Papers by Sylvain Lamprier
Structural Deep Encoding for Table Question Answering (2025.findings-acl)
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| Challenge: | Tabular data is a common data format, but many models flatten the structure of a table into a sequence of tokens, resulting in computational costs and over-fitting issues. |
| Approach: | They propose to use special tokens to mark rows and columns, structured embeddings, and sparse attention patterns to preserve structural information of tabular data. |
| Outcome: | The proposed models enhance computational efficiency and preserve structural integrity, leading to better overall performance. |
QuestEval: Summarization Asks for Fact-based Evaluation (2021.emnlp-main)
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Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang, Patrick Gallinari
| Challenge: | Existing evaluation metrics for summarization evaluation are limited and do not correlate well with human judgments. |
| Approach: | They propose to extend existing evaluation metrics to include question answering models to assess whether a summary contains all relevant information in its source document. |
| Outcome: | The proposed framework significantly improves the correlation with human judgments over four evaluation dimensions. |
MLSUM: The Multilingual Summarization Corpus (2020.emnlp-main)
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| Challenge: | Existing biases in multi-lingual datasets are limiting the use of multilingual data in document summarization tasks. |
| Approach: | They present MLSUM, the first large-scale MultiLingual SUMmarization dataset. |
| Outcome: | The proposed dataset contains 1.5M+ article/summary pairs in five different languages. |
Progress Ratio Embeddings: An Impatience Signal for Robust Length Control in Neural Text Generation (2026.findings-acl)
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| Challenge: | Modern neural language models achieve high accuracy in text generation, yet precise control over generation length remains underdeveloped. |
| Approach: | They propose a method to provide robust length control using Reverse Positional Embeddings. |
| Outcome: | The proposed method provides stable length fidelity without degrading text accuracy . the proposed method generalizes well to unseen target lengths . |
Learning Relational Decomposition of Queries for Question Answering from Tables (2024.acl-long)
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| Challenge: | Existing approaches to Table Question-Answering focus on generating answers directly from inputs, but there are limitations when executing numerical operations. |
| Approach: | They propose to imitate a restricted subset of SQL-like algebraic operations and use them to generate a query. |
| Outcome: | The proposed methods bridge the gap between semantic parsing and direct answering methods and offer valuable insights into which types of operations should be predicted by a generative architecture and which should be executed by an external algorithm. |
Rethinking NLP for Chemistry: A Critical Look at the USPTO Benchmark (2025.findings-emnlp)
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| Challenge: | Natural Language Processing (NLP) has revolutionized computer-aided synthesis planning by reframing chemical synthesis prediction as a sequence-to-sequence modeling problem over molecular string representations like SMILES. |
| Approach: | They propose to reframe chemical synthesis prediction as a sequence-to-sequence modeling problem over molecular string representations like SMILES. |
| Outcome: | The proposed framework yields impressive benchmark scores on the USPTO dataset, a large corpus of reactions extracted from US patents. |
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting (2025.findings-naacl)
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Mohamed Salim Aissi, Clément Romac, Thomas Carta, Sylvain Lamprier, Pierre-Yves Oudeyer, Olivier Sigaud, Laure Soulier, Nicolas Thome
| Challenge: | Reinforcement learning (RL) is a promising approach for aligning large language models knowledge with sequential decision-making tasks. |
| Approach: | They propose to use a contrastive loss framework to analyze the sensitivity of LLMs to prompt formulations following RL training in a textual environment. |
| Outcome: | The proposed framework improves the model's robustness and generalization capabilities by minimizing the model’s internal representations and salient tokens. |
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models (D19-1)
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| Challenge: | Abstractive summarization approaches based on Reinforcement Learning (RL) have been proposed to overcome classical likelihood maximization. |
| Approach: | They propose to use Reinforcement Learning to learn the model parameters through RL techniques to overcome classical likelihood maximization. |
| Outcome: | The proposed measures favor ROUGE with the additional property of not requiring reference summaries. |
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation (2021.emnlp-main)
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Clement Rebuffel, Thomas Scialom, Laure Soulier, Benjamin Piwowarski, Sylvain Lamprier, Jacopo Staiano, Geoffrey Scoutheeten, Patrick Gallinari
| Challenge: | QuestEval is a metric used in text-to-text tasks, but its adaptation to Data-to Text tasks requires multimodal Question Generation and Answering systems, which are seldom available. |
| Approach: | They propose to build synthetic multimodal corpora enabling to train multimodal components for a data-QuestEval metric. |
| Outcome: | The proposed method obtains state-of-the-art correlations with human judgment on the WebNLG and WikiBio benchmarks. |