LLM In-Context Recall is Prompt Dependent
Fuente:
arXiv
Saved in:
| Main Authors: | Machlab, Daniel, Battle, Rick |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Unreasonable Effectiveness of Eccentric Automatic Prompts
by: Battle, Rick, et al.
Published: (2024)
by: Battle, Rick, et al.
Published: (2024)
Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives
by: Hagos, Desta Haileselassie, et al.
Published: (2024)
by: Hagos, Desta Haileselassie, et al.
Published: (2024)
Understanding Contextual Recall in Transformers: How Finetuning Enables In-Context Reasoning over Pretraining Knowledge
by: Vasudeva, Bhavya, et al.
Published: (2026)
by: Vasudeva, Bhavya, et al.
Published: (2026)
Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference
by: Liskavets, Barys, et al.
Published: (2024)
by: Liskavets, Barys, et al.
Published: (2024)
ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines
by: Zhao, Minjun, et al.
Published: (2025)
by: Zhao, Minjun, et al.
Published: (2025)
Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Reasoning
by: Štorek, Adam, et al.
Published: (2025)
by: Štorek, Adam, et al.
Published: (2025)
Prompt Optimization via Adversarial In-Context Learning
by: Do, Xuan Long, et al.
Published: (2023)
by: Do, Xuan Long, et al.
Published: (2023)
Exploring Precision and Recall to assess the quality and diversity of LLMs
by: Bronnec, Florian Le, et al.
Published: (2024)
by: Bronnec, Florian Le, et al.
Published: (2024)
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
by: Cho, Youngjae, et al.
Published: (2024)
by: Cho, Youngjae, et al.
Published: (2024)
Prompt-Dependent Ranking of Large Language Models with Uncertainty Quantification
by: Menendez, Angel Rodrigo Avelar, et al.
Published: (2026)
by: Menendez, Angel Rodrigo Avelar, et al.
Published: (2026)
Characterizing Prompt Compression Methods for Long Context Inference
by: Jha, Siddharth, et al.
Published: (2024)
by: Jha, Siddharth, et al.
Published: (2024)
LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
by: Wu, Yuanchen, et al.
Published: (2025)
by: Wu, Yuanchen, et al.
Published: (2025)
Mimetic Initialization Helps State Space Models Learn to Recall
by: Trockman, Asher, et al.
Published: (2024)
by: Trockman, Asher, et al.
Published: (2024)
Extracting Prompts by Inverting LLM Outputs
by: Zhang, Collin, et al.
Published: (2024)
by: Zhang, Collin, et al.
Published: (2024)
Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs
by: Chughtai, Bilal, et al.
Published: (2024)
by: Chughtai, Bilal, et al.
Published: (2024)
Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
by: Zheng, Junhao, et al.
Published: (2023)
by: Zheng, Junhao, et al.
Published: (2023)
Dialectical Behavior Therapy Approach to LLM Prompting
by: Vitman, Oxana, et al.
Published: (2024)
by: Vitman, Oxana, et al.
Published: (2024)
Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall
by: Wang, Qianli, et al.
Published: (2025)
by: Wang, Qianli, et al.
Published: (2025)
Prompt Curriculum Learning for Efficient LLM Post-Training
by: Gao, Zhaolin, et al.
Published: (2025)
by: Gao, Zhaolin, et al.
Published: (2025)
Learning to Translate from Soft to Hard LLM Prompts
by: Kongsomjit, Pitipat, et al.
Published: (2026)
by: Kongsomjit, Pitipat, et al.
Published: (2026)
ADAPT: Hybrid Prompt Optimization for LLM Feature Visualization
by: Cardoso, João N., et al.
Published: (2026)
by: Cardoso, João N., et al.
Published: (2026)
LLMSteer: Improving Long-Context LLM Inference by Steering Attention on Reused Contexts
by: Gu, Zhuohan, et al.
Published: (2024)
by: Gu, Zhuohan, et al.
Published: (2024)
SkillAggregation: Reference-free LLM-Dependent Aggregation
by: Sun, Guangzhi, et al.
Published: (2024)
by: Sun, Guangzhi, et al.
Published: (2024)
Beyond the Prompt in Large Language Models: Comprehension, In-Context Learning, and Chain-of-Thought
by: Jiao, Yuling, et al.
Published: (2026)
by: Jiao, Yuling, et al.
Published: (2026)
Layerwise Recall and the Geometry of Interwoven Knowledge in LLMs
by: Lei, Ge, et al.
Published: (2025)
by: Lei, Ge, et al.
Published: (2025)
Compress the Context, Keep the Commitments: A Formal Framework for Verifiable LLM Context Compression
by: Trukhina, Natalia, et al.
Published: (2026)
by: Trukhina, Natalia, et al.
Published: (2026)
Does Prompt Formatting Have Any Impact on LLM Performance?
by: He, Jia, et al.
Published: (2024)
by: He, Jia, et al.
Published: (2024)
ProCut: LLM Prompt Compression via Attribution Estimation
by: Xu, Zhentao, et al.
Published: (2025)
by: Xu, Zhentao, et al.
Published: (2025)
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding
by: Fu, Yichao, et al.
Published: (2024)
by: Fu, Yichao, et al.
Published: (2024)
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
by: Jiang, Huiqiang, et al.
Published: (2023)
by: Jiang, Huiqiang, et al.
Published: (2023)
A Novel Spinor-Based Embedding Model for Transformers
by: White, Rick
Published: (2024)
by: White, Rick
Published: (2024)
Understanding Factual Recall in Transformers via Associative Memories
by: Nichani, Eshaan, et al.
Published: (2024)
by: Nichani, Eshaan, et al.
Published: (2024)
LongRecall: A Structured Approach for Robust Recall Evaluation in Long-Form Text
by: Ardestani, MohamamdJavad, et al.
Published: (2025)
by: Ardestani, MohamamdJavad, et al.
Published: (2025)
Context Dependence and Reliability in Autoregressive Language Models
by: Sengupta, Poushali, et al.
Published: (2026)
by: Sengupta, Poushali, et al.
Published: (2026)
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
by: Ma, Xuezhe, et al.
Published: (2024)
by: Ma, Xuezhe, et al.
Published: (2024)
Scaling Long-Horizon LLM Agent via Context-Folding
by: Sun, Weiwei, et al.
Published: (2025)
by: Sun, Weiwei, et al.
Published: (2025)
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation
by: He, Xinrui, et al.
Published: (2024)
by: He, Xinrui, et al.
Published: (2024)
Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference
by: Chen, Hao Mark, et al.
Published: (2024)
by: Chen, Hao Mark, et al.
Published: (2024)
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
by: Sun, Hao, et al.
Published: (2023)
by: Sun, Hao, et al.
Published: (2023)
Universal In-Context Approximation By Prompting Fully Recurrent Models
by: Petrov, Aleksandar, et al.
Published: (2024)
by: Petrov, Aleksandar, et al.
Published: (2024)
Similar Items
-
The Unreasonable Effectiveness of Eccentric Automatic Prompts
by: Battle, Rick, et al.
Published: (2024) -
Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives
by: Hagos, Desta Haileselassie, et al.
Published: (2024) -
Understanding Contextual Recall in Transformers: How Finetuning Enables In-Context Reasoning over Pretraining Knowledge
by: Vasudeva, Bhavya, et al.
Published: (2026) -
Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference
by: Liskavets, Barys, et al.
Published: (2024) -
ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines
by: Zhao, Minjun, et al.
Published: (2025)