Looped linear transformers with LN provably converge via GD to implement the power method on principal component prediction.
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A Survey on In-context Learning
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abstract
With the increasing capabilities of large language models (LLMs), in-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP), where LLMs make predictions based on contexts augmented with a few examples. It has been a significant trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress and challenges of ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques, including training strategies, prompt designing strategies, and related analysis. Additionally, we explore various ICL application scenarios, such as data engineering and knowledge updating. Finally, we address the challenges of ICL and suggest potential directions for further research. We hope that our work can encourage more research on uncovering how ICL works and improving ICL.
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- abstract With the increasing capabilities of large language models (LLMs), in-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP), where LLMs make predictions based on contexts augmented with a few examples. It has been a significant trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress and challenges of ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques, including training strategies, prompt des
co-cited works
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CREST-Search is a red-teaming framework that crafts seemingly benign search queries to induce unsafe citations from web-augmented LLMs, backed by a new WebSearch-Harm dataset for fine-tuning a specialized attacker model.
SITE applies soft gradient-based head selection to inject ICL-derived task embeddings, outperforming prior embedding adaptation and few-shot ICL across generation, reasoning, and NLU tasks on 12 LLMs from 4B to 70B parameters.
One training example via RLVR boosts LLM math reasoning from 17.6% to 35.7% average across six benchmarks.
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citing papers explorer
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ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild
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When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models
CREST-Search is a red-teaming framework that crafts seemingly benign search queries to induce unsafe citations from web-augmented LLMs, backed by a new WebSearch-Harm dataset for fine-tuning a specialized attacker model.
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Soft Head Selection for Injecting ICL-Derived Task Embeddings
SITE applies soft gradient-based head selection to inject ICL-derived task embeddings, outperforming prior embedding adaptation and few-shot ICL across generation, reasoning, and NLU tasks on 12 LLMs from 4B to 70B parameters.
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Reinforcement Learning for Reasoning in Large Language Models with One Training Example
One training example via RLVR boosts LLM math reasoning from 17.6% to 35.7% average across six benchmarks.
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A Rule-Aware Prompt Framework for Structured Numeric Reasoning in Cyber-Physical Systems
A rule-aware modular prompt framework enables LLMs to perform structured numeric reasoning on power grid data by separating rules from normalized deviations, improving anomaly detection consistency and reducing token use in IEEE 118-bus tests.
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SnapAudit: Active Auditing of Differentially Private In-Context Learning via Snapshot-Based Simulation
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Localizing Task Recognition and Task Learning in In-Context Learning via Attention Head Analysis
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TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
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Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm
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Context-Guided Decompilation: A Step Towards Re-executability
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Online In-Context Distillation for Low-Resource Vision Language Models
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SSA: Improving Performance With a Better Scoring Function
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Diverse LLMs or Diverse Question Interpretations? That is the Ensembling Question
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LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models
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Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption
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Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
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