advantage Alignment Algorithms—0
Exploring Spatial Representations in the Historical Lake District Texts with LLM-based Relation Extraction—0
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods—0
Revealing Vision-Language Integration in the Brain with Multimodal NetworksCode0
ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation—0
MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate—0
SPL: A Socratic Playground for Learning Powered by Large Language Model—0
Measuring Sample Importance in Data Pruning for Language Models based on Information Entropy—0
Ranking LLMs by compression—0
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning—0
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages—0
The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It—0
Transferable speech-to-text large language model alignment module—0
Large Language Models are Biased Because They Are Large Language Models—0
Towards Holistic Language-video Representation: the language model-enhanced MSR-Video to Text Dataset—0
PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation ModelCode0
LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application—0
VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework—0
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia—0
Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning—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