Stage - Trustworthy Deep Learning: Llms Confidence
il y a 8 heures
Position description
**Category**:
- Information system
**Contract**:
- Internship
**Job title**:
- Stage - Trustworthy Deep Learning: LLMs Confidence Representation H/F
**Subject**:
- LLMs Confidence Representation
**Contract duration (months)**:
- 6
**Job description**:
**Context**
The List Institute at CEA Tech (CEA’s technological research division), dedicate its activities to driving innovation in intelligent digital systems. The specialized R&D programs aim to carry out technological developments of excellence in critical industry sectors and by partnering with key industry and academic actors.- Within the LIST Institute, at the heart of the Paris-Saclay Campus (Essonne), the Embedded and Autonomous Systems Design Laboratory (LSEA) works on methods and tools for the design & development of trustworthy autonomous systems that incorporate AI-based components. In particular, the LSEA’s Trustworthy Deep Learning (TDL) team conducts research on confidence (uncertainty) representation and monitoring in deep neural networks (DNNs) for computer vision tasks and automated robots.**Mission**
- In recent years, large language models (LLMs) have demonstrated remarkable proficiency in a wide variety of natural language processing tasks such as reasoning and question-answering. For this reason, they are increasingly deployed in real-world settings, including safety-critical domains such as medicine (medical diagnosis) and automated robots (that interact with humans). Unfortunately, LLMs have a tendency to “hallucinate,” i.e., produce predictions that are nonsensical or unfaithful while facing unfamiliar queries. This limitation hinders a wider adoption of LLMs within safety-critical domains as it is paramount for these models to provide or elicit a notion of trust in their predictions.**Internship Objectives**
- Study the State-of-the-Art methods for uncertainty estimation and hallucination detection in LLMs.
- Evaluate and compare common methods for uncertainty estimation and hallucination detection.
- Challenge existing methods by identifying vulnerabilities.
- Design improvements to existing methods.
**Methods / Means**:
- Python, PyTorch, VLMs-CLIP
**Applicant Profile**:
**What do we expect from you?**
- You are a 2nd year Master student (M2 - France).
- Proficiency in Python and PyTorch.
- Deep learning skills: LLMs (Mistral, Llama-3,...), VLMs (CLIP...).
In line with CEA's commitment to integrating people with disabilities, this job is open to all.Position location
**Site**:
- Other
**Job location**:
- France, Ile-de-France, Essonne (91)
**Location**:
- Palaiseau
**Languages**:
- English (Fluent)
**Prepared diploma**:
- Bac+5 - Master 2
**Recommended training**:
- Computer Science
**PhD opportunity**:
- Oui
Requester
**Position start date**:
- 29/11/2024
General information
**Organisation**:
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas:
- defence and security,
- nuclear energy (fission and fusion),
- technological research for industry,
- fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
**Reference **:2024-34496
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