Explain challenges in training multimodal LLMs
Machine Learning Discussion: Multimodal and Reasoning LLMs
This is a conceptual discussion; no code is required. Assume you are involved in training or adapting a multimodal large model, such as one combining text and image, or text and audio.
Part 1: Challenges in Multimodal Foundation Models
- Identify and elaborate on 1-2 of the biggest challenges encountered when training multimodal foundation models. Go deep into your chosen challenges.
Part 2: Reasoning-Focused LLMs vs. Standard Instruction/Chat LLMs
Compare and contrast "reasoning-focused LLMs" with "standard instruction/chat LLMs" across the following dimensions:
- Objectives and Training Data: What are the key differences in their primary objectives and the types of training data used?
- Inference Characteristics: How do their inference processes differ? Consider aspects like tool use, planning, and test-time compute requirements.
- Evaluation: How would you evaluate the reasoning quality and reliability of such models?
Be prepared to discuss practical trade-offs related to data, alignment, evaluation, cost/latency, and safety throughout your answers.
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Core dilemma
Integrating heterogeneous data modalities into a cohesive, performant model while simultaneously developing robust, verifiable reasoning capabilities that go beyond superficial pattern matching, all under significant computational and data constraints.
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Interview prep
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Explain challenges in training multimodal LLMs
Machine Learning Discussion: Multimodal and Reasoning LLMs
This is a conceptual discussion; no code is required. Assume you are involved in training or adapting a multimodal large model, such as one combining text and image, or text and audio.
Part 1: Challenges in Multimodal Foundation Models
- Identify and elaborate on 1-2 of the biggest challenges encountered when training multimodal foundation models. Go deep into your chosen challenges.
Part 2: Reasoning-Focused LLMs vs. Standard Instruction/Chat LLMs
Compare and contrast "reasoning-focused LLMs" with "standard instruction/chat LLMs" across the following dimensions:
- Objectives and Training Data: What are the key differences in their primary objectives and the types of training data used?
- Inference Characteristics: How do their inference processes differ? Consider aspects like tool use, planning, and test-time compute requirements.
- Evaluation: How would you evaluate the reasoning quality and reliability of such models?
Be prepared to discuss practical trade-offs related to data, alignment, evaluation, cost/latency, and safety throughout your answers.