| 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. |
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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. |
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. |
Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective (2026.acl-long)
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Bishwamittra Ghosh, Soumi Das, Till Speicher, Qinyuan Wu, Mohammad Aflah Khan, Deepak Garg, Krishna P. Gummadi, Evimaria Terzi
| Challenge: | Prior studies comparing FT and ICL have yielded mixed and inconclusive results due to inconsistent experimental setups. |
| Approach: | They propose a formal language learning task with precise language boundaries, controlled string sampling, and no data contamination to enable a rigorous comparison. |
| Outcome: | The proposed task offers precise language boundaries, controlled string sampling, and no data contamination. |
Exploring the Relationship between In-Context Learning and Instruction Tuning (2024.findings-emnlp)
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| Challenge: | In-Context Learning (ICL) and Instruction Tuning (IT) are two primary paradigms of adopting Large Language Models (LLMs) to downstream applications, but they are significantly different. |
| Approach: | They examine how the hidden states of Large Language Models change in these two paradigms by examining how they differ in implementation. |
| Outcome: | The proposed model changes the hidden states of LLMs as if its accompanying demonstrations were used to instructionally tune the model. |
Ensemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMs (2023.findings-emnlp)
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Young-Suk Lee, Md Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos, Ramón Astudillo
| Challenge: | Empirical studies with different instruction-tuned LMs show that our proposed method yields higher-quality instruction tuning data than Self-Instruct. |
| Approach: | They propose to use in-context learning techniques to train strong conversational agents . they propose to categorize and simplify ICL templates to make prompt learning easier . |
| Outcome: | Empirical results show that the proposed method yields higher-quality instruction tuning data than Self-Instruct and improves performance of both vanilla and instruction-tuned LMs. |
Large Language Models Are Overconfident in Their Own Responses (2026.findings-acl)
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| Challenge: | Prior work has shown that instruction-tuned large language models are less well calibrated than their base pre-trained counterparts. |
| Approach: | They propose a simple inference-time strategy that frams the model’s answer as user input during confidence elicitation. |
| Outcome: | The proposed approach reduces overconfidence and improves calibration by up to 26% without retraining. |
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment (2024.findings-emnlp)
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| Challenge: | Recent studies have demonstrated that In-Context Learning (ICA) can align Large Language Models (LLMs) with human preferences without requiring parameter adjustments. |
| Approach: | They investigate the effectiveness of each part in enabling ICA to function effectively and examine how variants in these parts impact alignment performance. |
| Outcome: | The proposed model can comprehend human instructions without parameter adjustments. |
Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance (2024.findings-emnlp)
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| Challenge: | In-context learning (ICL) is a dominant paradigm in natural language processing. |
| Approach: | They propose a prompting method for classification tasks using exemplar answers in a *comparative format' they also propose introducing a test instance before the exemplars to improve performance . |
| Outcome: | The proposed method achieves up to 13.76% increase in accuracy on classification tasks across decoder-only and encoder-decoder LLMs. |
Investigating the Multilingual Calibration Effects of Language Model Instruction Tuning (2026.eacl-short)
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Jerry Huang, Peng Lu, Qiuhao Zeng, Yusuke Iwasawa, Yutaka Matsuo, Sarath Chandar, Edison Marrese-Taylor, Irene Li
| Challenge: | despite advances in foundation model research, the relationship between large language models and their calibration remains an open area of research. |
| Approach: | They examine a gap in the calibration of large language models within multilingual settings to better understand how data scarcity can potentially lead to different calibration effects. |
| Outcome: | The proposed calibration gap is found in two multilingual benchmarks over 29 and 42 languages. |
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning (2024.findings-naacl)
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| Challenge: | Existing studies have explored multiple aspects that affect the performance of large language models (LLMs) such as input-output mapping, extensive data resources, and the ability to train on labeled examples. |
| Approach: | They propose a framework that injects knowledge into LLMs during continual self-supervised pre-training and judiciously selects examples with high knowledge relevance. |
| Outcome: | The proposed framework outperforms baseline models and improves by more than 13% and 7% on text classification and question-answering tasks. |