Prof. Subbarao Kambhampati argues that while LLMs are impressive and useful tools, especially for creative tasks, they have fundamental limitations in logical reasoning and cannot provide guarantees about the correctness of their outputs. He advocates for hybrid approaches that combine LLMs with external verification systems. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. This is 2/13 of our #ICML2024 series TOC [00:00:00] Intro [00:02:06] Bio [00:03:02] LLMs are n-gram models on steroids [00:07:26] Is natural language a formal language? [00:08:34] Natural language is formal? [00:11:01] Do LLMs reason? [00:19:13] Definition of reasoning [00:31:40] Creativity in reasoning [00:50:27] Chollet's ARC challenge [01:01:31] Can we reason without verification? [01:10:00] LLMs cant solve some tasks [01:19:07] LLM Modulo framework [01:29:26] Future trends of architecture [01:34:48] Future research directions Pod: https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/Prof--Subbarao-Kambhampati---LLMs-dont-reason--they-memorize-ICML2024-213-e2mjcse Subbarao Kambhampati: https://x.com/rao2z Interviewer: Dr. Tim Scarfe Refs: Can LLMs Really Reason and Plan? https://cacm.acm.org/blogcacm/can-llms-really-reason-and-plan/ On the Planning Abilities of Large Language Models : A Critical Investigation https://arxiv.org/pdf/2305.15771 Chain of Thoughtlessness? An Analysis of CoT in Planning https://arxiv.org/pdf/2405.04776 On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks https://arxiv.org/pdf/2402.08115 LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks https://arxiv.org/pdf/2402.01817 Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve https://arxiv.org/pdf/2309.13638 https://arxiv.org/abs/2402.04210 "Task Success" is not Enough Faith and Fate: Limits of Transformers on Compositionality "finetuning multiplication with four digit numbers" (added after pub) https://arxiv.org/pdf/2305.18654 Partition function (number theory) (Srinivasa Ramanujan and G.H. Hardy's work) https://en.wikipedia.org/wiki/Partition_function_(number_theory) Poincaré conjecture https://en.wikipedia.org/wiki/Poincar%C3%A9_conjecture Gödel's incompleteness theorems https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_theorems ROT13 (Rotate13, "rotate by 13 places") https://en.wikipedia.org/wiki/ROT13 A Mathematical Theory of Communication (C. E. SHANNON) https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf Sparks of AGI https://arxiv.org/abs/2303.12712 Kambhampati thesis on speech recognition (1983) https://rakaposhi.eas.asu.edu/rao-btech-thesis.pdf PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change https://arxiv.org/abs/2206.10498 Explainable human-AI interaction https://link.springer.com/book/10.1007/978-3-031-03767-2 Tree of Thoughts https://arxiv.org/abs/2305.10601 On the Measure of Intelligence (ARC Challenge) https://arxiv.org/abs/1911.01547 Getting 50% (SoTA) on ARC-AGI with GPT-4o (Ryan Greenblatt ARC solution) https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt PROGRAMS WITH COMMON SENSE (John McCarthy) - "AI should be an advice taker program" https://www.cs.cornell.edu/selman/cs672/readings/mccarthy-upd.pdf Original chain of thought paper https://arxiv.org/abs/2201.11903 ICAPS 2024 Keynote: Dale Schuurmans on "Computing and Planning with Large Generative Models" (COT) https://www.youtube.com/watch?v=YnMqbpdHcaY The Hardware Lottery (Hooker) https://arxiv.org/abs/2009.06489 A Path Towards Autonomous Machine Intelligence (JEPA/LeCun) https://openreview.net/pdf?id=BZ5a1r-kVsf AlphaGeometry https://www.nature.com/articles/s41586-023-06747-5 FunSearch https://www.nature.com/articles/s41586-023-06924-6 Emergent Abilities of Large Language Models https://arxiv.org/abs/2206.07682 Language models are not naysayers (Negation in LLMs) https://arxiv.org/abs/2306.08189 The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" https://arxiv.org/abs/2309.12288 Embracing negative results https://openreview.net/forum?id=3RXAiU7sss