Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines (2025.findings-acl)
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Do Xuan Long, Duong Ngoc Yen, Do Xuan Trong, Anh Tuan Luu, Kenji Kawaguchi, Shafiq Joty, Min-Yen Kan, Nancy F. Chen
| Challenge: | In-context learning is an important but not fully understood ability of pre-trained large language models. |
| Approach: | They propose a tool that generates two streams of guidelines capturing task language and format distributions and prompts them to define them by prompting. |
| Outcome: | The proposed model improves both strong open- and closed-source LLMs by over 5% in both zero- and few-shot settings. |
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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 . |
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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. |
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| Challenge: | Large language models can perform a task by conditioning on task instructions and a few input-output examples without optimizing any parameters. |
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| Challenge: | Large language models (LLMs) have the ability of in-context generation (ICG) when given an in-text prompt, they can implicitly recognize the pattern of the examples and complete the prompt in the desired way. |
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| Challenge: | In-context learning is limited by context length, but it can be used for many tasks. |
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In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax (2024.naacl-long)
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| Challenge: | In-context learning is a common method for teaching large language models new tasks . given labeled examples in the input context, the model learns to perform the task without weight updates. |
| Approach: | They examine whether models guided via ICL infer the underlying structure of the task defined by the context or rely on superficial heuristics that only generalize to identically distributed examples. |
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Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism (2024.emnlp-main)
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| 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. |
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Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions (2025.findings-emnlp)
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| Challenge: | In-context learning has improved performance of large language models, but descriptive instructions are still under-explored. |
| Approach: | They propose an ensemble prompt framework to describe selection criteria of multiple in-context examples. preliminary experiments on machine translation confirm that this framework boosts ICL performance. |
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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. |
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ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)
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| 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. |