Papers by Simon Lupart
Generating Multi-Aspect Queries for Conversational Search (2026.eacl-long)
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| Challenge: | Conversational information seeking (CIS) systems aim to model the user’s information need within the conversational context and retrieve the relevant information. |
| Approach: | They propose a multi-aspect query generation and retrieval framework which uses Large Language Models to break the user utterance into multiple queries. |
| Outcome: | The proposed framework outperforms state-of-the-art query rewriting methods on six widely used CIS datasets and fine-tunes the model on MASQ yields significant improvements. |
ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering (2026.acl-long)
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| Challenge: | Unlike static ‘rewrite, retrieve, and generate’ pipelines, ChatR1 interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through RL. |
| Approach: | They propose a reasoning framework based on reinforcement learning (RL) for conversational question answering that interleaves search and reasoning across turns and provides turn-level feedback. |
| Outcome: | The proposed framework outperforms competing models on five CQA datasets, measured by different metrics (F1, BERTScore, and LLM-as-judge). |