Christopher Manning ✓

@chrmanning · Palo Alto · joined 17 Sep 2014

Founder @stanfordnlp & cs224n—Senior Fellow @StanfordHAI—Prof. CS & Linguistics @Stanford—GP @aixventureshq—MTS @moonlake—Australian🇦🇺—Do #NLProc & #AI 👋

169 236Followers

373Following

3 038Posts total

391.3KViews on collected posts

Postingan terbaru

Christopher Manning ✓@chrmanning12 Sep

@chrmanning @DarioAmodei @stanfordnlp I support this 7.1K views · 61 likes · 1 reposts · 6 replies Open on X →

Christopher Manning ✓@chrmanning12 Sep

Holistic Evaluation of Language Models – https://t.co/jEje8m0mDp ELEPHANT: Measuring and understanding social sycophancy in LLMs – https://t.co/sHYjxabKQx Sycophantic AI decreases prosocial intentions and promotes dependence – https://t.co/aTSrtInvKS AI generates covertly 2.8K views · 27 likes · 0 reposts · 0 replies Open on X →

Christopher Manning ✓@chrmanning12 Sep

The researchers in university groups are unequalled at the task of contributing to “a much broader and more ingenious stable of evaluations.” University research emphasizes novel discovery and rigorous and skeptical verification. Students who are relatively new to a field but 3.5K views · 23 likes · 0 reposts · 2 replies Open on X →

Christopher Manning ✓@chrmanning12 Sep

Stanford NLP is an ideal independent third-party evaluator of model alignment and safety with deep expertise in interpretability and downstream evaluations. Much ink has been spilled on what universities are good for in the current hyper-speed AI world, but universities have 3.8K views · 34 likes · 2 reposts · 4 replies Open on X →

Christopher Manning ✓@chrmanning12 Sep

I propose Stanford NLP as an independent third-party evaluator under @DarioAmodei’s 3 step plan. For important parts of the work, universities would be better than any other organization (see below 🧵👇), and, of university groups, @stanfordnlp would be the best one to choose. 😊 69.2K views · 765 likes · 64 reposts · 44 replies Open on X →

Christopher Manning ✓@chrmanning25 Aug

Substantive ML discussion on Twitter. Like in the 2010s. Feeling blessed. 😊 28.6K views · 199 likes · 2 reposts · 8 replies Open on X →

Christopher Manning ✓@chrmanning24 Aug

@RolandMemisevic @akarshkumar0101 @chrmanning I think that's a good bet too. Actually let me clarify that I don't really think of SMT as "distill transformers." It's joint optimization of a memory-bottlenecked transformer and an RNN. So it may be closer to your bet than it seems? 30.6K views · 37 likes · 0 reposts · 0 replies Open on X →

Christopher Manning ✓@chrmanning23 Aug

@chrmanning @akarshkumar0101 @phillip_isola Another cool connection is the ability to parallelize GRUs over the sequence length for both forward and backward passes, allowing pretraining at scale without teacher forcing. As for example in this paper! https://t.co/9QB0cr7rqf 1.9K views · 36 likes · 5 reposts · 1 replies Open on X →

Christopher Manning ✓@chrmanning23 Aug

When teaching CS224N, I thought it important to introduce students to a broad neural toolbox – not just transformers but FFNs, CNNs, LSTMs, tree-recursive NNs, BiDAF QA nets, highway nets, …. I think the resurgence of work using recurrence shows the importance of this approach. 81.7K views · 630 likes · 40 reposts · 14 replies Open on X →

Christopher Manning ✓@chrmanning23 Aug

Paper: https://t.co/IoDcRINaAL This is one of a whole bunch of recent papers reviving study of recurrent neural networks. One weird omission is not testing LSTM RNNs. Surely they remain the canonical successful RNN architecture? Another completely uninvestigated thing is the 6.4K views · 59 likes · 5 reposts · 4 replies Open on X →

Christopher Manning ✓@chrmanning23 Aug

Pretraining Recurrent Networks without Recurrence by @akarshkumar0101 & @phillip_isola is a great paper! It makes an end-run around the problems of RNNs via a transformer teacher to learn good predictive state representations & supervised learning of a memory transition function 100.1K views · 917 likes · 112 reposts · 16 replies Open on X →

Christopher Manning ✓@chrmanning19 Aug

Transformers are Inherently Succinct by Pascal Bergsträßer, Ryan Cotterell & Anthony Widjaja Lin is an interesting contribution. Various work (Hahn 2020, Li & Cotterell 2025 i.a.) has shown that transformers are less powerful than RNNs … yet somehow they do so well in practice. 31.9K views · 375 likes · 51 reposts · 8 replies Open on X →

Christopher Manning ✓@chrmanning18 Aug

The Bay Area’s AI corporate titans still just haven’t come to terms with the fact that it’s not that the public has a negative view of or fear of AI. It’s that the public has a negative view of _them_. But I don’t imagine that they’re likely to go on a similar listening tour…. 23.6K views · 156 likes · 18 reposts · 9 replies Open on X →

Dibandingkan akun berukuran sama

13 postingan dari 90 hari terakhir, dibandingkan dengan rentang 100K–1M pengikut. tampil ke lebih banyak orang dibanding akun seukurannya.

Median tayangan23 586akun ini5 983median untuk 100K–1M

Jangkauan, %13.94%akun ini1.49%median untuk 100K–1M

Interaksi, %0.96%akun ini0.93%median untuk 100K–1M

Metrik	Akun ini	Median untuk 100K–1M	Rasio
Median tayangan per postingan	23 586	5 983	3.94×
Jangkauan (tayangan ÷ pengikut)	13.94%	1.49%	9.35×
Tingkat interaksi	0.96%	0.93%	1.03×

Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

23.6K18 Aug

31.9K19 Aug

100.1K23 Aug

6.4K

81.7K

1.9K

30.6K24 Aug

28.6K25 Aug

69.2K12 Sep

3.8K

3.5K

2.8K

7.1K

Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.79%18 Aug

1.37%19 Aug

1.05%23 Aug

1.07%

0.84%

2.18%

0.12%24 Aug

0.73%25 Aug

1.26%12 Sep

1.04%

0.72%

0.96%

0.95%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.

What the audience does

Likes57.7%3 319 in total

Reposts5.2%300 in total

Replies2.0%116 in total

Quotes0.3%17 in total

Bookmarks34.7%1 998 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Postingan terbaru

Dibandingkan akun berukuran sama

Growth & engagement

Views per post

Engagement rate per post

What the audience does

Akun serupa