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Ekdeep Singh @ ICML

@ekdeepl.bsky.social
280 followers 380 following 48 posts

Postdoc at CBS, Harvard University (New around here)

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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025
Dynamics of attention maps is particularly striking in this competition: e.g., with high diversity, we see a bigram counter forming, but memorization eventually occurs and the pattern becomes uniform! This means models can remove learned components if they are not useful anymore!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025
Beyond corroborating our phase diagram, LIA confirms a persistent competition underlies ICL: once the induction head forms, with enough diversity bigram-based inference takes over; under low diversity, memorization occurs faster, yielding a bigram-based retrieval solution!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025
Given optimization and data diversity are independent axes, we then ask if these forces race against each other to yield our observed algorithmic phases. We propose a tool called LIA (linear interpolation of algorithms) for this analysis.
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025
The tests check out! We see before/after a critical # of train steps are met (where induction head emerges), the model relies on unigram/bigram stats. With few chains (less diversity), there is retrieval behavior: we can literally reconstruct transition matrices from MLP neurons!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025
To test the above claim, we compute the effect of shuffling a sequence on next-token probs: this breaks bigram stats, but preserves unigrams. We check how “retrieval-like” or memorization-based model behavior is by comparing predicted transitions’ KL to a random set of chains.
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025
We claim four algorithms explain the model’s behavior in diff. train/test settings. These algos compute uni-/bi- gram frequency statistics of an input to either *retrieve* a memorized chain or to in-context *infer* the chain used to define the input: latter performs better OOD!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025
We analyze models trained on a fairly simple task: learning to simulate a *finite mixture* of Markov chains. The sequence modeling nature of this task makes it a better abstraction for studying ICL abilities in LMs, (compared to abstractions of few-shot learning like linear reg.)
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 16/02/2025
New 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.
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 12/02/2025
There'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/
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
Overall, there's a long way to go, but I'm hopeful for the next year of SAE research and am looking forward to contributing to it! Paper link again: arxiv.org/abs/2410.11767 And note again that paper co-lead Abhinav Menon is awesome and looking for PhD positions! Recruit him!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
2. We should build on ideas from disentanglement research to avoid reinventing the wheel! For ex., the community came up with really ingenious approaches to exploit "weak supervision" (kinda like self-supervision) to enable disentanglement! See paper linked in pic and our paper for a jab at this!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
Result 3: None of the latents turn out to be causally relevant to the model's computation! E.g., intervening on a specific part-of-speech feature induces no effects of distribution of other parts in model's generations! Again, we expect this based on results from disentanglement!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
Result 2: The results above are however quite brittle. Small architectural changes yield huge deviations in fraction of variance explained of the original representations! This inability / limitation of SAEs is exactly what the disentanglement community's results suggested!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
Result 1: We can find highly interpretable features in SAEs! We follow standard protocols to arrive at these results, yielding, e.g., features for parts-of-speech, counters, and depth trackers in different languages!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
Taking inspiration from the disentanglement literature, we first define toy settings where we know what ground-truth latents to expect: specifically, we use formal grammars like Dyck-2, Expr, and English. We pretrain models on these grammars and then train SAEs on their features!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
Interestingly, the ICML best paper winner in 2019 established an impossibility theorem for disentanglement: if the DGP is nonlinear, there are infinite solutions to the problem. Hence, identified latents will depend on what AE pipeline is used, and may not be causally relevant!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
In Disentanglement, one trains Autoencoders (possibly with some regularization like sparsity) to identify latents underlying the data-generating process (DGP); with SAEs, one is essentially trying to solve the same problem, but the data source is now model representations!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
Paper co-led with Abhinav Menon (bsky.app/profile/bani...), who is awesome and is looking for PhD positions---recruit him! arXiv link: arxiv.org/abs/2410.11767
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 18/12/2024
Paper 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!🤯
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
Also highlighting our previous paper in this concept learning series that this work builds on top of, and a theoretical follow up with @YongyiYang7 & @weihu_ where we find we can prove the bulk of empirical results shown above! Previous: arxiv.org/abs/2310.09336 Follow-up: arxiv.org/abs/2410.08309
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
Finally, shoutout to my amazing collaborators @corefpark, @MayaOkawa, @a_jy_l, and @Hidenori8Tanaka! This was a really fun collaboration where everybody played a very different, extremely synergistic role. :)
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
The paper contains several more results! E.g., theoretical models that capture the learning dynamics, impact of underspecification (i.e., correlated concepts), and further musings on concept signal. Check out the link below! Link: arxiv.org/abs/2406.19370
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
To further corroborate this claim, we (i) train models on CelebA & (ii) experiment with pretrained StableDiffusion models. We again find latent interventions can elicit arbitrary compositions that are not present in the training data (or are unlikely to be present for SD models)!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
Despite being a toy setting, we hypothesize this result will likely hold at scale with modern generative models: there exist latent capabilities that one, via mere input prompting, is unable to elicit! This can be concerning, for such capabilities may slip through during evals!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
We hypothesize the sudden turns mark *disentanglement*: the model can arbitrarily compose all concepts after this turn. But learning dynamics shows otherwise–what’s going on?! Turns out capabilities are *latent* at this point, but can be elicited via mere linear interventions!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
We then analyze the learning dynamics of unseen concept combinations (compared with a seen combination in figure below), and find fascinating *sudden turns* (pink stars) in the learning dynamics!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
Analyzing concept learning dynamics now, we find the concept that explains maximal variance in the data (called “concept signal”) dictates the learning dynamics: concepts with a stronger signal strength are learned *first*, and bias the learning trajectories for all combinations.
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
We first define “concept space”, a coordinate space whose axes denote specific concepts (e.g., color, size). We train diffusion models on a set of concept combinations & map generations of both seen / unseen combinations on the concept space, yielding *concept learning dynamics*!
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Ekdeep Singh @ ICML @ekdeepl.bsky.social · 10/11/2024
Paper 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! 🤯
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