Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models exhibit remarkable in-context learning (ICL) capabilities, but the underlying working mechanism of ICL remains unclear. |
| Approach: | They propose a Two-Dimensional Coordinate System that unifies both views into a systematic framework that explains the behavior of ICL through two orthogonal variables: whether similar examples are presented in the demonstrations and whether LLMs can recognize the task. |
| Outcome: | The proposed method can interpret ICL for generation tasks effectively. |
Similar Papers
What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) can perform in-context learning (ICL) with only a few demonstrations, but its mechanisms are not well-understood. |
| Approach: | They characterize two ways in which LLMs leverage demonstrations to solve tasks with a few demonstrations. |
| Outcome: | The proposed model achieves non-trivial performance with only TR, and TR does not improve with larger models or more demonstrations. |
Label Words as Local Task Vectors in In-Context Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable abilities, one of the most important being in-context learning (ICL). |
| Approach: | They hypothesized that the network creates a task vector in specific positions during ICL, which can be computed by averaging across the dataset. |
| Outcome: | The proposed model can achieve zero-shot performance with dummy inputs comparable to few-shot learning by patching the global task vector. |
How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning (2024.emnlp-main)
Copied to clipboard
| Challenge: | In-context learning (ICL) is an emergent ability of large language models. |
| Approach: | They propose to use in-context learning to predict sentences with semantically-unrelated labels on 1% heads to investigate the mechanism. |
| Outcome: | The proposed methods reduce the majority label bias and recency bias by 22% and 17%, respectively. |
ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing evaluation frameworks focus on language abilities and knowledge, often overlooking the assessment of ICL ability. |
| Approach: | They propose to evaluate the ICL ability of Large Language Models (LLMs) using the ICLEval benchmark. |
| Outcome: | The proposed benchmark demonstrates that ICL ability is universally present in different LLMs and model size is not the sole determinant of ICL efficacy. |
A Survey on In-context Learning (2024.emnlp-main)
Copied to clipboard
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui
| Challenge: | In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples . |
| Approach: | They propose to explore ICL to evaluate and extrapolate the ability of large language models. |
| Outcome: | The proposed methods can be used to evaluate and extrapolate the ability of large language models. |
In-Context Learning Creates Task Vectors (2023.findings-emnlp)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a powerful new learning paradigm for Large Language Models (LLMs). |
| Approach: | They propose to use a model with a prompt and a query to learn a mapping based on two examples to produce the output. |
| Outcome: | The proposed model can learn functions from a simple structure based on a training set and a single task vector calculated from the training set. |
What Do Language Models Learn in Context? The Structured Task Hypothesis. (2024.acl-long)
Copied to clipboard
| Challenge: | Pre-trained large language models have exhibited an impressive ability to learn in context across various domains, e.g., code generation, education, medicine and even medicine. |
| Approach: | They taxonomize existing candidate theories into three competing hypotheses that explain LLMs’ ability to learn in context. |
| Outcome: | The proposed model can learn a task from in-context examples presented in a demonstration and generalize it to the prompt. |
Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored . |
| Approach: | They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries. |
| Outcome: | The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries . |
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples. |
| Approach: | They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept . |
| Outcome: | The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples. |
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a promising capability for large language models (LLMs) but its underlying mechanism remains unexplored. |
| Approach: | They propose a demonstration compression technique to expedite inference and an analysis framework for diagnosing ICL errors in GPT2-XL. |
| Outcome: | The proposed method improves ICL performance and expedites inference. |