1 00:00:00,000 --> 00:00:03,280 Hey everyone and welcome back to AI Papers podcast daily. 2 00:00:03,280 --> 00:00:07,120 So today we're gonna be taking a deep dive into a paper 3 00:00:07,120 --> 00:00:11,480 that's tackling a really, really interesting challenge. 4 00:00:11,480 --> 00:00:14,040 How to make AI think more logically. 5 00:00:14,040 --> 00:00:15,040 Right. 6 00:00:15,040 --> 00:00:17,240 You've probably heard all the buzz about LLMs. 7 00:00:17,240 --> 00:00:18,080 Yeah. 8 00:00:18,080 --> 00:00:21,760 Large language models being amazing at like writing stories, 9 00:00:21,760 --> 00:00:23,160 translating languages, all that. 10 00:00:23,160 --> 00:00:25,200 Yeah, they're great at all that stuff. 11 00:00:25,200 --> 00:00:28,240 But logic puzzles, not so much, right? 12 00:00:28,240 --> 00:00:29,080 Yeah, that's right. 13 00:00:29,080 --> 00:00:33,120 LLMs, they often rely on just recognizing patterns 14 00:00:33,120 --> 00:00:35,840 in the data, you know, kind of like finding trends. 15 00:00:35,840 --> 00:00:39,480 But when it comes to actual reasoning, they can hit a wall. 16 00:00:39,480 --> 00:00:41,680 Yeah, and that is where this paper comes in. 17 00:00:41,680 --> 00:00:42,520 Okay. 18 00:00:42,520 --> 00:00:44,960 It introduces this super fascinating concept 19 00:00:44,960 --> 00:00:46,840 called critical questions of thought. 20 00:00:46,840 --> 00:00:47,680 Okay. 21 00:00:47,680 --> 00:00:48,520 Or CQOT for short. 22 00:00:48,520 --> 00:00:49,360 Okay. 23 00:00:49,360 --> 00:00:52,320 And it's all about giving LLMs a structured way 24 00:00:52,320 --> 00:00:54,720 to actually like question their own logic. 25 00:00:54,720 --> 00:00:55,560 Interesting. 26 00:00:55,560 --> 00:00:58,280 So we're looking at the work of Federico Castagna. 27 00:00:58,280 --> 00:01:00,800 Isbel Sassoon and Simon Parsons. 28 00:01:00,800 --> 00:01:04,360 And their paper is called Critical Questions of Thought. 29 00:01:04,360 --> 00:01:07,920 Steering LLM reasoning with argumentative querying. 30 00:01:07,920 --> 00:01:09,040 Catchy title. 31 00:01:09,040 --> 00:01:09,880 I know, right? 32 00:01:09,880 --> 00:01:10,720 Yeah. 33 00:01:10,720 --> 00:01:12,880 So what's really interesting is that they draw inspiration 34 00:01:12,880 --> 00:01:14,440 from argumentation theory. 35 00:01:14,440 --> 00:01:15,280 Mm-hmm. 36 00:01:15,280 --> 00:01:17,320 Like, you know how we build and analyze arguments 37 00:01:17,320 --> 00:01:18,240 like in a debate? 38 00:01:18,240 --> 00:01:19,080 Oh, interesting. 39 00:01:19,080 --> 00:01:20,920 So instead of just like giving an answer, 40 00:01:20,920 --> 00:01:23,440 the AI actually has to justify its reasoning. 41 00:01:23,440 --> 00:01:24,280 Okay. 42 00:01:24,280 --> 00:01:26,160 Like a lawyer presenting a case. 43 00:01:26,160 --> 00:01:27,120 Exactly. 44 00:01:27,120 --> 00:01:29,880 The paper uses this model of argumentation 45 00:01:29,880 --> 00:01:32,520 developed by Stephen Tolman. 46 00:01:32,520 --> 00:01:33,360 Okay. 47 00:01:33,360 --> 00:01:37,040 And Tolman argued that a strong argument 48 00:01:37,040 --> 00:01:39,240 needs more than just a claim. 49 00:01:39,240 --> 00:01:42,320 It needs evidence warrants to connect that evidence 50 00:01:42,320 --> 00:01:43,160 to the claim. 51 00:01:43,160 --> 00:01:44,000 Okay. 52 00:01:44,000 --> 00:01:45,120 Backing to support those warrants. 53 00:01:45,120 --> 00:01:47,800 And even the ability to address rebuttals. 54 00:01:47,800 --> 00:01:48,640 Wow. 55 00:01:48,640 --> 00:01:52,360 So CQOT applies the same rigor to AI. 56 00:01:52,360 --> 00:01:53,200 Yeah. 57 00:01:53,200 --> 00:01:54,440 Essentially forcing it to think through 58 00:01:54,440 --> 00:01:55,640 each step of its logic. 59 00:01:55,640 --> 00:01:56,480 That's so cool. 60 00:01:56,480 --> 00:01:59,040 So how does this actually work like in practice? 61 00:01:59,040 --> 00:01:59,880 Right, we're all. 62 00:01:59,880 --> 00:02:02,560 Give the AI like a textbook on argumentation. 63 00:02:02,560 --> 00:02:04,000 Well, not quite. 