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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8151–8200 of 17610 papers

TitleStatusHype
Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing—0
Why Would You Suggest That? Human Trust in Language Model Responses—0
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding—0
Towards Harnessing Large Language Models for Comprehension of Conversational GroundingCode0
Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost—0
OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models—0
Scalable Ensembling For Mitigating Reward Overoptimisation—0
SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models—0
Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling—0
Large Language Model Assisted Optimal Bidding of BESS in FCAS Market: An AI-agent based Approach—0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study—0
Understanding Token Probability Encoding in Output Embeddings—0
LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning—0
SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information—0
Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository ExplorationCode0
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients—0
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs—0
ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models—0
HBTP: Heuristic Behavior Tree Planning with Large Language Model ReasoningCode0
Harnessing Business and Media Insights with Large Language Models—0
Distortion-free Watermarks are not Truly Distortion-free under Watermark Key Collisions—0
FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models—0
LongSkywork: A Training Recipe for Efficiently Extending Context Length in Large Language Models—0
Towards a copilot in BIM authoring tool using a large language model-based agent for intelligent human-machine interaction—0
KGLink: A column type annotation method that combines knowledge graph and pre-trained language modelCode0
On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots—0
Large Language Model Confidence Estimation via Black-Box Access—0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning—0
Controlling Large Language Model Agents with Entropic Activation Steering—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores—0
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF—0
DYNA: Disease-Specific Language Model for Variant Pathogenicity—0
StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond—0
LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based RankingCode0
RAG Does Not Work for Enterprises—0
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling—0
LOLAMEME: Logic, Language, Memory, Mechanistic Framework—0
Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model AlignmentCode0
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis—0
You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet—0
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual InstructionsCode0
Who Writes the Review, Human or AI?—0
Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference HeadsCode0
Efficient Indirect LLM Jailbreak via Multimodal-LLM Jailbreak—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Automated Generation and Tagging of Knowledge Components from Multiple-Choice QuestionsCode0
Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models—0
Evaluating Large Language Model Biases in Persona-Steered GenerationCode0
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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