Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches address key factors that influence multilingual ICL, but they do not integrate them into the model. |
| Approach: | They propose a method that quantifies and optimally balances three factors for improved example selection. |
| Outcome: | Experiments on mCSQA and TYDI show that the proposed method outperforms existing methods. |
Similar Papers
Revisiting In-Context Learning with Long Context Language Models (2025.findings-acl)
Copied to clipboard
| 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 . |
Blessing of Multilinguality: A Systematic Analysis of Multilingual In-Context Learning (2025.findings-acl)
Copied to clipboard
| 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 . |
Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained Multilingual Language Models have shown a strong ability to transfer knowledge across languages. |
| Approach: | They examine factors contributing to the ability of MLLMs to perform zero-shot cross-lingual transfer . they identify consensuses among studies with consistent findings and resolve conflicts . |
| Outcome: | The authors outline and discuss factors that contribute to the ability of MLLMs to perform zero-shot cross-lingual transfer. |
An Empirical Study of In-context Learning in LLMs for Machine Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies focus on optimizing translation quality, with limited attention to understanding specific aspects of ICL that influence the said quality. |
| Approach: | They conduct the first of its kind, exhaustive study of in-context learning for machine translation (MT) they establish that ICL is primarily example-driven and not instruction-driven . |
| Outcome: | The proposed model is based on examples and not instruction-driven learning. |
Pruning Multilingual Large Language Models for Multilingual Inference (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Multilingual large language models (MLLMs) demonstrate better zeroshot learning performance in non-English languages compared to large language model trained on English-dominant data. |
| Approach: | They propose a pruning approach to prune large language models using bilingual sentence pairs from English and other languages to enhance their performance in non-English language. |
| Outcome: | The proposed pruning strategy enhances the MLLMs’ performance in non-English language. |
Seeing All Sides: Multi-Perspective In-Context Learning for Subjective NLP (2026.findings-eacl)
Copied to clipboard
| Challenge: | Modern language models excel at factual reasoning but struggle with value diversity, authors say . task-sensitive tasks such as hate speech expose this limitation . human disagreement captures the diversity of plausible human perspectives, authors argue . |
| Approach: | They evaluate four large language models with human disagreements on five datasets . they find multi-perspective in-context learning outperforms standard prompting . |
| Outcome: | The proposed approach outperforms standard prompting on English labels while disaggregated soft predictions better align with human judgments in Arabic and Italian datasets. |
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. |
Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks (2026.acl-long)
Copied to clipboard
| 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. |
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes (2024.naacl-short)
Copied to clipboard
| Challenge: | Multi-task learning (MTL) for generalist models is a promising direction that offers transfer learning potential. |
| Approach: | They propose to combine multi-task learning (MTL) with in-context learning (ICL) to build models that can generalize to multiple tasks while being robust to out-of-distribution examples. |
| Outcome: | The proposed training strategies enable models to learn difficult tasks while mixing in prior tasks, denoted as mixed curriculum. |
Submodular-based In-context Example Selection for LLMs-based Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Prior studies have focused on the role of well-chosen examples in in-context learning . |
| Approach: | They propose to use multiple translational factors for in-context example selection by using monotone submodular function maximization. |
| Outcome: | The proposed approach outperforms random selection and robust single-factor baselines across various NLP tasks. |