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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 2301–2350 of 17610 papers

TitleStatusHype
LMM-Regularized CLIP Embeddings for Image Classification—0
OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews—0
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents—0
Embodied CoT Distillation From LLM To Off-the-shelf AgentsCode3
LLMs Can Simulate Standardized Patients via Agent CoevolutionCode1
MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond—0
Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach—0
OmniVLM: A Token-Compressed, Sub-Billion-Parameter Vision-Language Model for Efficient On-Device Inference—0
SepLLM: Accelerate Large Language Models by Compressing One Segment into One SeparatorCode4
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Embracing Large Language Models in Traffic Flow Forecasting—0
Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization—0
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts—0
Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
Learning to Verify Summary Facts with Fine-Grained LLM FeedbackCode0
WEPO: Web Element Preference Optimization for LLM-based Web Navigation—0
Human-Centric NLP or AI-Centric Illusion?: A Critical Investigation—0
Large Language Models for Medical Forecasting -- Foresight 2—0
Superhuman performance of a large language model on the reasoning tasks of a physician—0
Inference Scaling for Bridging Retrieval and Augmented Generation—0
Optimizing Vision-Language Interactions Through Decoder-Only Models—0
Bridging Vision and Language: Modeling Causality and Temporality in Video Narratives—0
EVLM: Self-Reflective Multimodal Reasoning for Cross-Dimensional Visual Editing—0
Solving the Inverse Alignment Problem for Efficient RLHF—0
WHAT-IF: Exploring Branching Narratives by Meta-Prompting Large Language Models—0
WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language ModelCode1
B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal TokensCode0
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial InjectionCode1
Small Language Model as Data Prospector for Large Language Model—0
PickLLM: Context-Aware RL-Assisted Large Language Model Routing—0
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks—0
CareBot: A Pioneering Full-Process Open-Source Medical Language Model—0
Regulation of Language Models With Interpretability Will Likely Result In A Performance Trade-OffCode0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials—0
Dialogue Language Model with Large-Scale Persona Data Engineering—0
Language model driven: a PROTAC generation pipeline with dual constraints of structure and property—0
Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model—0
SPRec: Leveraging Self-Play to Debias Preference Alignment for Large Language Model-based RecommendationsCode1
Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering—0
Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis—0
Towards a Multimodal Large Language Model with Pixel-Level Insight for BiomedicineCode2
When Text Embedding Meets Large Language Model: A Comprehensive Survey—0
Phi-4 Technical ReportCode2
Learning Novel Skills from Language-Generated Demonstrations—0
Predicting Human Brain States with TransformerCode2
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework—0
LatentQA: Teaching LLMs to Decode Activations Into Natural Language—0
Large Concept Models: Language Modeling in a Sentence Representation SpaceCode7
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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