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Alfonso Amayuelas

10 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

DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics

EMNLP 2025

Multi-agent debates have been introduced to improve the accuracy of Large Language Models (LLMs) by having multiple agents discuss solutions to a problem over several rounds of debate. However, models often generate incorrect yet confident-sounding responses, which can mislead the others. This issue

2025

Generalization v.s. Memorization: Tracing Language Models’ Capabilities Back to Pretraining Data

ICLR 2025poster

The impressive capabilities of large language models (LLMs) have sparked debate over whether these models genuinely generalize to unseen tasks or predominantly rely on memorizing vast amounts of pretraining data. To explore this issue, we introduce an extended concept of memorization, distributional…

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

Investigating the Transferability of Code Repair for Low-Resource Programming Languages

NAACL 2025findings

Large language models (LLMs) have shown remarkable performance on code generation tasks. A recent use case is iterative code repair, where an LLM fixes an incorrect program by rationalizing about errors and generating new code. Recent works augment the code repair process by integrating modern techn…

2024

Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models

ACL 2024findings

This paper investigates the capabilities of Large Language Models (LLMs) in understanding their knowledge and uncertainty over questions. Specifically, we focus on addressing known-unknown questions, characterized by high uncertainty due to the absence of definitive answers. To facilitate our study,…

2024

MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate

EMNLP 2024finding

Large Language Models (LLMs) have shown exceptional results on current benchmarks when working individually. The advancement in their capabilities, along with a reduction in parameter size and inference times, has facilitated the use of these models as agents, enabling interactions among multiple mo…

2024

Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation

ICML 2024poster

Pre-trained language models (LMs) are able to perform complex reasoning without explicit fine-tuning. To understand how pre-training with a next-token prediction objective contributes to the emergence of such reasoning capability, we propose that we can view an LM as deriving new conclusions by aggr…

2022

Neural Methods for Logical Reasoning over Knowledge Graphs

ICLR 2022poster

Reasoning is a fundamental problem for computers and deeply studied in Artificial Intelligence. In this paper, we specifically focus on answering multi-hop logical queries on Knowledge Graphs (KGs). This is a complicated task because, in real world scenarios, the graphs tend to be large and incomple…

2021

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

AAAI 2021technical

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This missingness, if ignored, nullifies any fair…

Cited by 41SourcePDFScholar