Derek Chong (@dch) 's Twitter Profile
Derek Chong

@dch

MSCS @ Stanford – @StanfordNLP

ID: 3579431

calendar_today06-04-2007 02:17:16

20 Tweet

90 Takipçi

170 Takip Edilen

Stanford NLP Group (@stanfordnlp) 's Twitter Profile Photo

Jenny Hong & Derek Chong present how to do effective #NLProc information extraction from long legal texts despite having very limited labels—and why it is important to allow stakeholders to be able to audit legal systems Video youtube.com/watch?v=TIN_2n… Paper aclanthology.org/2021.nllp-1.20…

Markus Deserno (@markusdeserno) 's Twitter Profile Photo

May I invite you to a fun thread about a delightful quirk of relativity theory? Starting with a simple fact about rotations, I’ll hope to give you some intuition about something that’s considered wildly counterintuitive: velocity addition. Intrigued? Buckle up!

May I invite you to a fun thread about a delightful quirk of relativity theory? Starting with a simple fact about rotations, I’ll hope to give you some intuition about something that’s considered wildly counterintuitive: velocity addition. Intrigued? Buckle up!
Shikhar (@shikharmurty) 's Twitter Profile Photo

How can users fix "bugs" in trained classifiers post-hoc without finetuning on additional data? In our EMNLP 2022 paper, we show that corrective feedback expressed as a *library of conditional natural language statements* are a promising direction. (1/n) arxiv.org/pdf/2211.03318…

How can users fix "bugs" in trained classifiers post-hoc without finetuning on additional data? In our EMNLP 2022 paper, we show that corrective feedback expressed as a *library of conditional natural language statements* are a promising direction. (1/n)
arxiv.org/pdf/2211.03318…
Stanford NLP Group (@stanfordnlp) 's Twitter Profile Photo

Detecting Label Errors using Pre-Trained Language Models You don’t need special techniques for label error detection—just use a foundation model! Derek Chong Derek Chong Jenny Hong Christopher Manning also stress needing realistic label noise for experiments. #emnlp2022 arxiv.org/abs/2205.12702

Detecting Label Errors using Pre-Trained Language Models
You don’t need special techniques for label error detection—just use a foundation model! Derek Chong <a href="/dch/">Derek Chong</a> Jenny Hong <a href="/chrmanning/">Christopher Manning</a> also stress needing realistic label noise for experiments. #emnlp2022
arxiv.org/abs/2205.12702
Kenneth Goodman (@pythonprimes) 's Twitter Profile Photo

#OpenAI's ChatGPT is ready to become a lawyer, it passed a practice bar exam! Scoring 70% (35/50). Guessing randomly would happen < 0.00000001% of the time

#OpenAI's ChatGPT is ready to become a lawyer, it passed a practice bar exam!  Scoring 70% (35/50). Guessing randomly would happen &lt; 0.00000001% of the time
CLS (@chengleisi) 's Twitter Profile Photo

Automating AI research is exciting! But can LLMs actually produce novel, expert-level research ideas? After a year-long study, we obtained the first statistically significant conclusion: LLM-generated ideas are more novel than ideas written by expert human researchers.

Automating AI research is exciting! But can LLMs actually produce novel, expert-level research ideas?

After a year-long study, we obtained the first statistically significant conclusion: LLM-generated ideas are more novel than ideas written by expert human researchers.
Shikhar (@shikharmurty) 's Twitter Profile Photo

Super excited to share NNetnav : A new method for generating complex demonstrations to train web agents—driven entirely via exploration! Here's how we’re building useful browser agents, without expensive human supervision: 🧵👇 Code: github.com/MurtyShikhar/N… Preprint:

Shikhar (@shikharmurty) 's Twitter Profile Photo

New #NAACL2025 paper! 🚨 Transformer LMs are data hungry, we propose a new auxiliary loss function (TreeReg) to fix that. TreeReg takes bracketing decisions from syntax trees and turns them into orthogonality constraints on span representations. ✅ Boosts pre-training data

OryxMaps (@oryxmaps) 's Twitter Profile Photo

The Malaysian election on Wednesday, which has largely gone under the radar – despite the competition. The brutal truth is Malaysia is malapportioned. Sarawak has 9 more seats then Selangor, despite having half the people. /1 of long story #PRU14 #MalaysiaDecides #ElectionTwitter

The Malaysian election on Wednesday, which has largely gone under the radar – despite the competition. The brutal truth is Malaysia is malapportioned. Sarawak has 9 more seats then Selangor, despite having half the people. /1 of long story #PRU14 #MalaysiaDecides #ElectionTwitter