Reasoning, Robustness, and Human Feedback in AI - Max Bartolo (Cohere)
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/commandTRANSCRIPT:https://www.dropbox.com/scl/fi/vujxscaffw37pqgb6hpie/MAXB.pdf?rlkey=0oqjxs5u49eqa2m7uaol64lbw&dl=0TOC: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 Limitations2. 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 Robustness3. 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 Tools4. 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 Challenges5. Future Directions and Challenges [01:13:48] 5.1 Context Window Evolution and Trade-offs [01:22:47] 5.2 Enterprise Applications and Future ChallengesREFS:[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 & Lianghttps://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 Hartmannhttps://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 AIhttps://huggingface.co/CohereForAI/c4ai-command-a-03-2025[00:13:30] OpenInterpreterhttps://github.com/KillianLucas/open-interpreter[00:16:15] Human Feedback is not Gold Standard, Tom Hosking, Max Bartolo, Phil Blunsomhttps://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 Madryhttps://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 Hookerhttps://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 Chollethttps://github.com/fchollet/ARC-AGI[01:15:50] Command A, Coherehttps://cohere.com/blog/command-a[01:22:55] Enterprise search using LLMs, Coherehttps://cohere.com/blog/commonly-asked-questions-about-search-from-coheres-enterprise-customers
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