Understanding Token Probability Encoding in Output Embeddings
Fuente:
arXiv
Saved in:
| Main Authors: | Cho, Hakaze, Sakai, Yoshihiro, Tanaka, Kenshiro, Kato, Mariko, Inoue, Naoya |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Token-based Decision Criteria Are Suboptimal in In-context Learning
by: Cho, Hakaze, et al.
Published: (2024)
by: Cho, Hakaze, et al.
Published: (2024)
Revisiting In-context Learning Inference Circuit in Large Language Models
by: Cho, Hakaze, et al.
Published: (2024)
by: Cho, Hakaze, et al.
Published: (2024)
Affinity and Diversity: A Unified Metric for Demonstration Selection via Internal Representations
by: Kato, Mariko, et al.
Published: (2025)
by: Kato, Mariko, et al.
Published: (2025)
Mechanistic Fine-tuning for In-context Learning
by: Cho, Hakaze, et al.
Published: (2025)
by: Cho, Hakaze, et al.
Published: (2025)
Mechanism of Task-oriented Information Removal in In-context Learning
by: Cho, Hakaze, et al.
Published: (2025)
by: Cho, Hakaze, et al.
Published: (2025)
StaICC: Standardized Evaluation for Classification Task in In-context Learning
by: Cho, Hakaze, et al.
Published: (2025)
by: Cho, Hakaze, et al.
Published: (2025)
Binary Autoencoder for Mechanistic Interpretability of Large Language Models
by: Cho, Hakaze, et al.
Published: (2025)
by: Cho, Hakaze, et al.
Published: (2025)
Measuring Intrinsic Dimension of Token Embeddings
by: Kataiwa, Takuya, et al.
Published: (2025)
by: Kataiwa, Takuya, et al.
Published: (2025)
NoisyICL: A Little Noise in Model Parameters Calibrates In-context Learning
by: Zhao, Yufeng, et al.
Published: (2024)
by: Zhao, Yufeng, et al.
Published: (2024)
The Transfer Neurons Hypothesis: An Underlying Mechanism for Language Latent Space Transitions in Multilingual LLMs
by: Tezuka, Hinata, et al.
Published: (2025)
by: Tezuka, Hinata, et al.
Published: (2025)
Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings
by: Kadhim, Ahmed K., et al.
Published: (2025)
by: Kadhim, Ahmed K., et al.
Published: (2025)
Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference
by: Zhao, Stephen, et al.
Published: (2025)
by: Zhao, Stephen, et al.
Published: (2025)
Output Embedding Centering for Stable LLM Pretraining
by: Stollenwerk, Felix, et al.
Published: (2026)
by: Stollenwerk, Felix, et al.
Published: (2026)
Do LLMs Encode Functional Importance of Reasoning Tokens?
by: Singh, Janvijay, et al.
Published: (2026)
by: Singh, Janvijay, et al.
Published: (2026)
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs
by: Yang, Zhihe, et al.
Published: (2025)
by: Yang, Zhihe, et al.
Published: (2025)
Struc-EMB: The Potential of Structure-Aware Encoding in Language Embeddings
by: Liu, Shikun, et al.
Published: (2025)
by: Liu, Shikun, et al.
Published: (2025)
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
by: Fadeeva, Ekaterina, et al.
Published: (2024)
by: Fadeeva, Ekaterina, et al.
Published: (2024)
Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization
by: Foroutan, Negar, et al.
Published: (2025)
by: Foroutan, Negar, et al.
Published: (2025)
Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation
by: Wang, Yiming, et al.
Published: (2024)
by: Wang, Yiming, et al.
Published: (2024)
Understanding LLM Embeddings for Regression
by: Tang, Eric, et al.
Published: (2024)
by: Tang, Eric, et al.
Published: (2024)
Understanding and Mitigating Tokenization Bias in Language Models
by: Phan, Buu, et al.
Published: (2024)
by: Phan, Buu, et al.