64 00:02:04,000 --> 00:02:07,560 The researchers created this pipeline with four key steps. 65 00:02:07,560 --> 00:02:08,400 Okay. 66 00:02:08,400 --> 00:02:11,600 First, the LLM, it creates a reasoning plan for the problem. 67 00:02:11,600 --> 00:02:12,440 Got it. 68 00:02:12,440 --> 00:02:14,400 Brings it down into premises and conclusions, 69 00:02:14,400 --> 00:02:17,480 kind of like outlining an essay before you start writing. 70 00:02:17,480 --> 00:02:19,040 So the AI is saying, 71 00:02:19,040 --> 00:02:21,040 okay, here's how I'm gonna tackle this problem 72 00:02:21,040 --> 00:02:21,960 step by step. 73 00:02:21,960 --> 00:02:22,800 Precisely. 74 00:02:22,800 --> 00:02:23,640 Cool. 75 00:02:23,640 --> 00:02:25,000 Then it gets really interesting in the second step. 76 00:02:25,000 --> 00:02:25,840 Okay. 77 00:02:25,840 --> 00:02:28,400 The AI actually probes its own reasoning. 78 00:02:28,400 --> 00:02:29,240 Really? 79 00:02:29,240 --> 00:02:30,920 Using eight critical questions. 80 00:02:30,920 --> 00:02:31,760 Wow. 81 00:02:31,760 --> 00:02:33,360 Inspired by Tullman's model. 82 00:02:33,360 --> 00:02:34,200 Okay. 83 00:02:34,200 --> 00:02:37,160 And these questions are designed to expose any flaws 84 00:02:37,160 --> 00:02:38,720 in the AI's logic. 85 00:02:38,720 --> 00:02:41,560 So like a self-check to make sure it's not making any leaps 86 00:02:41,560 --> 00:02:42,400 in its thinking. 87 00:02:42,400 --> 00:02:43,240 Exactly. 88 00:02:43,240 --> 00:02:47,320 Like, you know, are the premises supported by evidence? 89 00:02:47,320 --> 00:02:48,160 Uh-huh. 90 00:02:48,160 --> 00:02:50,760 Or does the reasoning avoid logical fallacies? 91 00:02:50,760 --> 00:02:51,600 Right. 92 00:02:51,600 --> 00:02:52,440 And then step three, 93 00:02:52,440 --> 00:02:53,920 the AI actually checks its answers 94 00:02:53,920 --> 00:02:55,280 to those critical questions. 95 00:02:55,280 --> 00:02:55,620 Right. 96 00:02:55,620 --> 00:02:58,160 And if its reasoning didn't quite pass muster, 97 00:02:58,160 --> 00:02:58,720 Yeah. 98 00:02:58,720 --> 00:03:00,160 it goes back to step one, 99 00:03:00,160 --> 00:03:02,560 revises its plan and tries again. 100 00:03:02,560 --> 00:03:03,360 Oh, wow. 101 00:03:03,360 --> 00:03:06,360 It can actually go through this cycle up to 11 times. 102 00:03:06,360 --> 00:03:07,640 That's wild. 103 00:03:07,640 --> 00:03:11,720 So it's like the AI is having an internal debate with itself. 104 00:03:11,720 --> 00:03:12,440 Yeah. 105 00:03:12,440 --> 00:03:13,920 Challenging its own assumptions. 106 00:03:13,920 --> 00:03:14,800 It really is. 107 00:03:14,800 --> 00:03:15,200 Oh, yeah. 108 00:03:15,200 --> 00:03:18,040 And only once it's satisfied with its reasoning 109 00:03:18,040 --> 00:03:20,560 does it move to step four and give its final answer. 110 00:03:20,560 --> 00:03:21,080 Okay. 111 00:03:21,080 --> 00:03:23,360 So by going through this whole process. 112 00:03:23,360 --> 00:03:23,800 Right. 113 00:03:23,800 --> 00:03:25,920 The AI is much more likely to arrive 114 00:03:25,920 --> 00:03:27,840 at a logically sound conclusion. 115 00:03:27,840 --> 00:03:28,280 Yeah. 116 00:03:28,280 --> 00:03:30,240 Even for really tricky problems. 117 00:03:30,240 --> 00:03:30,520 OK. 118 00:03:30,520 --> 00:03:33,720 So we've given the AI this rigorous training in logic. 119 00:03:33,720 --> 00:03:34,280 Right. 120 00:03:34,280 --> 00:03:36,600 But how do we know if it actually works? 121 00:03:36,600 --> 00:03:38,440 That's where the researchers got really creative. 122 00:03:38,440 --> 00:03:38,800 OK. 123 00:03:38,800 --> 00:03:43,080 They tested CQOT on a variety of LLMs, 124 00:03:43,080 --> 00:03:46,560 both open source models like Lama and Nimitron, 125 00:03:46,560 --> 00:03:50,000 and some of the big names in proprietary AI like Gemini, 126 00:03:50,000 --> 00:03:52,360 GPT4O, and Claude. 127 00:03:52,360 --> 00:03:53,920 So a real AI showdown. 128 00:03:53,920 --> 00:03:54,320 Yeah. 129 00:03:54,320 --> 00:03:55,200 It was quite a showdown. 