Papers by Aldo Lipani
Multilingual Refusal Alignment for Safer Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly used globally, but their safety and alignment can vary unpredictably between languages. |
| Approach: | They propose a multilingual refusal alignment dataset to investigate whether alignment transfers cross-lingually and how language consistency is preserved during training. |
| Outcome: | The proposed model can be trained on multilingual datasets without affecting general performance. |
A Survey on Asking Clarification Questions Datasets in Conversational Systems (2023.acl-long)
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| Challenge: | Existing studies on Asking Clarification Questions (ACQs) are incomparable due to inconsistent data, experimental setups and evaluation strategies. |
| Approach: | They analyse the current research status on Asking Clarification Questions (ACQs) and propose a set of evaluation metrics and benchmarks for multiple ACQs-related tasks. |
| Outcome: | The proposed techniques are compared with the available datasets and evaluated against benchmarks. |
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking (2022.acl-long)
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| Challenge: | Existing approaches to model the relations between domains and slots fail to address these issues and can be generalized to unseen domains. |
| Approach: | They propose a Dynamic Schema Graph Fusion Network which generates a dynamic schema graph to explicitly fuse prior slot-domain membership relations and dialogue-aware dynamic slot relations. |
| Outcome: | The proposed model outperforms existing methods on benchmark datasets showing that it can extract users' goals or intentions as dialogue states and keep them updated over the whole dialogue. |
Mitigating Context Interference for Reliable and Efficient Search Agents (2026.acl-long)
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Boyang Xue, Bin Wu, Shuofei Qiao, Sheng Wang, Rui Wang, Yiming Du, Hongru Wang, Jeff Z. Pan, Emine Yilmaz, Kam-Fai Wong, Aldo Lipani
| Challenge: | Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved. |
| Approach: | They propose a distill-based context refiner to dynamically mitigate context interference . they also propose RLs that refine contexts to generate outputs . |
| Outcome: | The proposed refiner can mitigate context interference in multi-turn search agents. |
Learning to Execute Actions or Ask Clarification Questions (2022.findings-naacl)
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| Challenge: | Existing work on Minecraft Corpus Dataset only learns to execute instructions neglecting the importance of asking for clarifications. |
| Approach: | They propose to annotate all builder utterances into eight types, including clarification questions, and propose a builder agent model capable of determining when to ask or execute instructions. |
| Outcome: | The proposed model outperforms existing models on the collaborative building task with a substantial improvement. |
Interpretability-based Tailored Knowledge Editing in Transformers (2024.emnlp-main)
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| Challenge: | Existing methods for modifying in-context learning fail to analyze the instability of in-constitu learning outcomes. |
| Approach: | They propose a model-based knowledge editing method that considers the unique information flow of each sample and aims to correct errors without costly retraining. |
| Outcome: | The proposed method exploits the critical role of feed-forward MLPs in decoder-only models and reveals diverse attribute recall across transformer layers, guiding edits to specific features at different depths and mitigating over-editing issues. |
Lexical Entrainment for Conversational Systems (2023.findings-emnlp)
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| Challenge: | Conversational agents are expected to possess human-like features such as lexical entrainment (LE). |
| Approach: | They propose a dataset and a measure for LE for conversational systems to explicitly integrate LE into conversational system. |
| Outcome: | The proposed dataset and a measure for LE for conversational systems address this human-like phenomenon. |
Subsequence Based Deep Active Learning for Named Entity Recognition (2021.acl-long)
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| Challenge: | Active Learning (AL) has been successfully applied to Deep Learning to drastically reduce the amount of data required to achieve high performance. |
| Approach: | They propose to query subsequences within sentences and propagate their labels to other sentences. |
| Outcome: | The proposed approach achieves high performance on OntoNotes 5.0 and CoNLL 2003 with only 13% of training data and 27% of the training data. |
Transparent and Scrutable Recommendations Using Natural Language User Profiles (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) rely on implicit or explicit feedback from users to suggest new items, resulting in a lack of transparency and a user's ability to scrutinize and modify their preferences. |
| Approach: | They propose to use a natural language (NL) user profile to summarize a user's preferences and then use it to fine-tune a LLM using only NL profiles to make transparent and scrutable recommendations. |
| Outcome: | The proposed model performs on two benchmarking rating prediction datasets and is comparable to existing models. |