GRIP uses contrastive training on LMM feedback to retrieve beneficial in-context examples for multimodal tasks, outperforming similarity-based methods and transferring across models including GPT-4o.
Learning to retrieve in-context examples for large language models
9 Pith papers cite this work, alongside 13 external citations. Polarity classification is still indexing.
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DIN-Retrieval uses domain-invariant neuron representations to retrieve cross-domain demonstrations, achieving an average 1.8-point gain over state-of-the-art methods on mathematical and logical reasoning tasks.
VRSD is defined by maximizing query-to-sum similarity, proven NP-complete, with a parameter-free heuristic outperforming MMR and DPP baselines.
Large-scale review mining of 1M+ comments from 171 Gen-AI apps using an LLM framework reveals top topics plus three opportunities and three challenges for developers.
Cross-domain demonstrations enable conditional positive transfer in in-context learning beyond an example absorption threshold by repairing reasoning structures instead of relying on semantic overlap.
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.
A survey of RAG paradigms, components, benchmarks, and challenges for improving LLMs on knowledge-intensive tasks.
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.
citing papers explorer
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GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
GRIP uses contrastive training on LMM feedback to retrieve beneficial in-context examples for multimodal tasks, outperforming similarity-based methods and transferring across models including GPT-4o.
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Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval
DIN-Retrieval uses domain-invariant neuron representations to retrieve cross-domain demonstrations, achieving an average 1.8-point gain over state-of-the-art methods on mathematical and logical reasoning tasks.
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Vector Retrieval with Similarity and Diversity: How Hard Is It?
VRSD is defined by maximizing query-to-sum similarity, proven NP-complete, with a parameter-free heuristic outperforming MMR and DPP baselines.
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Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study
Large-scale review mining of 1M+ comments from 171 Gen-AI apps using an LLM framework reveals top topics plus three opportunities and three challenges for developers.
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Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning
Cross-domain demonstrations enable conditional positive transfer in in-context learning beyond an example absorption threshold by repairing reasoning structures instead of relying on semantic overlap.
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
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From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.
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Retrieval-Augmented Generation for Large Language Models: A Survey
A survey of RAG paradigms, components, benchmarks, and challenges for improving LLMs on knowledge-intensive tasks.
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A Comprehensive Overview of Large Language Models
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.