130 00:03:55,200 --> 00:03:57,400 What kind of tasks did they use to test them? 131 00:03:57,400 --> 00:04:00,200 Well, they use a benchmark called MTBench, 132 00:04:00,200 --> 00:04:02,520 which is specifically designed to challenge AI 133 00:04:02,520 --> 00:04:05,160 with these complex reasoning and math problems. 134 00:04:05,160 --> 00:04:05,560 Gotcha. 135 00:04:05,560 --> 00:04:08,360 Think of brain teasers that would make even a sweat a little. 136 00:04:08,360 --> 00:04:08,800 OK. 137 00:04:08,800 --> 00:04:09,040 Yeah. 138 00:04:09,040 --> 00:04:09,680 I'm intrigued. 139 00:04:09,680 --> 00:04:11,760 And the results. 140 00:04:11,760 --> 00:04:13,120 I'm on the edge of my seat here. 141 00:04:13,120 --> 00:04:14,040 We'll get ready for this. 142 00:04:14,040 --> 00:04:18,240 CQOT significantly improved the performance of all these LLMs 143 00:04:18,240 --> 00:04:19,120 across the board. 144 00:04:19,120 --> 00:04:19,720 Really? 145 00:04:19,720 --> 00:04:21,640 And here's the real kicker. 146 00:04:21,640 --> 00:04:26,400 They achieved the best performance in 18 out of 20 test cases. 147 00:04:26,400 --> 00:04:26,720 Wow. 148 00:04:26,720 --> 00:04:27,520 That's impressive. 149 00:04:27,520 --> 00:04:28,880 It is pretty remarkable. 150 00:04:28,880 --> 00:04:31,800 So this CQOT method is really making a difference. 151 00:04:31,800 --> 00:04:31,960 Yeah. 152 00:04:31,960 --> 00:04:33,040 It's making a big difference. 153 00:04:33,040 --> 00:04:33,600 But hold on. 154 00:04:33,600 --> 00:04:37,040 Didn't you say they tested this on both open source 155 00:04:37,040 --> 00:04:38,360 and proprietary models? 156 00:04:38,360 --> 00:04:39,200 I did, yeah. 157 00:04:39,200 --> 00:04:41,320 How did they compare, like head to head? 158 00:04:41,320 --> 00:04:43,080 Oh, that's where it gets even more interesting. 159 00:04:43,080 --> 00:04:43,480 OK. 160 00:04:43,480 --> 00:04:45,040 One of the most remarkable findings 161 00:04:45,040 --> 00:04:49,960 was that the open source LLAMA model, when enhanced with CQOT, 162 00:04:49,960 --> 00:04:53,320 actually outperformed the baseline performance 163 00:04:53,320 --> 00:04:57,320 of the much larger and more complex GPT-4O. 164 00:04:57,320 --> 00:04:59,640 Wait, so a smaller, more accessible model 165 00:04:59,640 --> 00:05:01,200 beat out one of the industry giants? 166 00:05:01,200 --> 00:05:02,080 Yeah, that's right. 167 00:05:02,080 --> 00:05:03,080 That's a game changer. 168 00:05:03,080 --> 00:05:04,000 It's huge. 169 00:05:04,000 --> 00:05:04,480 Wow. 170 00:05:04,480 --> 00:05:06,920 And to really make sure that the critical questions were 171 00:05:06,920 --> 00:05:09,320 the key factor driving this improvement. 172 00:05:09,320 --> 00:05:09,600 Right. 173 00:05:09,600 --> 00:05:12,040 The researchers even did an ablation study. 174 00:05:12,040 --> 00:05:12,440 OK. 175 00:05:12,440 --> 00:05:14,400 Remove the questions from the process. 176 00:05:14,400 --> 00:05:14,560 Yeah. 177 00:05:14,560 --> 00:05:15,320 And guess what? 178 00:05:15,320 --> 00:05:17,680 The performance dropped significantly. 179 00:05:17,680 --> 00:05:20,360 So the questions really are the secret sauce here. 180 00:05:20,360 --> 00:05:21,560 They really are. 181 00:05:21,560 --> 00:05:26,720 We're giving AI the tools to not just solve problems, 182 00:05:26,720 --> 00:05:30,240 but also to think critically about its own solution. 183 00:05:30,240 --> 00:05:31,440 That's exactly right. 184 00:05:31,440 --> 00:05:32,920 This is seriously cool stuff. 185 00:05:32,920 --> 00:05:33,960 It is really cool stuff. 186 00:05:33,960 --> 00:05:36,120 But before we get too carried away, 187 00:05:36,120 --> 00:05:37,360 what about the limitations? 188 00:05:37,360 --> 00:05:38,160 Right. 189 00:05:38,160 --> 00:05:40,600 Is CQOT like a silver bullet? 190 00:05:40,600 --> 00:05:42,640 Well, not quite. 191 00:05:42,640 --> 00:05:43,560 OK. 192 00:05:43,560 --> 00:05:47,160 While CQOT shows this incredible promise, 193 00:05:47,160 --> 00:05:49,360 there are some things to consider. 