Published: (2024)
Concept Tokens: Learning Behavioral Embeddings Through Concept Definitions
by: Sastre, Ignacio, et al.
Published: (2026)
by: Sastre, Ignacio, et al.
Published: (2026)
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
by: Zhang, Zhongjian, et al.
Published: (2026)
by: Zhang, Zhongjian, et al.
Published: (2026)
Memory Tokens: Large Language Models Can Generate Reversible Sentence Embeddings
by: Sastre, Ignacio, et al.
Published: (2025)
by: Sastre, Ignacio, et al.
Published: (2025)
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
by: Wang, Duo, et al.
Published: (2024)
by: Wang, Duo, et al.
Published: (2024)
Localizing Task Recognition and Task Learning in In-Context Learning via Attention Head Analysis
by: Yang, Haolin, et al.
Published: (2025)
by: Yang, Haolin, et al.
Published: (2025)
TIDE: Every Layer Knows the Token Beneath the Context
by: Jaiswal, Ajay, et al.
Published: (2026)
by: Jaiswal, Ajay, et al.
Published: (2026)
T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings
by: Deiseroth, Björn, et al.
Published: (2024)
by: Deiseroth, Björn, et al.
Published: (2024)
LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference
by: Fu, Qichen, et al.
Published: (2024)
by: Fu, Qichen, et al.
Published: (2024)
SliceMoE: Routing Embedding Slices Instead of Tokens for Fine-Grained and Balanced Transformer Scaling
by: Vejendla, Harshil
Published: (2025)
by: Vejendla, Harshil
Published: (2025)
PHOTON: Hierarchical Autoregressive Modeling for Lightspeed and Memory-Efficient Language Generation
by: Ichikawa, Yuma, et al.
Published: (2025)
by: Ichikawa, Yuma, et al.
Published: (2025)
TokenButler: Token Importance is Predictable
by: Akhauri, Yash, et al.
Published: (2025)
by: Akhauri, Yash, et al.
Published: (2025)
One Swallow Does Not Make a Summer: Understanding Semantic Structures in Embedding Spaces
by: Sun, Yandong, et al.
Published: (2025)
by: Sun, Yandong, et al.
Published: (2025)
Group Representational Position Encoding
by: Zhang, Yifan, et al.
Published: (2025)
by: Zhang, Yifan, et al.
Published: (2025)
Lossless Token Sequence Compression via Meta-Tokens
by: Harvill, John, et al.
Published: (2025)
by: Harvill, John, et al.
Published: (2025)
SLOT: Structuring the Output of Large Language Models
by: Wang, Darren Yow-Bang, et al.
Published: (2025)
by: Wang, Darren Yow-Bang, et al.
Published: (2025)
Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach
by: Quevedo, Ernesto, et al.
Published: (2024)
by: Quevedo, Ernesto, et al.
Published: (2024)
Adversarial Tokenization
by: Geh, Renato Lui, et al.
Published: (2025)
by: Geh, Renato Lui, et al.
Published: (2025)
Asynchronous and Segmented Bidirectional Encoding for NMT
by: Yang, Jingpu, et al.
Published: (2024)
by: Yang, Jingpu, et al.
Published: (2024)
xVal: A Continuous Numerical Tokenization for Scientific Language Models
by: Golkar, Siavash, et al.
Published: (2023)
by: Golkar, Siavash, et al.
Published: (2023)
Similar Items
-
Token-based Decision Criteria Are Suboptimal in In-context Learning
by: Cho, Hakaze, et al.
Published: (2024) -
Revisiting In-context Learning Inference Circuit in Large Language Models
by: Cho, Hakaze, et al.
Published: (2024) -
Affinity and Diversity: A Unified Metric for Demonstration Selection via Internal Representations
by: Kato, Mariko, et al.
Published: (2025) -
Mechanistic Fine-tuning for In-context Learning
by: Cho, Hakaze, et al.
Published: (2025) -
Mechanism of Task-oriented Information Removal in In-context Learning
by: Cho, Hakaze, et al.
Published: (2025)