Papers by Giulia Pucci
Multilingual Reasoning via Self-training (2025.naacl-long)
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| Challenge: | Recent studies have introduced eclectic strategies to improve reasoning beyond English, but these methods are related to specific language that is not always optimal for reasoning. |
| Approach: | They propose a modular approach that instructs models to structure reasoning passages in a different problem space and then self-refines their capabilities to deliver step-wise reasoning passage. |
| Outcome: | The proposed approach achieves significant improvements in multilingual reasoning of various models and task, with improved reasoning consistency across languages. |
Empowering cross-lingual abilities of instruction-tuned large language models by translation-following demonstrations (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are often unbalanced towards English because of the imbalance in the distribution of pre-training data. |
| Approach: | They propose to empower Instruction-tuned LLMs (It-LLMs) in languages other than English by building semantic alignment between them. |
| Outcome: | The proposed model outperforms the existing model on multilingual question answering benchmarks and adapted versions of MMLU and BBH on six different languages. |
R2-MultiOmnia: Leading Multilingual Multimodal Reasoning via Self-Training (2025.acl-long)
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| Challenge: | Recent studies have introduced eclectic strategies to enhance MLLMs’ reasoning capabilities, but they remain related to a single language. |
| Approach: | They propose a modular approach that instructs models to abstract key elements of the reasoning process and refine reasoning trajectories via self-correction. |
| Outcome: | The proposed approach improves multimodal reasoning, gets aligned performances among the languages approaching strong models and improves the model's performance. |
Learning Multilingual Agentic Policy to Control Sycophancy (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) are effective at adapting to users’ styles, preferences, and contextual signals, but can manifest as sycophancy, i.e., alignment with user-implied beliefs or assumptions even when these contradict factual correctness, uncertainty, or proper logical reasoning. |
| Approach: | They propose to use large language models to model sycophancy as a decision-making problem by learning agentic policies that are trained to optimise a multi-objective reward that balances task success, scophancies resistance and behavioural consistency. |
| Outcome: | The proposed model equips a model with an explicit action space that includes answering directly, countering misleading signals, or asking for clarification. |
Advancing Oversight Reasoning across Languages for Audit Sycophantic Behaviour via X-Agent (2025.emnlp-main)
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| Challenge: | Large language models have demonstrated capabilities that are satisfactory to a wide range of users by adapting to their culture and wisdom. |
| Approach: | They propose an Oversight Reasoning framework that audits human–LLM dialogues, reasons about them, captures sycophancy and corrects the final outputs. |
| Outcome: | The proposed framework detects sycophancy, reduces unwarranted agreement and improves cross-turn consistency across different scenarios and languages. |
Does the Language Matter? Curriculum Learning over Neo-Latin Languages (2024.lrec-main)
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| Challenge: | Curriculum Learning (CL) is emerging as a useful technique to reduce the cost of pre-training Large Language Models. |
| Approach: | They propose to organize training examples from the simplest to the most complex . they then test the approach to Italian and French to determine the complexity of examples . |
| Outcome: | The proposed method can be exported to other languages without adaptation. |
A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages (2024.findings-naacl)
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| Challenge: | Existing methods for eliciting Large Language Models (LLMs) to solve complex tasks are limited to English due to the imbalance in the distribution of pre-training data. |
| Approach: | They propose a method for aligning Cross-lingual CoT reasoning across languages . they propose eliciting Large Language Models to solve complex tasks step-by-step . |
| Outcome: | The proposed method outperforms existing prompting methods by reducing interactions and achieving state-of-the-art performance. |
Exploring Backward Reasoning in Large Language Models (2025.findings-naacl)
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| Challenge: | Multi-step reasoning through in-context learning strategies have been extensively explored, highlighting the abilities of Large Language Models (LLMs) to solve problems in a step-wise manner. |
| Approach: | They propose to use Large Language Models to generate answers from step-by-step reasoning by re-constructing the original question that led to the final answer. |
| Outcome: | The proposed models show that they are able to reason about the conclusion and reconstruct the original question that led to the final answer. |
Empowering Multi-step Reasoning across Languages via Program-Aided Language Models (2024.emnlp-main)
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| Challenge: | In-context learning methods elicit Large Language Models to solve tasks using provided demonstrations without parameter updates. |
| Approach: | They propose a method for aligning reasoning programs across languages using a double-step cross-lingual prompting mechanism. |
| Outcome: | The proposed method outperforms existing prompting methods and reduces interaction time. |