194 00:05:49,360 --> 00:05:51,400 Even with the sophisticated approach, 195 00:05:51,400 --> 00:05:55,280 AI is still limited by the data it's been trained on. 196 00:05:55,280 --> 00:05:57,880 If it hasn't encountered certain concepts or types 197 00:05:57,880 --> 00:06:01,160 of reasoning before, it's going to struggle. 198 00:06:01,160 --> 00:06:04,000 It's like giving someone the best study guide in the world. 199 00:06:04,000 --> 00:06:06,560 But if they haven't attended class or read the textbook, 200 00:06:06,560 --> 00:06:08,360 they're not going to ace the test. 201 00:06:08,360 --> 00:06:08,600 Right. 202 00:06:08,600 --> 00:06:10,000 It needs that foundational knowledge. 203 00:06:10,000 --> 00:06:10,800 Exactly. 204 00:06:10,800 --> 00:06:11,080 OK. 205 00:06:11,080 --> 00:06:11,920 So that's one thing. 206 00:06:11,920 --> 00:06:12,320 Yeah. 207 00:06:12,320 --> 00:06:15,240 And also, this approach can be more time consuming 208 00:06:15,240 --> 00:06:17,760 than simpler approaches to AI reasoning. 209 00:06:17,760 --> 00:06:20,480 Because the AI is going through these multiple rounds 210 00:06:20,480 --> 00:06:23,880 of self-evaluation and revision, it can take longer 211 00:06:23,880 --> 00:06:25,560 to arrive at a final answer. 212 00:06:25,560 --> 00:06:27,760 So there's a trade-off between speed and accuracy. 213 00:06:27,760 --> 00:06:28,920 Exactly. 214 00:06:28,920 --> 00:06:30,800 And remember, this research was only 215 00:06:30,800 --> 00:06:33,080 tested on a limited set of LLMs. 216 00:06:33,080 --> 00:06:33,480 Right. 217 00:06:33,480 --> 00:06:36,480 So we need more research to see how CQOT performs 218 00:06:36,480 --> 00:06:39,160 on other models, especially smaller ones 219 00:06:39,160 --> 00:06:40,360 with fewer parameters. 220 00:06:40,360 --> 00:06:41,160 That makes sense. 221 00:06:41,160 --> 00:06:41,680 Yeah. 222 00:06:41,680 --> 00:06:43,440 It sounds like there's still a lot to explore. 223 00:06:43,440 --> 00:06:44,520 Oh, absolutely. 224 00:06:44,520 --> 00:06:46,080 This is just the beginning of the journey. 225 00:06:46,080 --> 00:06:47,520 Yeah, just the beginning. 226 00:06:47,520 --> 00:06:49,280 We've covered a lot of ground already. 227 00:06:49,280 --> 00:06:51,320 But before we dive into the deeper implications 228 00:06:51,320 --> 00:06:54,520 of this research, I think it's time for a quick break. 229 00:06:54,520 --> 00:06:55,640 Oh, it's OK. 230 00:06:55,640 --> 00:06:58,000 When we come back, we'll unpack what these findings mean 231 00:06:58,000 --> 00:07:01,200 for the future of AI and how this research could impact 232 00:07:01,200 --> 00:07:04,520 the way we think about building truly intelligent machines. 233 00:07:04,520 --> 00:07:05,640 Stay with us. 234 00:07:05,640 --> 00:07:06,000 All right. 235 00:07:06,000 --> 00:07:08,040 See you in a bit. 236 00:07:08,040 --> 00:07:11,080 Welcome back to AI Papers podcast daily. 237 00:07:11,080 --> 00:07:12,960 Before the break, we were just getting into this, 238 00:07:12,960 --> 00:07:16,680 like, groundbreaking paper on critical questions of thought 239 00:07:16,680 --> 00:07:18,920 or CQOT and how it's kind of pushing 240 00:07:18,920 --> 00:07:20,520 the boundaries of AI reasoning. 241 00:07:20,520 --> 00:07:21,920 Yeah, it's really exciting stuff. 242 00:07:21,920 --> 00:07:25,440 You know, giving LLMs that structured way 243 00:07:25,440 --> 00:07:27,240 to question their own logic. 244 00:07:27,240 --> 00:07:27,760 Yeah. 245 00:07:27,760 --> 00:07:30,000 We're seeing some really significant improvements 246 00:07:30,000 --> 00:07:32,960 in their ability to tackle these complex problems. 247 00:07:32,960 --> 00:07:35,040 Yeah, we talked about the impressive results 248 00:07:35,040 --> 00:07:36,280 that they got on the MT bench. 249 00:07:36,280 --> 00:07:36,760 Right. 250 00:07:36,760 --> 00:07:38,960 But I'm curious, like, what are the implications 251 00:07:38,960 --> 00:07:42,520 of this research for the broader AI landscape? 252 00:07:42,520 --> 00:07:47,360 Like, what does CQOT mean for the future of AI development? 