Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism

• URL: http://arxiv.org/abs/2407.17011v2
• Date: Wed, 9 Oct 2024 08:58:40 GMT
• Title: Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism
• Authors: Anhao Zhao, Fanghua Ye, Jinlan Fu, Xiaoyu Shen,
• Abstract summary: Large language models (LLMs) exhibit remarkable in-context learning capabilities. Recent research presents two conflicting views on ICL. We provide a Two-Dimensional Coordinate System that unifies both views into a systematic framework.
• Score: 28.751003584429615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) exhibit remarkable in-context learning (ICL) capabilities. However, the underlying working mechanism of ICL remains poorly understood. Recent research presents two conflicting views on ICL: One emphasizes the impact of similar examples in the demonstrations, stressing the need for label correctness and more shots. The other attributes it to LLMs' inherent ability of task recognition, deeming label correctness and shot numbers of demonstrations as not crucial. In this work, we provide a Two-Dimensional Coordinate System that unifies both views into a systematic framework. The framework explains the behavior of ICL through two orthogonal variables: whether similar examples are presented in the demonstrations (perception) and whether LLMs can recognize the task (cognition). We propose the peak inverse rank metric to detect the task recognition ability of LLMs and study LLMs' reactions to different definitions of similarity. Based on these, we conduct extensive experiments to elucidate how ICL functions across each quadrant on multiple representative classification tasks. Finally, we extend our analyses to generation tasks, showing that our coordinate system can also be used to interpret ICL for generation tasks effectively.

Related papers

• Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning [99.05401042153214]
  In-context learning (ICL) is potentially attributed to two major abilities: task recognition (TR) and task learning (TL) We take the first step by examining the pre-training dynamics of the emergence of ICL. We propose a simple yet effective method to better integrate these two abilities for ICL at inference time.
  arXiv  Detail & Related papers  (2024-06-20T06:37:47Z)
• Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning [43.356895599336504]
  We analyze the working mechanisms of the learning-based demonstration selection methods. We empirically identify two important factors related to similarity measurement. We introduce two effective yet simplified exemplar selection methods catering to task-agnostic and task-specific demands.
  arXiv  Detail & Related papers  (2024-06-14T03:34:02Z)
• Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning [41.606494950216764]
  In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs) This paper decomposes the overall performance of ICL into three dimensions, label space, format, and discrimination. We show that ICL exhibits significant efficacy in regulating the label space and format, which helps LLMs respond to desired label words.
  arXiv  Detail & Related papers  (2024-04-11T08:20:10Z)
• Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning [67.25698169440818]
  Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL) We train the same LLMs with the same demonstration examples via ICL and supervised learning (SL), respectively, and investigate their performance under label perturbations. First, we find that gold labels have significant impacts on the downstream in-context performance, especially for large language models. Second, when comparing with SL, we show empirically that ICL is less sensitive to label perturbations than SL, and ICL gradually attains comparable performance to SL as the model size increases.
  arXiv  Detail & Related papers  (2023-07-28T09:03:19Z)
• Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning [77.7070536959126]
  In-context learning (ICL) emerges as a promising capability of large language models (LLMs) In this paper, we investigate the working mechanism of ICL through an information flow lens. We introduce an anchor re-weighting method to improve ICL performance, a demonstration compression technique to expedite inference, and an analysis framework for diagnosing ICL errors in GPT2-XL.
  arXiv  Detail & Related papers  (2023-05-23T15:26:20Z)
• Iterative Forward Tuning Boosts In-Context Learning in Language Models [88.25013390669845]
  In this study, we introduce a novel two-stage framework to boost in-context learning in large language models (LLMs) Specifically, our framework delineates the ICL process into two distinct stages: Deep-Thinking and test stages. The Deep-Thinking stage incorporates a unique attention mechanism, i.e., iterative enhanced attention, which enables multiple rounds of information accumulation.
  arXiv  Detail & Related papers  (2023-05-22T13:18:17Z)
• What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning [24.395288160951118]
  Large language models (LLMs) exploit in-context learning (ICL) to solve tasks with only a few demonstrations. We characterize two ways through which ICL leverages demonstrations. We show that models can achieve non-trivial performance with only TR, and TR does not further improve with larger models or more demonstrations.
  arXiv  Detail & Related papers  (2023-05-16T18:05:19Z)
• ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction [56.790794611002106]
  Large language models (LLMs) have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning. We propose a simple but effective in-context learning framework called ICL-D3IE. Specifically, we extract the most difficult and distinct segments from hard training documents as hard demonstrations.
  arXiv  Detail & Related papers  (2023-03-09T06:24:50Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.