Modern LLMs have acquired impressive language understanding while being trained on a massive amount of data but they may struggle to generate responses that align with user preferences and expectations for the input request. In deployment systems, a crucial task is to ensure that the generated content is free of offensive expressions and patronizing language to address the safety risks posed by deployed systems, such as chatbots and conversational agents. While a lot of effort is invested into alignment of LLMs [1,2,3,4,5], the safety risk is still existent, especially for non-English content [6,7,8,9]. Moreover, many aligned models tend to overreact to certain “trigger patterns” (eg. swear words, mention of protected attributes, etc.) and may wrongly refuse to answer inoffensive questions, which results in existing tension between “helpfulness” and “safety”. Models’ over-reliance on such patterns makes detection of implicit hate speech more challenging [10,11,12,13].
The goal of this internship is to investigate strategies to diminish offensive content generation focusing on implicit offensive speech in multilingual settings.
This internship is part of an ANR project called DIKÉ, which aims at studying bias, fairness and ethics of compressed NLP models. Results are expected to be reported in a paper by the end of the internship (or soon after). The internship will be hosted at NAVER LABS Europe and co-supervised by NAVER LABS and Lyon 2 University researchers.
Supervisors: Caroline Brun and Vassilina Nikoulina
- PhD or last year MSc student in NLP-related domains
- Solid deep learning and NLP background
- Strong programming skills, with knowledge of PyTorch, NumPy and the HF Transformers
- Familiarity with recent preference optimization techniques, such as DPO, is a plus
- Ability to communicate in English; knowledge of French is an advantage.
[1] Compositional Preference Models for Aligning LMs, Go et al., ICLR 2024
[2] Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs, Ahmadian et al., ACL 2024
[3] Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2. Ivison et al., arXiv:2311.10702
[4] Direct Preference Optimization: Your Language Model is Secretly a Reward Model, Rafailov et al., NeurIPS 2023
[5] Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models, Pozzobon et al., EMNLP Findings 2023
[6] Preference tuning for toxicity mitigation generalizes across languages, Li et al., arXiv:2406.16235
[7] From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models, Ermis et al. ACL Findings 2024.
[8] Polyglo Toxicity Prompts: Multilingual Evaluation of Neural Toxic Degeneration in Large Language Models, Jain et al, arXiv:2405.09373
[9] FrenchToxicityPrompts: a large benchmark for evaluating and mitigating toxicity in French Texts. Brun and Nikoulina, TRAC workshop (LREC-COLING) 2024
[10] Playing the Part of the Sharp Bully: Generating Adversarial Examples for Implicit Hate Speech Detection, Ocampo et al., ACL Findings 2023
[11] An in-depth analysis of implicit and subtle hate speech messages, Ocampo et al. EACL 2023.
[12] Latent Hatred: A Benchmark for Understanding Implicit Hate Speech, ELSherief et al., EMNLP 2021.
[13] Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection, Zang et al., ACL 2024
Please note that applicants must be registered students at a university or other academic institution and that this establishment will need to sign an 'Internship Convention' with NAVER LABS Europe before the student is accepted.
You can apply for this position online. Don't forget to upload your CV and cover letter before you submit. Incomplete applications will not be accepted.
NAVER is the #1 Internet portal in Korea with activities that span a wide range of businesses including search, commerce, content, financial and cloud platforms.
NAVER LABS, co-located in Korea and France, is the organization dedicated to preparing NAVER’s future. NAVER LABS Europe is located in a spectacular setting in Grenoble, in the heart of the French Alps. Scientists at NAVER LABS Europe are empowered to pursue long-term research problems that, if successful, can have significant impact and transform NAVER. We take our ideas as far as research can to create the best technology of its kind. Active participation in the academic community and collaborations with world-class public research groups are, among others, important tools to achieve these goals. Teamwork, focus and persistence are important values for us.
NAVER LABS Europe is an equal opportunity employer.
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
1. Difference in female/male salary: 34/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: Non calculable
4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
——————-
Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
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Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
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