253 00:07:47,360 --> 00:07:49,600 Well, I think one of the most significant implications 254 00:07:49,600 --> 00:07:53,440 is the potential for CQOT to make AI systems more 255 00:07:53,440 --> 00:07:55,960 transparent and trustworthy. 256 00:07:55,960 --> 00:08:00,600 So by forcing LLMs to justify their reasoning step by step, 257 00:08:00,600 --> 00:08:03,440 we can kind of get a glimpse into their thought process. 258 00:08:03,440 --> 00:08:07,200 And that can help us identify potential biases, errors, 259 00:08:07,200 --> 00:08:10,160 or areas where the AI needs additional training. 260 00:08:10,160 --> 00:08:13,080 So it's like having a window into the AI's mind. 261 00:08:13,080 --> 00:08:13,840 Exactly. 262 00:08:13,840 --> 00:08:16,240 Which is crucial for building confidence in its decisions. 263 00:08:16,240 --> 00:08:16,840 Absolutely. 264 00:08:16,840 --> 00:08:18,640 Especially as AI becomes more integrated 265 00:08:18,640 --> 00:08:19,960 into our everyday lives. 266 00:08:19,960 --> 00:08:20,560 Exactly. 267 00:08:20,560 --> 00:08:22,800 That transparency is going to be essential. 268 00:08:22,800 --> 00:08:23,280 Yeah. 269 00:08:23,280 --> 00:08:26,880 Like, we need to be able to understand how AI arrives 270 00:08:26,880 --> 00:08:29,840 at its conclusion, especially when those conclusions have 271 00:08:29,840 --> 00:08:31,400 real-world consequences. 272 00:08:31,400 --> 00:08:33,920 Yeah, especially in fields like health care or finance. 273 00:08:33,920 --> 00:08:35,200 Right, where the stakes are so high. 274 00:08:35,200 --> 00:08:36,360 Exactly where the stakes are high. 275 00:08:36,360 --> 00:08:38,640 OK, so transparency, that's a big one. 276 00:08:38,640 --> 00:08:41,000 Yeah, and another exciting implication, I think, 277 00:08:41,000 --> 00:08:45,200 is the potential for CQOT to actually democratize 278 00:08:45,200 --> 00:08:48,760 access to these advanced AI capabilities. 279 00:08:48,760 --> 00:08:49,800 OK, how so? 280 00:08:49,800 --> 00:08:53,280 Well, remember how we talked about the open source LLMA model? 281 00:08:53,280 --> 00:08:53,640 Right. 282 00:08:53,640 --> 00:08:56,440 When combined with CQOT, actually outperformed 283 00:08:56,440 --> 00:08:58,320 the much larger GPC4O? 284 00:08:58,320 --> 00:08:59,760 Yeah, that was a real shocker. 285 00:08:59,760 --> 00:09:00,960 Yeah, it was surprising. 286 00:09:00,960 --> 00:09:06,000 It suggests that CQOT can unlock the potential of AI models 287 00:09:06,000 --> 00:09:10,320 that are accessible to a wider range of researchers 288 00:09:10,320 --> 00:09:13,880 and developers, not just those with massive resources. 289 00:09:13,880 --> 00:09:15,920 Right, so it could really accelerate innovation 290 00:09:15,920 --> 00:09:16,640 in the field. 291 00:09:16,640 --> 00:09:17,600 Yeah, I get it. 292 00:09:17,600 --> 00:09:19,560 Imagine smaller teams and startups, 293 00:09:19,560 --> 00:09:21,960 what they could achieve with access 294 00:09:21,960 --> 00:09:24,720 to those really powerful reasoning capabilities. 295 00:09:24,720 --> 00:09:27,720 Yeah, that's what's exciting to think about. 296 00:09:27,720 --> 00:09:29,920 And then there's the potential for CQOT 297 00:09:29,920 --> 00:09:33,360 to inspire new ways of thinking about human learning 298 00:09:33,360 --> 00:09:34,400 and reasoning. 299 00:09:34,400 --> 00:09:35,480 Ooh, that's interesting. 300 00:09:35,480 --> 00:09:40,480 Yeah, if AI can benefit from this critical self-questioning, 301 00:09:40,480 --> 00:09:41,520 perhaps we can too. 302 00:09:41,520 --> 00:09:42,480 I love that idea. 303 00:09:42,480 --> 00:09:44,840 Maybe we should all adopt a bit of that CQOT mindset 304 00:09:44,840 --> 00:09:45,480 in our own lives. 305 00:09:45,480 --> 00:09:47,040 Yeah, I think we could all benefit 306 00:09:47,040 --> 00:09:49,560 from being a bit more critical of our own assumptions 307 00:09:49,560 --> 00:09:50,360 and thought processes. 308 00:09:50,360 --> 00:09:52,200 For sure, for sure. 309 00:09:52,200 --> 00:09:55,080 OK, so we've talked a lot about the potential 310 00:09:55,080 --> 00:09:56,440 upsides of CQOT. 311 00:09:56,440 --> 00:09:57,000 Right. 