Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/07/2025Submit your latest and greatest papers to the hottest workshop on the block---on cognitive interpretability! 🔥 081
Reposted by Ekdeep Singh @ ICMLJennifer Hu @jennhu.bsky.social · 16/07/2025Excited to announce the first workshop on CogInterp: Interpreting Cognition in Deep Learning Models @ NeurIPS 2025! 📣 How can we interpret the algorithms and representations underlying complex behavior in deep learning models? 🌐 coginterp.github.io/neurips2025/ 1/4coginterp.github.ioHomeFirst Workshop on Interpreting Cognition in Deep Learning Models (NeurIPS 2025) 15819
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 12/07/2025I'll be at ICML beginning this Monday---hit me up if you'd like to chat! 030
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 28/06/2025Check out one of the most exciting papers of the year! :D 120
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 29/04/2025I'll be attending NAACL at New Mexico beginning today---hit me up if you'd like to chat! 000
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 07/03/2025Check out our new work on the duality between SAEs and how concepts are organized in model representations! 020
Reposted by Ekdeep Singh @ ICMLSumedh Hindupur @sumedh-hindupur.bsky.social · 07/03/2025New preprint alert! Do Sparse Autoencoders (SAEs) reveal all concepts a model relies on? Or do they impose hidden biases that shape what we can even detect? We uncover a fundamental duality between SAE architectures and concepts they can recover. Link: arxiv.org/abs/2503.01822 1142
Reposted by Ekdeep Singh @ ICMLAndrew Lampinen @lampinen.bsky.social · 16/02/2025Very nice paper; quite aligned with the ideas in our recent perspective on the broader spectrum of ICL. In large models, there's probably a complicated, context dependent mixture of strategies that get learned, not a single ability. 1172
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025New paper–accepted as *spotlight* at #ICLR2025! 🧵👇 We show a competition dynamic between several algorithms splits a toy model’s ICL abilities into four broad phases of train/test settings! This means ICL is akin to a mixture of different algorithms, not a monolithic ability. 2325
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 12/02/2025There's never been a more exciting time to explore the science of intelligence! 🧠 What can ideas and approaches from science tell us about how AI works? What might superhuman AI reveal about human cognition? Join us for an internship at Harvard to explore together! 1/ 121
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 23/01/2025Now accepted at NAACL! This would be my first time presenting at an ACL conference---I've got almost first-year grad school level of excitement! :P 040
Reposted by Ekdeep Singh @ ICMLCore Francisco Parkg @corefpark.bsky.social · 05/01/2025New paper! “In-Context Learning of Representations” What happens to an LLM’s internal representations in the large context limit? We find that LLMs form “in-context representations” to match the structure of the task given in context! 172
Reposted by Ekdeep Singh @ ICMLAndrew Lee @ajyl.bsky.social · 05/01/2025New paper <3 Interested in inference-time scaling? In-context Learning? Mech Interp? LMs can solve novel in-context tasks, with sufficient examples (longer contexts). Why? Bc they dynamically form *in-context representations*! 1/N 25316
Reposted by Ekdeep Singh @ ICMLNaomi Saphra @nsaphra.bsky.social · 20/12/2024Transformer LMs get pretty far by acting like ngram models, so why do they learn syntax? A new paper by sunnytqin.bsky.social, me, and @dmelis.bsky.social illuminates grammar learning in a whirlwind tour of generalization, grokking, training dynamics, memorization, and random variation. #mlsky #nlparxiv.orgSometimes I am a Tree: Data Drives Unstable Hierarchical GeneralizationLanguage models (LMs), like other neural networks, often favor shortcut heuristics based on surface-level patterns. Although LMs behave like n-gram models early in training, they must eventually learn... 514230
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024Paper alert––*Awarded best paper* at NeurIPS workshop on Foundation Model Interventions! 🧵👇 We analyze the (in)abilities of SAEs by relating them to the field of disentangled rep. learning, where limitations of AE based interpretability protocols have been well established!🤯 2379
Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024Paper alert—accepted as a *Spotlight* at NeurIPS!🧵 Building on our work relating emergent abilities to task compositionality, we analyze the *learning dynamics* of compositional abilities & find there exist latent interventions that can elicit them much before input prompting works! 🤯 1214
