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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4751–4800 of 17610 papers

TitleStatusHype
Understanding and Mitigating Tokenization Bias in Language Models—0
First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model ReasoningCode0
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods—0
MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries?—0
video-SALMONN: Speech-Enhanced Audio-Visual Large Language ModelsCode0
MR-MLLM: Mutual Reinforcement of Multimodal Comprehension and Vision Perception—0
Language Alignment via Nash-learning and Adaptive feedback—0
EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and VotingCode2
CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans—0
TacoLM: GaTed Attention Equipped Codec Language Model are Efficient Zero-Shot Text to Speech SynthesizersCode1
Teaching LLMs to Abstain across Languages via Multilingual FeedbackCode0
Reading Is Believing: Revisiting Language Bottleneck Models for Image Classification—0
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis—0
Inferring Pluggable Types with Machine Learning—0
Automated radiotherapy treatment planning guided by GPT-4Vision—0
Open-Vocabulary Temporal Action Localization using Multimodal Guidance—0
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship EmbeddingsCode1
FIRST: Faster Improved Listwise Reranking with Single Token DecodingCode2
InternLM-Law: An Open Source Chinese Legal Large Language ModelCode1
Autonomous Agents for Collaborative Task under Information AsymmetryCode14
A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative GenerationCode0
LLM2FEA: Discover Novel Designs with Generative Evolutionary Multitasking—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
Brain-Like Language Processing via a Shallow Untrained Multihead Attention NetworkCode0
Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation—0
Safely Learning with Private Data: A Federated Learning Framework for Large Language ModelCode1
GiusBERTo: A Legal Language Model for Personal Data De-identification in Italian Court of Auditors Decisions—0
Unsupervised Morphological Tree Tokenizer—0
MoA: Mixture of Sparse Attention for Automatic Large Language Model CompressionCode2
CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM PipelinesCode0
LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path PlanningCode2
SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal BehaviorsCode1
HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation—0
Factual Dialogue Summarization via Learning from Large Language Models—0
MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate—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
Advantage Alignment Algorithms—0
SPL: A Socratic Playground for Learning Powered by Large Language Model—0
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model EvaluationCode0
Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems—0
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods—0
Asynchronous Large Language Model Enhanced Planner for Autonomous DrivingCode2
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMsCode1
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas—0
Healing Powers of BERT: How Task-Specific Fine-Tuning Recovers Corrupted Language Models—0
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning—0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Measuring Sample Importance in Data Pruning for Language Models based on Information Entropy—0
Ranking LLMs by compression—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