Recent interest has surged in employing Large Language Models (LLMs) for machine translation (MT) via in-context learning (ICL) (Vilar et al., 2023). Most prior studies primarily focus on optimizing translation quality, with limited attention to understanding the specific aspects of ICL that influence the said quality. To this end, we perform the first of its kind, exhaustive study of in-context learning for machine translation (MT). We first establish that ICL is primarily example-driven and not instruction-driven. Following this, we conduct an extensive exploration of various aspects of the examples to understand their influence on downstream performance. Our analysis includes factors such as quality and quantity of demonstrations, spatial proximity, and source versus target originality. Further, we also investigate challenging scenarios involving indirectness and misalignment of examples to understand the limits of ICL. While we establish the significance of the quality of the target distribution over the source distribution of demonstrations, we further observe that perturbations sometimes act as regularizers, resulting in performance improvements. Surprisingly, ICL does not necessitate examples from the same task, and a related task with the same target distribution proves sufficient. We hope that our study acts as a guiding resource for considerations in utilizing ICL for MT. Our code is available on https://github.com/PranjalChitale/in-context-mt-analysis.

An Empirical Study of In-context Learning in LLMs for Machine Translation

Neural Machine Translation models are extremely data and compute-hungry. However, not all data
points contribute equally to model training and generalization. Data pruning to remove the low-
value data points has the benefit of drastically reducing the compute budget without significant
drop in model performance. In this paper, we propose a new data pruning technique: Checkpoints
Across Time (CAT ), that leverages early model training dynamics to identify the most relevant
data points for model performance. We benchmark CAT against several data pruning techniques
including COMET-QE, LASER and LaBSE. We find that CAT outperforms the benchmarks on
Indo-European languages on multiple test sets. When applied to English-German, English-French
and English-Swahili translation tasks, CAT achieves comparable performance to using the full
dataset, while pruning up to 50% of training data. We inspect the data points that CAT selects
and find that it tends to favour longer sentences and sentences with unique or rare words.

Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning

Hate speech detection has been the subject of high research attention, due to the scale of content created on social media. In spite of the attention and the sensitive nature of the task, privacy preservation in hate speech detection has remained under-studied.
The majority of research has focused on centralised machine learning infrastructures which risk leaking data. In this paper, we show that using federated machine learning can help address privacy the concerns that are inherent to hate speech detection while obtaining up to 6.81\% improvement in terms of F1-score.

A Federated Approach for Hate Speech Detection

Findings In-Person Poster Session 3

poster

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Virtual Poster Session 3

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Jay Gala

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SHORT BIO

Presentations

An Empirical Study of In-context Learning in LLMs for Machine Translation

Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning

A Federated Approach for Hate Speech Detection

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