312 00:09:57,000 --> 00:09:58,360 But what about the challenges? 313 00:09:58,360 --> 00:10:01,440 Are there any roadblocks we need to consider? 314 00:10:01,440 --> 00:10:04,840 Well, of course, no technology is without its limitations. 315 00:10:04,840 --> 00:10:05,760 Right. 316 00:10:05,760 --> 00:10:08,720 One challenge is that the CQOT pipeline 317 00:10:08,720 --> 00:10:11,520 can be more time consuming than simpler approaches 318 00:10:11,520 --> 00:10:12,600 to AI reasoning. 319 00:10:12,600 --> 00:10:13,100 OK. 320 00:10:13,100 --> 00:10:15,600 Because the AI is going through those multiple rounds 321 00:10:15,600 --> 00:10:19,160 of self-evaluation and revision, can take longer 322 00:10:19,160 --> 00:10:21,040 to arrive at that final answer. 323 00:10:21,040 --> 00:10:23,720 So there's that trade-off between accuracy and speed. 324 00:10:23,720 --> 00:10:24,440 Exactly. 325 00:10:24,440 --> 00:10:27,760 And remember, CQOT relies on the AI having access 326 00:10:27,760 --> 00:10:29,560 to relevant knowledge. 327 00:10:29,560 --> 00:10:33,120 So if it hasn't been exposed to the necessary information 328 00:10:33,120 --> 00:10:36,120 or concepts, it won't be able to reason effectively, 329 00:10:36,120 --> 00:10:38,480 even with those critical questions, guiding it. 330 00:10:38,480 --> 00:10:40,880 Like trying to solve a puzzle, but you're missing some pieces. 331 00:10:40,880 --> 00:10:41,600 Precisely. 332 00:10:41,600 --> 00:10:42,040 OK. 333 00:10:42,040 --> 00:10:43,880 And also, I think we touched upon this earlier. 334 00:10:43,880 --> 00:10:47,360 But this research was only tested on a limited set of LLMs. 335 00:10:47,360 --> 00:10:48,200 Right. 336 00:10:48,200 --> 00:10:52,920 We need more research to see how CQOT performs on a wider range 337 00:10:52,920 --> 00:10:55,280 of models and in different contexts. 338 00:10:55,280 --> 00:10:56,280 Makes sense. 339 00:10:56,280 --> 00:10:58,920 So it sounds like there's still a lot to explore, 340 00:10:58,920 --> 00:11:01,480 but the initial findings are really promising. 341 00:11:01,480 --> 00:11:02,120 Absolutely. 342 00:11:02,120 --> 00:11:05,120 CQOT represents this significant step 343 00:11:05,120 --> 00:11:08,360 towards developing AI systems that are not just intelligent, 344 00:11:08,360 --> 00:11:13,120 but also transparent and trustworthy and accessible. 345 00:11:13,120 --> 00:11:16,600 It's paving the way for a future where AI can be a more 346 00:11:16,600 --> 00:11:19,800 reliable and collaborative partner in problem solving. 347 00:11:19,800 --> 00:11:21,960 It's a really exciting time to be following 348 00:11:21,960 --> 00:11:23,160 the developments in AI. 349 00:11:23,160 --> 00:11:23,840 It really is. 350 00:11:23,840 --> 00:11:26,160 We've talked about the potential, the challenges, 351 00:11:26,160 --> 00:11:28,200 and the implications of this research. 352 00:11:28,200 --> 00:11:30,960 But before we wrap things up, I kind of 353 00:11:30,960 --> 00:11:33,960 want to hear more about the specific results 354 00:11:33,960 --> 00:11:35,560 the researchers actually achieved. 355 00:11:35,560 --> 00:11:36,040 OK. 356 00:11:36,040 --> 00:11:37,960 So when we come back, we'll delve into the data 357 00:11:37,960 --> 00:11:40,080 and explore what these findings tell us 358 00:11:40,080 --> 00:11:42,920 about the effectiveness of CQOT in action. 359 00:11:42,920 --> 00:11:43,680 Sounds good. 360 00:11:43,680 --> 00:11:44,960 Stay tuned. 361 00:11:44,960 --> 00:11:47,880 Welcome back to AI Papers podcast daily. 362 00:11:47,880 --> 00:11:50,000 We've been kind of geeking out over this research 363 00:11:50,000 --> 00:11:53,520 on critical questions of thought or CQOT. 364 00:11:53,520 --> 00:11:54,720 It's really cool stuff. 365 00:11:54,720 --> 00:11:55,720 It is, right. 366 00:11:55,720 --> 00:11:58,320 And how it's like enhancing AI reasoning 367 00:11:58,320 --> 00:12:00,760 by giving these LLMs a structured way 368 00:12:00,760 --> 00:12:03,200 to critique their own logic. 