In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties (2025.emnlp-main)
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| Challenge: | Existing models fail to adapt to unfamiliar speakers and language varieties . however, there are significant gaps in the adaptation of certain varieties based on the test speaker, variety, or recording conditions . |
| Approach: | They propose a framework that allows for in-context learning in Phi-4 Multimodal . they find that as few as 12 example utterances reduce word error rates by 19.7% . |
| Outcome: | The proposed framework reduces word error rates by 19.7% across diverse English corpora. |
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Multimodal In-context Learning for ASR of Low-resource Languages (2026.findings-acl)
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| Challenge: | In-context learning with large language models addresses this limitation, but prior work focuses on high-resource languages covered during training and text-only settings. |
| Approach: | They propose to use multimodal ICL to learn unseen languages with multimodal learning to improve ASR in large language models. |
| Outcome: | The proposed model outperforms existing models on unseen languages with multimodal ICL (MICL) and cross-lingual transfer learning matches or outperformed models without using target-language data. |
Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks (2026.acl-long)
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| Challenge: | In-context learning (ICL) enables models to adapt to new tasks via inference-time demonstrations. |
| Approach: | They propose a simple inference-stage enhancement method that reinforces task mapping transfer. |
| Outcome: | The proposed method strengthens task mapping transfer in multimodal models . it performs comparable to text-only ICL in zero-shot settings but degrades significantly under few-shot demonstrations. |
Revisiting In-Context Learning with Long Context Language Models (2025.findings-acl)
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| Challenge: | In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. |
| Approach: | They revisited previous studies using in-context learning techniques . they found that using a data augmentation approach, they significantly improved ICL performance . |
| Outcome: | The proposed approach significantly improves ICL performance on 18 datasets spanning 4 tasks . the proposed approach does not improve performance over a simple random sample selection method . |
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. |
| Outcome: | The proposed framework improves on commonsense, math, logical reasoning and hallucination tasks with three LLMs. |
How Does In-Context Learning Help Prompt Tuning? (2024.findings-eacl)
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| Challenge: | a growing number of parameter-efficient adaptation methods are needed to fine-tune large language models. |
| Approach: | They propose a method that combines prompt tuning and in-context learning to improve prompt tuning by concatenating a natural language demonstration with learned prompt embeddings. |
| Outcome: | The proposed method outperforms prompt tuning and prompt tuning on five language generation tasks. |
Blessing of Multilinguality: A Systematic Analysis of Multilingual In-Context Learning (2025.findings-acl)
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| Challenge: | In-context learning (ICL) is a widely adopted technique for learning large language models . however, there is little systematic understanding of when and why it works well . |
| Approach: | They analyze multilingual in-context learning using demonstrations in HRLs to enhance cross-lingual transfer. |
| Outcome: | The proposed method outperforms English-only models on high-resource languages . the study shows that the presence of irrelevant non-English sentences in the prompt yields measurable gains . |
Using Natural Language Explanations to Improve Robustness of In-context Learning (2024.acl-long)
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| Challenge: | Recent studies show that large language models excel in many tasks via in-context learning (ICL). However, ICL struggles to execute complex tasks such as arithmetic, commonsense, and symbolic reasoning. |
| Approach: | They propose to augment ICL with natural language explanations (NLEs) to produce further NLEs on adversarial datasets. |
| Outcome: | The proposed approach yields more accurate results than zero-shot-ICL and using only human-generated NLEs on eight adversarial datasets. |
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning (2023.emnlp-main)
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| Challenge: | Large language models have demonstrated their capability with few-shot inference . however, in-domain demonstrations are not always available in real scenarios . |
| Approach: | They propose unsupervised domain adaptation problem to adapt language models from source domain to target domain without any target labels. |
| Outcome: | The proposed model performs better than baseline models on Sentiment Analysis and Named Entity Recognition tasks. |
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. |
Inference and Verbalization Functions During In-Context Learning (2024.findings-emnlp)
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| Challenge: | Previous work has found that, in some settings, ICL performance is minimally affected by using demonstrations with irrelevant label words. |
| Approach: | They hypothesize that large language models (LMs) perform in-context learning from a handful of demonstrations via two sequential processes: an inference function that solves the task and a verbalization function that maps the inferred answer to the label space. |
| Outcome: | The proposed model can be localized in specific layers across open-source models, including GEMMA-7B, MISTRAL-7B-V0.3, GEIMA-2-27B, and LLAMA-3.1-70B. |