utkuumur

5 ms·

1. 1.▲
   by utkuumur 14d ago

   Same here in the US.
2. 2.▲
   by utkuumur 3mo ago

   I think one problem is that the models that hallucinate often, a few times out of 8 or 16 so that they get good results on benchmarks, most of which measures success out of top k. From benchmark perspective, you don't really care wheth
3. 3.▲
   by utkuumur 2y ago

   I don't see why many people complaining on this issue. Not everyone mastered English unfortunately. I am especially very weak at writing a paper, and to be honest, find it taxing. I love research but after having results, turning it in
4. 4.▲
   by utkuumur 2y ago

   I couldn't agree more! What am I supposed to do with the related work section? Especially after reading many similar works in the field, it is very hard not to be influenced by what you read but you have to make sure not to say the sam
5. 5.▲
   by utkuumur 2y ago

   I believe it helps but not the sole reason. Because there are also autoregressive models that perform slightly worse. Unsupervised learning + Diffusion + Neural Search is the way to go in my opinion. However, currently, the literature lacks
6. 6.▲
   by utkuumur 2y ago

   There are recent papers based on diffusion that perform quite well. Here's an example of a recent paper https://arxiv.org/pdf/2406.01661 . I am also working on ML-based CO. My approach has a close 1% gap on hard in