369 00:12:03,200 --> 00:12:05,480 Yeah, force them to think a bit more carefully. 370 00:12:05,480 --> 00:12:06,400 Exactly. 371 00:12:06,400 --> 00:12:08,840 So let's get into the nitty gritty of the results. 372 00:12:08,840 --> 00:12:09,880 All right, let's dive in. 373 00:12:09,880 --> 00:12:11,000 What are the researchers actually 374 00:12:11,000 --> 00:12:13,760 fine when they put CQOT to the test? 375 00:12:13,760 --> 00:12:15,800 Well, the results were really compelling. 376 00:12:15,800 --> 00:12:17,600 Remember that MT bench we talked about, 377 00:12:17,600 --> 00:12:20,680 that set of challenging reasoning and math problems? 378 00:12:20,680 --> 00:12:21,200 Yeah. 379 00:12:21,200 --> 00:12:23,640 Well, across almost all the tests, 380 00:12:23,640 --> 00:12:25,720 the LLMs that were equipped with CQOT 381 00:12:25,720 --> 00:12:28,800 consistently outperformed those baseline models, 382 00:12:28,800 --> 00:12:31,360 as well as the ones using other prompting methods. 383 00:12:31,360 --> 00:12:34,880 They actually saw the best performance in 18 out of 20 384 00:12:34,880 --> 00:12:35,760 test cases. 385 00:12:35,760 --> 00:12:36,120 Wow. 386 00:12:36,120 --> 00:12:39,000 So CQOT wasn't just like a slight edge. 387 00:12:39,000 --> 00:12:40,200 It was a clear advantage. 388 00:12:40,200 --> 00:12:41,240 It was a huge advantage. 389 00:12:41,240 --> 00:12:41,440 Yeah. 390 00:12:41,440 --> 00:12:42,200 That's amazing. 391 00:12:42,200 --> 00:12:43,720 And one of the most striking findings 392 00:12:43,720 --> 00:12:45,280 was that these gains weren't just 393 00:12:45,280 --> 00:12:47,840 limited to a single type of LLM. 394 00:12:47,840 --> 00:12:50,400 So we tested it on those open source models like Lama 395 00:12:50,400 --> 00:12:54,520 and Nemotron and the proprietary models like Gemini, 396 00:12:54,520 --> 00:12:56,680 GPC4O, and Claude. 397 00:12:56,680 --> 00:12:58,560 And across the board, they saw these marked 398 00:12:58,560 --> 00:13:00,920 improvements in reasoning accuracy. 399 00:13:00,920 --> 00:13:04,240 So it seems like CQOT could be like a widely applicable 400 00:13:04,240 --> 00:13:06,800 technique, not just like a niche solution. 401 00:13:06,800 --> 00:13:07,440 Yeah. 402 00:13:07,440 --> 00:13:08,920 That's what the data suggests. 403 00:13:08,920 --> 00:13:09,840 That's really cool. 404 00:13:09,840 --> 00:13:10,520 It is. 405 00:13:10,520 --> 00:13:13,240 And remember that surprising finding we talked about earlier, 406 00:13:13,240 --> 00:13:15,920 where the open source Lama model with CQOT 407 00:13:15,920 --> 00:13:20,080 actually outperformed the baseline of the much larger GPT4O. 408 00:13:20,080 --> 00:13:20,520 Yeah. 409 00:13:20,520 --> 00:13:21,400 That was crazy. 410 00:13:21,400 --> 00:13:22,320 It was pretty wild. 411 00:13:22,320 --> 00:13:24,000 It really challenges that whole idea 412 00:13:24,000 --> 00:13:25,320 that bigger is always better. 413 00:13:25,320 --> 00:13:25,720 Right. 414 00:13:25,720 --> 00:13:26,080 Exactly. 415 00:13:26,080 --> 00:13:27,240 When it comes to AI model. 416 00:13:27,240 --> 00:13:29,040 Yeah, it's not always about size. 417 00:13:29,040 --> 00:13:29,440 Wow. 418 00:13:29,440 --> 00:13:33,040 So to really kind of nail down that the critical questions 419 00:13:33,040 --> 00:13:35,400 were the key driver in this improvement. 420 00:13:35,400 --> 00:13:35,760 Yeah. 421 00:13:35,760 --> 00:13:37,800 The researchers did what's called an ablation study. 422 00:13:37,800 --> 00:13:38,800 That's right. 423 00:13:38,800 --> 00:13:41,880 And they removed the critical questions 424 00:13:41,880 --> 00:13:45,360 from the whole CQOT pipeline and ran the tests again. 425 00:13:45,360 --> 00:13:46,000 I did. 426 00:13:46,000 --> 00:13:47,760 And the performance plummeted. 427 00:13:47,760 --> 00:13:48,160 Wow. 428 00:13:48,160 --> 00:13:51,000 So those critical questions really are like the secret sauce. 429 00:13:51,000 --> 00:13:52,600 Yeah, they're the magic ingredient. 430 00:13:52,600 --> 00:13:56,080 They're what's pushing the LLMs to think more deeply 431 00:13:56,080 --> 00:13:56,800 and logically. 