Papers by Thibaut Thonet
Drift: Decoding-time Personalized Alignments with Implicit User Preferences (2025.findings-emnlp)
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| Challenge: | Drift personalizes large language models at decoding time with implicit user preferences . Unlike traditional Reinforcement Learning from Human Feedback, Drift operates in a training-free manner . |
| Approach: | They propose a framework that personalizes large language models at decoding time with implicit user preferences. |
| Outcome: | The proposed framework personalizes large language models at decoding time with implicit user preferences. |
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks for long-context LLMs focus on generic tasks that are not necessarily aligned with real-world applications. |
| Approach: | They propose to augment existing ELITR corpus by adding 271 manually crafted questions with their ground-truth answers and noisy versions of meeting transcripts altered to target different Word Error Rate levels. |
| Outcome: | The proposed benchmark augments the existing ELITR corpus by adding 271 manually crafted questions with ground-truth answers, as well as noisy versions of meeting transcripts altered to target different Word Error Rate levels. |
FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data (2025.emnlp-main)
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| Challenge: | Recent studies have focused on personalizing conversational assistants to meet specific user preferences. |
| Approach: | They propose to use a dataset to analyze a problem where only a small set of preference annotations can be collected per user. |
| Outcome: | The proposed approach leverages high-level features discovered from the data, achieving the best overall performance. |