Chandan Singh | chandan singh

Research

Some areas I'm currently excited about. If you want to chat about research or are interested in interning at MSR, feel free to reach out over email :)

🔎 Interpretability. I'm interested in rethinking interpretability in the context of LLMs

augmented imodels - use LLMs to build a transparent model
explanation penalization - regularize explanations to align models with prior knowledge
adaptive wavelet distillation - replace neural nets with simple, performant wavelet models

🚗 LLM steering. Interpretability tools can provide ways to better guide and use LLMs (without needing gradients!)

tree prompting - improve black-box few-shot text classification with decision trees
attention steering - guide LLMs by emphasizing specific input spans
interpretable autoprompting - automatically find fluent natural-language prompts

🧠 Neuroscience. Since joining MSR, I've been focused on leveraging LLM interpretability to understand how the human brain represents language (using fMRI in collaboration with the Huth lab at UT Austin).

explanation-mediated validation - build and test fMRI explanations using LLM-generated stimuli
qa embeddings - build interpretable fMRI encoding models by asking yes/no questions to LLMs
summarize & score explanations - generate natural-language explanations of fMRI encoding models

💊 Healthcare. I'm also actively working on how we can improve clinical decision instruments by using the information contained across various sources in the medical literature (in collaboration with Aaron Kornblith at UCSF and the MSR Health Futures team).

clinical self-verification - self-verification improves performance and interpretability of clinical information extraction
clinical rule vetting - stress testing a clinical decision instrument performance for intra-abdominal injury

Across these areas, I'm interested in decision trees and how we can build flexible but accurate transparent models. I put a lot of my code into the imodels and imodelsX packages.

year	title	authors	tags	paper	misc
'24	Interpretable Language Modeling via Induction-head Ngram Models	kim, mantena, et al.	🧠🔎🌀	arxiv
'24	A generative framework to bridge data-driven models and scientific theories in language neuroscience	antonello, singh, jain, hsu, gao, yu, & huth	🧠🔎🌀	arxiv
'24	Crafting Interpretable Embeddings by Asking LLMs Questions	benara, singh, morris, antonello, stoica, huth, & gao	🧠🔎🌀	neurips
'24	Rethinking Interpretability in the Era of Large Language Models	singh, inala, galley, caruana, & gao	🔎🌀	arxiv
'24	Vector-ICL: In-context Learning with Continuous Vector Representations	zhuang et al.	🔎🌀	arxiv
'24	Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning	chen et al.	🔎🌀	COLING
'24	Learning a Decision Tree Algorithm with Transformers	zhuang et al.	🔎🌀🌳	tmlr
'24	Model Tells Itself Where to Attend: Faithfulness Meets Automatic Attention Steering	zhang, yu, et al.	🔎🌀	arxiv
'24	Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs	zhang et al.	🔎🌀	iclr
'24	Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries	gero et al.	🔎🌀	ml4h findings
'23	Tree Prompting	morris, singh, rush, gao, & deng	🔎🌀🌳	emnlp
'23	Augmenting Interpretable Models with LLMs during Training	singh, askari, caruana, & gao	🔎🌀🌳	nature communications
'23	Explaining black box text modules in natural language with language models	singh, hsu, antonello, jain, huth, yu & gao	🔎🌀	neurips workshop
'23	Self-Verification Improves Few-Shot Clinical Information Extraction	gero, singh, cheng, naumann, galley, gao, & poon	🔎🌀💊	icml workshop
'22	Explaining patterns in data with language models via interpretable autoprompting	singh, morris, aneja, rush, & gao	🔎🌀	emnlp workshop
'22	Stress testing a clinical decision instrument performance for intra-abdominal injury	kornblith, singh et al.	🔎🌳💊	PLOS digital health
'22	Fast interpretable greedy-tree sums (FIGS)	tan, singh, nasseri, agarwal, & yu	🔎🌳	pnas
'22	Hierarchical shrinkage for trees	agarwal, tan, ronen, singh, & yu	🔎🌳	icml (spotlight)
'22	VeridicalFlow: a python package for building trustworthy data science pipelines with PCS	duncan, kapoor, agarwal, singh, & yu	💻🔍	joss
'21	imodels: a python package for fitting interpretable models	singh, nasseri, et al.	💻🔍🌳	joss
'21	Adaptive wavelet distillation from neural networks through interpretations	ha, singh, et al.	🔍🌀🌳	neurips
'21	Matched sample selection with GANs for mitigating attribute confounding	singh, balakrishnan, & perona	🌀	cvpr workshop
'21	Revisiting complexity and the bias-variance tradeoff	dwivedi, singh, yu & wainwright	🌀	jmlr
'20	Curating a COVID-19 data repository and forecasting county-level death counts in the United States	altieri et al.	🔎🦠	hdsr
'20	Transformation importance with applications to cosmology	singh, ha, lanusse, boehm, liu & yu	🔎🌀🌌	iclr workshop (spotlight)
'20	Interpretations are useful: penalizing explanations to align neural networks with prior knowledge	rieger, singh, murdoch & yu	🔎🌀	icml
'19	Hierarchical interpretations for neural network predictions	Singh, Murdoch, & Yu	🔍🌀	ICLR
'19	interpretable machine learning: definitions, methods, and applications	Murdoch, Singh, et al.	🔍🌳🌀	pnas
'19	disentangled attribution curves for interpreting random forests and boosted trees	devlin, singh, murdoch & yu	🔍🌳	arxiv
'18	large scale image segmentation with structured loss based deep learning for connectome reconstruction	Funke, Tschopp, et al.	🧠🌀	TPAMI
'18	linearization of excitatory synaptic integration at no extra cost	Morel, Singh, & Levy	🧠	J Comp Neuro
'17	a consensus layer V pyramidal neuron can sustain interpulse-interval coding	Singh & Levy	🧠	Plos One
'17	a constrained, weighted-l1 minimization approach for joint discovery of heterogeneous neural connectivity graphs	Singh, Wang, & Qi	🧠	neurips Workshop	,

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