432 00:13:56,800 --> 00:13:57,840 Exactly. 433 00:13:57,840 --> 00:14:00,240 So you know this has some pretty exciting implications 434 00:14:00,240 --> 00:14:02,120 for the future of AI development. 435 00:14:02,120 --> 00:14:03,360 OK, like what? 436 00:14:03,360 --> 00:14:06,400 Imagine a world where AI isn't just this black box 437 00:14:06,400 --> 00:14:07,720 spitting out answers. 438 00:14:07,720 --> 00:14:08,040 Right. 439 00:14:08,040 --> 00:14:12,520 But it's like a transparent partner in problem solving. 440 00:14:12,520 --> 00:14:13,200 OK. 441 00:14:13,200 --> 00:14:15,960 Capable of explaining its reasoning in a way 442 00:14:15,960 --> 00:14:18,280 that we can understand and trust. 443 00:14:18,280 --> 00:14:19,760 That's a really powerful vision. 444 00:14:19,760 --> 00:14:20,360 It is. 445 00:14:20,360 --> 00:14:23,840 AI that can not only solve problems, but also teach us 446 00:14:23,840 --> 00:14:25,600 how it arrived at those solutions. 447 00:14:25,600 --> 00:14:26,120 Exactly. 448 00:14:26,120 --> 00:14:28,120 It could revolutionize so many fields 449 00:14:28,120 --> 00:14:31,480 like scientific discovery, medical diagnosis, even 450 00:14:31,480 --> 00:14:32,560 education. 451 00:14:32,560 --> 00:14:33,280 Absolutely. 452 00:14:33,280 --> 00:14:34,080 That's amazing. 453 00:14:34,080 --> 00:14:34,360 It is. 454 00:14:34,360 --> 00:14:35,760 It's a lot to be excited about. 455 00:14:35,760 --> 00:14:36,240 It is. 456 00:14:36,240 --> 00:14:36,600 It is. 457 00:14:36,600 --> 00:14:38,400 You know, of course, there's still work to be done. 458 00:14:38,400 --> 00:14:41,200 We need to explore how CQOT performs 459 00:14:41,200 --> 00:14:45,480 on a wider range of LLMs and develop strategies 460 00:14:45,480 --> 00:14:47,720 for mitigating that potential slowdown that 461 00:14:47,720 --> 00:14:50,360 comes with this more deliberative reasoning process. 462 00:14:50,360 --> 00:14:50,840 Yeah. 463 00:14:50,840 --> 00:14:52,440 Those are all important considerations. 464 00:14:52,440 --> 00:14:53,200 They are. 465 00:14:53,200 --> 00:14:57,120 But overall, this research paints a very optimistic picture 466 00:14:57,120 --> 00:14:59,240 of the future of AI reasoning. 467 00:14:59,240 --> 00:15:00,200 I think so. 468 00:15:00,200 --> 00:15:03,000 By giving LLMs the tools to think critically, 469 00:15:03,000 --> 00:15:04,680 we're not just making them smarter. 470 00:15:04,680 --> 00:15:07,560 We're making them more reliable, more transparent, 471 00:15:07,560 --> 00:15:08,960 more collaborative partners. 472 00:15:08,960 --> 00:15:09,880 Couldn't agree more. 473 00:15:09,880 --> 00:15:10,120 Yeah. 474 00:15:10,120 --> 00:15:11,760 This research is really like a testament 475 00:15:11,760 --> 00:15:14,440 to the power of human ingenuity. 476 00:15:14,440 --> 00:15:17,760 To not only build intelligent machines, 477 00:15:17,760 --> 00:15:20,920 but to teach those machines how to think more like us. 478 00:15:20,920 --> 00:15:21,600 Exactly. 479 00:15:21,600 --> 00:15:25,560 To build logic and rigor and a healthy dose of self-reflection. 480 00:15:25,560 --> 00:15:26,480 I love it. 481 00:15:26,480 --> 00:15:28,960 This has been a truly fascinating deep dive 482 00:15:28,960 --> 00:15:30,600 into the world of AI reasoning. 483 00:15:30,600 --> 00:15:32,000 Yeah, it has. 484 00:15:32,000 --> 00:15:34,680 Thanks for joining us on this journey of discovery. 485 00:15:34,680 --> 00:15:37,040 If you want to explore the research in more depth, 486 00:15:37,040 --> 00:15:39,240 be sure to check out the full paper and the code, which 487 00:15:39,240 --> 00:15:40,320 are linked in the show notes. 488 00:15:40,320 --> 00:15:41,280 Definitely check that out. 489 00:15:41,280 --> 00:15:44,200 And don't forget to subscribe to AI Papers Podcast daily 490 00:15:44,200 --> 00:15:46,440 for more exciting explorations into the cutting 491 00:15:46,440 --> 00:15:47,880 edge of AI research. 492 00:15:47,880 --> 00:15:49,000 We'll see you next time. 493 00:15:49,000 --> 00:15:51,760 Until next time.