On the Relation between Sensitivity and Accuracy in In-Context Learning (2023.findings-emnlp)
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| Challenge: | In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios. |
| Approach: | They propose a few-shot selective prediction method that abstains from sensitive predictions. |
| Outcome: | The proposed method outperforms confidence-based and entropy-based methods on ten classification datasets. |
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How are Prompts Different in Terms of Sensitivity? (2024.naacl-long)
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| Challenge: | In-context learning (ICL) has become one of the most popular learning paradigms due to the rapid development of large language models (LLMs). |
| Approach: | They propose a prompt analysis based on sensitivity and introduce sensitivity-aware decoding which incorporates sensitivity estimation as a penalty term in the standard greedy decoding. |
| Outcome: | The proposed approach is particularly useful when information in the input is scarce. |
Mitigating Label Biases for In-context Learning (2023.acl-long)
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| Challenge: | Existing methods to categorize label biases in in-context learning (ICL) have not addressed all three types of label bias. |
| Approach: | They propose a method that estimates a language model’s label bias using random in-domain words from the task corpus to categorize and detect label biases in ICL. |
| Outcome: | The proposed method significantly improves the performance of GPT-J and GPT-3 on a wide range of tasks. |
In-context Learning and Gradient Descent Revisited (2024.naacl-long)
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| Challenge: | In-context learning (ICL) has shown impressive results in few-shot learning tasks, yet its underlying mechanism remains elusive. |
| Approach: | They propose a simple gradient descent-based optimization procedure that respects layer causality and improves similarity scores significantly. |
| Outcome: | The proposed procedure improves similarity scores on untrained models despite not showing ICL. |
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)
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| 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. |
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations (2022.emnlp-main)
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Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, Taeuk Kim
| Challenge: | Intuitively, ground-truth labels should have as much impact in in-context learning as supervised learning, but the impact of the quality of demonstrations remains elusive. |
| Approach: | They propose to measure input-label correspondence and ground-truth label effect ratio . they propose to use verbosity of prompt templates and language model size as controlling factors . |
| Outcome: | The proposed metrics show that ground-truth labels have less impact than previously thought . the authors identify key components as controlling factors to achieve noise-resilient ICL . |
A Study on the Calibration of In-context Learning (2024.naacl-long)
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Hanlin Zhang, YiFan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric Xing, Himabindu Lakkaraju, Sham Kakade
| Challenge: | Prior research has demonstrated improvements in the calibration of language models (LMs) in-context learning is a popular method for adapting static LMs to safety-critical domains. |
| Approach: | They use in-context learning to adapt static language models through tailored prompts to a wide range of tasks and find that miscalibration occurs in low-shot settings. |
| Outcome: | The proposed calibrations show that models exhibit increased miscalibration before achieving better calibration in low-shot settings. |
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)
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| 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. |
Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning (2024.findings-emnlp)
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| Challenge: | Fine-tuning and in-context learning are two prevalent methods in imbuing large language models with task-specific knowledge. |
| Approach: | They propose to use a circuit shift theory to explain why in-context learning is superior to fine-tuning for tasks with implicit patterns. |
| Outcome: | The proposed method can grasp deep patterns and significantly improve accuracy on implicit patterns, compared with fine-tuning and in-context learning. |
In-Context Learning (and Unlearning) of Length Biases (2025.naacl-long)
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| Challenge: | Existing work has demonstrated the ability of large language models to learn lexical and label biases in-context negatively impacts performance and robustness of models. |
| Approach: | They investigate the impact of length biases on in-context learning by analyzing model length information in-constext. |
| Outcome: | The proposed model learns length biases in the context window without parameter updates. |
Large Language Models are Miscalibrated In-Context Learners (2025.findings-acl)
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| Challenge: | In-context Learning and Supervised Fine-Tuning have emerged as pre-dominant methodologies for machine learning and NLP. |
| Approach: | They propose to use self-ensembling to improve both performance and calibration of language models. |
| Outcome: | The proposed learning paradigms can achieve better calibration and better performance than the previous learning paradigm. |