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Marzieh Fadaee

15 accepted papers

2026

Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation

ICLR 2026poster

The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and language, many rely on translations of English datasets, failing t…

Cited by 0SourcecodeScholar
2025

From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions

ACL 2025long

Large Language Models (LLMs) are increasingly used in working environments for a wide range of tasks, excelling at solving individual problems in isolation. However, are they also able to effectively collaborate over long-term interactions? To investigate this, we introduce MemoryCode, a synthetic m…

2025

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

ACL 2025long

Reliable multilingual evaluation is difficult, and culturally appropriate evaluation is even harder to achieve.A common practice to fill this gap is to machine-translate English evaluation sets. However, translation introduces language bias and carries over cultural and regional assumptions from the…

Cited by 0SourcePDFScholar
2025

INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge

ICLR 2025spotlight

The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (i.e., mult…

Cited by 9SourcePDFScholar
2025

M-RewardBench: Evaluating Reward Models in Multilingual Settings

ACL 2025long

Reward models (RMs) have driven the state-of-the-art performance of LLMs today by enabling the integration of human feedback into the language modeling process. However, RMs are primarily trained and evaluated in English, and their capabilities in multilingual settings remain largely understudied. I…

2025

The State of Multilingual LLM Safety Research: From Measuring The Language Gap To Mitigating It

EMNLP 2025

This paper presents a comprehensive analysis of the linguistic diversity of LLM safety research, highlighting the English-centric nature of the field. Through a systematic review of nearly 300 publications from 2020–2024 across major NLP conferences and workshops at ACL, we identify a significant an

Cited by 0SourcePDFScholar
2025

To Code or Not To Code? Exploring Impact of Code in Pre-training

ICLR 2025poster

Including code in the pre-training data mixture, even for models not specifically designed for code, has become a common practice in LLMs pre-training. While there has been anecdotal consensus among practitioners that code data plays a vital role in general LLMs' performance, there is only limited w…

Cited by 4SourcePDFScholar
2024

Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning

ACL 2024long

Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the fine-tuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to i…

Cited by 94SourcePDFScholar
2024

Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model

ACL 2024long

Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class citizen languages? Our work introduces Aya, a massively multilingual generative language model that follows instructions…

Cited by 181SourcePDFScholar
2024

Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs

ACL 2024long

AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models. Proximal Policy Optimization (PPO) has been installed by the seminal literature as the standard method for the RL part of RLHF. H…

Cited by 174SourcePDFScholar
2024

Elo Uncovered: Robustness and Best Practices in Language Model Evaluation

NeurIPS 2024poster

In Natural Language Processing (NLP), the Elo rating system, originally designed for ranking players in dynamic games such as chess, is increasingly being used to evaluate Large Language Models (LLMs) through "A vs B" paired comparisons. However, while popular, the system's suitability for assessing…

Cited by 40SourcePDFScholar
2024

LLM See, LLM Do: Leveraging Active Inheritance to Target Non-Differentiable Objectives

EMNLP 2024main

The widespread adoption of synthetic data raises new questions about how models generating the data can influence other large language models (LLMs). To start, our work exhaustively characterizes the impact of passive inheritance of model properties by systematically studying how the source of synth…

2024

The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm

EMNLP 2024main

A key concern with the concept of *“alignment”* is the implicit question of *“alignment to what?”*. AI systems are increasingly used across the world, yet safety alignment is often focused on homogeneous monolingual settings. Additionally, preference training and safety measures often overfit to har…

Cited by 18SourcePDFScholar