Sohee Yang
@soheeyang.bsky.social
PhD student/research scientist intern at UCL NLP/Google DeepMind (50/50 split). Previously MS at KAIST AI and research engineer at Naver Clova. #NLP #ML 👉 https://soheeyang.github.io/
Pinned
Sohee Yang
@soheeyang.bsky.social
· Jun 13
🚨 New Paper 🚨
How effectively do reasoning models reevaluate their thought? We find that:
- Models excel at identifying unhelpful thoughts but struggle to recover from them
- Smaller models can be more robust
- Self-reevaluation ability is far from true meta-cognitive awareness
1/N 🧵
How effectively do reasoning models reevaluate their thought? We find that:
- Models excel at identifying unhelpful thoughts but struggle to recover from them
- Smaller models can be more robust
- Self-reevaluation ability is far from true meta-cognitive awareness
1/N 🧵
🚨 New Paper 🚨
How effectively do reasoning models reevaluate their thought? We find that:
- Models excel at identifying unhelpful thoughts but struggle to recover from them
- Smaller models can be more robust
- Self-reevaluation ability is far from true meta-cognitive awareness
1/N 🧵
How effectively do reasoning models reevaluate their thought? We find that:
- Models excel at identifying unhelpful thoughts but struggle to recover from them
- Smaller models can be more robust
- Self-reevaluation ability is far from true meta-cognitive awareness
1/N 🧵
June 13, 2025 at 4:15 PM
🚨 New Paper 🚨
How effectively do reasoning models reevaluate their thought? We find that:
- Models excel at identifying unhelpful thoughts but struggle to recover from them
- Smaller models can be more robust
- Self-reevaluation ability is far from true meta-cognitive awareness
1/N 🧵
How effectively do reasoning models reevaluate their thought? We find that:
- Models excel at identifying unhelpful thoughts but struggle to recover from them
- Smaller models can be more robust
- Self-reevaluation ability is far from true meta-cognitive awareness
1/N 🧵
Reposted by Sohee Yang
How do LLMs learn to reason from data? Are they ~retrieving the answers from parametric knowledge🦜? In our new preprint, we look at the pretraining data and find evidence against this:
Procedural knowledge in pretraining drives LLM reasoning ⚙️🔢
🧵⬇️
Procedural knowledge in pretraining drives LLM reasoning ⚙️🔢
🧵⬇️
November 20, 2024 at 4:35 PM
How do LLMs learn to reason from data? Are they ~retrieving the answers from parametric knowledge🦜? In our new preprint, we look at the pretraining data and find evidence against this:
Procedural knowledge in pretraining drives LLM reasoning ⚙️🔢
🧵⬇️
Procedural knowledge in pretraining drives LLM reasoning ⚙️🔢
🧵⬇️
Reposted by Sohee Yang
Sparks of multi-hop reasoning ✨
🚨 New Paper 🚨
Can LLMs perform latent multi-hop reasoning without exploiting shortcuts? We find the answer is yes – they can recall and compose facts not seen together in training or guessing the answer, but success greatly depends on the type of the bridge entity (80% for country, 6% for year)! 1/N
Can LLMs perform latent multi-hop reasoning without exploiting shortcuts? We find the answer is yes – they can recall and compose facts not seen together in training or guessing the answer, but success greatly depends on the type of the bridge entity (80% for country, 6% for year)! 1/N
November 29, 2024 at 9:41 AM
Sparks of multi-hop reasoning ✨
🚨 New Paper 🚨
Can LLMs perform latent multi-hop reasoning without exploiting shortcuts? We find the answer is yes – they can recall and compose facts not seen together in training or guessing the answer, but success greatly depends on the type of the bridge entity (80% for country, 6% for year)! 1/N
Can LLMs perform latent multi-hop reasoning without exploiting shortcuts? We find the answer is yes – they can recall and compose facts not seen together in training or guessing the answer, but success greatly depends on the type of the bridge entity (80% for country, 6% for year)! 1/N
November 27, 2024 at 5:26 PM
🚨 New Paper 🚨
Can LLMs perform latent multi-hop reasoning without exploiting shortcuts? We find the answer is yes – they can recall and compose facts not seen together in training or guessing the answer, but success greatly depends on the type of the bridge entity (80% for country, 6% for year)! 1/N
Can LLMs perform latent multi-hop reasoning without exploiting shortcuts? We find the answer is yes – they can recall and compose facts not seen together in training or guessing the answer, but success greatly depends on the type of the bridge entity (80% for country, 6% for year)! 1/N