Intro

Dr. Max Bartolo from Cohere discusses machine learning model development, evaluation, and robustness. Key topics include model reasoning, the DynaBench platform for dynamic benchmarking, data-centric AI development, model training challenges, and the limitations of human feedback mechanisms. The conversation also covers technical aspects like influence functions, model quantization, and the PRISM project.

Max Bartolo (Cohere):

https://www.maxbartolo.com/

https://cohere.com/command

TRANSCRIPT:

https://www.dropbox.com/scl/fi/vujxscaffw37pqgb6hpie/MAXB.pdf?rlkey=0oqjxs5u49eqa2m7uaol64lbw&dl=0

TOC:

1. Model Reasoning and Verification

[00:00:00] 1.1 Model Consistency and Reasoning Verification

[00:03:25] 1.2 Influence Functions and Distributed Knowledge Analysis

[00:10:28] 1.3 AI Application Development and Model Deployment

[00:14:24] 1.4 AI Alignment and Human Feedback Limitations

2. Evaluation and Bias Assessment

[00:20:15] 2.1 Human Evaluation Challenges and Factuality Assessment

[00:27:15] 2.2 Cultural and Demographic Influences on Model Behavior

[00:32:43] 2.3 Adversarial Examples and Model Robustness

3. Benchmarking Systems and Methods

[00:41:54] 3.1 DynaBench and Dynamic Benchmarking Approaches

[00:50:02] 3.2 Benchmarking Challenges and Alternative Metrics

[00:50:33] 3.3 Evolution of Model Benchmarking Methods

[00:51:15] 3.4 Hierarchical Capability Testing Framework

[00:52:35] 3.5 Benchmark Platforms and Tools

4. Model Architecture and Performance

[00:55:15] 4.1 Cohere's Model Development Process

[01:00:26] 4.2 Model Quantization and Performance Evaluation

[01:05:18] 4.3 Reasoning Capabilities and Benchmark Standards

[01:08:27] 4.4 Training Progression and Technical Challenges

5. Future Directions and Challenges

[01:13:48] 5.1 Context Window Evolution and Trade-offs

[01:22:47] 5.2 Enterprise Applications and Future Challenges

REFS:

[00:03:10] Research at Cohere with Laura Ruis et al., Max Bartolo, Laura Ruis et al.

https://cohere.com/research/papers/procedural-knowledge-in-pretraining-drives-reasoning-in-large-language-models-2024-11-20

[00:04:15] Influence functions in machine learning, Koh & Liang

https://arxiv.org/abs/1703.04730

[00:08:05] Studying Large Language Model Generalization with Influence Functions, Roger Grosse et al.

https://storage.prod.researchhub.com/uploads/papers/2023/08/08/2308.03296.pdf

[00:11:10] The LLM ARChitect: Solving ARC-AGI Is A Matter of Perspective, Daniel Franzen, Jan Disselhoff, and David Hartmann

https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf

[00:12:10] Hugging Face model repo for C4AI Command A, Cohere and Cohere For AI

https://huggingface.co/CohereForAI/c4ai-command-a-03-2025

[00:13:30] OpenInterpreter

https://github.com/KillianLucas/open-interpreter

[00:16:15] Human Feedback is not Gold Standard, Tom Hosking, Max Bartolo, Phil Blunsom

https://arxiv.org/abs/2309.16349

[00:27:15] The PRISM Alignment Dataset, Hannah Kirk et al.

https://arxiv.org/abs/2404.16019

[00:32:50] How adversarial examples arise, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry

https://arxiv.org/abs/1905.02175

[00:43:00] DynaBench platform paper, Douwe Kiela et al.

https://aclanthology.org/2021.naacl-main.324.pdf

[00:50:15] Sara Hooker's work on compute limitations, Sara Hooker

https://arxiv.org/html/2407.05694v1

[00:53:25] DataPerf: Community-led benchmark suite, Mazumder et al.

https://arxiv.org/abs/2207.10062

[01:04:35] DROP, Dheeru Dua et al.

https://arxiv.org/abs/1903.00161

[01:07:05] GSM8k, Cobbe et al.

https://paperswithcode.com/sota/arithmetic-reasoning-on-gsm8k

[01:09:30] ARC, François Chollet

https://github.com/fchollet/ARC-AGI

[01:15:50] Command A, Cohere

https://cohere.com/blog/command-a

[01:22:55] Enterprise search using LLMs, Cohere

https://cohere.com/blog/commonly-asked-questions-about-search-from-coheres-enterprise-customers