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Negar Foroutan

7 accepted papers

2025

How Do Multilingual Language Models Remember Facts?

ACL 2025finding

Large Language Models (LLMs) store and retrieve vast amounts of factual knowledge acquired during pre-training. Prior research has localized and identified mechanisms behind knowledge recall; however, it has only focused on English monolingual models. The question of how these mechanisms generalize…

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

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

NeurIPS 2025poster

Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstract and complex phenomena such as `safety' and `robustness' requires strong construct validity, that is, having measures t…

Cited by 0SourceScholar
2025

WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts

ACL 2025finding

Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic document understanding (DU). While vision-language large models (VLLMs) have demonstrated improvements across various tasks, t…

2024

Discovering Knowledge-Critical Subnetworks in Pretrained Language Models

EMNLP 2024main

Pretrained language models (LMs) encode implicit representations of knowledge in their parameters. However, localizing these representations and disentangling them from each other remains an open problem. In this work, we investigate whether pretrained language models contain various *knowledge-crit…

2023

Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention

EMNLP 2023long findings

In this work, we study whether multilingual language models (MultiLMs) can transfer logical reasoning abilities to other languages when they are fine-tuned for reasoning in a different language. We evaluate the cross-lingual reasoning abilities of MultiLMs in two schemes: (1) where the language of t…

Cited by 0SourcecodeScholar
2022

Discovering Language-neutral Sub-networks in Multilingual Language Models

EMNLP 2022main

Multilingual pre-trained language models transfer remarkably well on cross-lingual downstream tasks. However, the extent to which they learn language-neutral representations (i.e., shared representations that encode similar phenomena across languages), and the effect of such representations on cross…