dataset-review/dataset-review huggingface dataset:
Compositional Causal Reasoning Evaluation in Language Models
ICML 2025

Compositional Causal Reasoning Evaluation in Language Models
Jacqueline R. M. A. Maasch 1 Alihan Hüyük 2 Xinnuo Xu 3 Aditya V. Nori 3 Javier Gonzalez 3
Abstract
Causal reasoning and compositional reasoning
are two core aspirations in AI. Measuring the ex-
tent of these behaviors requires principled eval-
uation methods. We explore a unified perspec-
tive that considers both behaviors simultaneously,
termed compositional causal reasoning (CCR):
the ability to infer how causal measures compose
and, equivalently, how causal quantities propagate
through graphs. We instantiate a framework for
the systematic evaluation of CCR for the average
treatment effect and the probability of necessity
and sufficiency. As proof of concept, we demon-
strate CCR evaluation for language models in the
Llama, Phi, and GPT families. On a math word
problem, our framework revealed a range of tax-
onomically distinct error patterns. CCR errors
increased with the complexity of causal paths for
all models except o1.
1. Introduction
Causal reasoning is a defining outcome of human evolution
(Goddu & Gopnik, 2024). Humans flexibly reason about
cause and effect in factual realities that can be observed and
intervened on, as well as imagined counterfactual worlds.
A causal lens enables humans and machines alike to learn
generalizable lessons about the mechanics of the universe
(Schölkopf et al., 2021). Thus, human-like AI might require
reasoning at all three levels of Pearl’s Causal Hierarchy: as-
sociational, interventional, and counterfactual (Pearl, 2000).
Human-like AI might also require compositional reasoning
(Lake et al., 2017): the capacity to recognize and synthe-
size novel combinations of previously observed concepts
(Xu et al., 2022). Compositionality is ubiquitous in the
physical world, symbolic systems, and human cognition
1Cornell
Tech
2Harvard
University
3Microsoft
Re-
search Cambridge.
Correspondence to:
Jacqueline Maasch
<maasch@cs.cornell.edu>. Work done while authors J. Maasch
an...
Unnatural Languages Are Not Bugs but Features for LLMs
ICML 2025

Unnatural Languages Are Not Bugs but Features for LLMs
Keyu Duan * 1 Yiran Zhao * 1 Zhili Feng 2 Jinjie Ni 1 Tianyu Pang 3 Qian Liu 3 Tianle Cai 4 Longxu Dou 3
Kenji Kawaguchi 1 Anirudh Goyal 5 J. Zico Kolter 2 Michael Qizhe Shieh 1
Abstract
Large Language Models (LLMs) have been ob-
served to process non-human-readable text se-
quences, such as jailbreak prompts, often viewed
as a bug for aligned LLMs. In this work, we
present a systematic investigation challenging
this perception, demonstrating that unnatural lan-
guages - strings that appear incomprehensible
to humans but maintain semantic meanings for
LLMs - contain latent features usable by mod-
els. Notably, unnatural languages possess latent
features that can be generalized across differ-
ent models and tasks during inference. Further-
more, models fine-tuned on unnatural versions
of instruction datasets perform on-par with those
trained on natural language, achieving 49.71 win
rates in Length-controlled AlpacaEval 2.0 in av-
erage across various base models. In addition,
through comprehensive analysis, we demonstrate
that LLMs process unnatural languages by filter-
ing noise and inferring contextual meaning from
filtered words. Our code is publicly available
at https://github.com/John-AI-Lab/
Unnatural_Language.
1. Introduction
Large Language Models (LLMs) (OpenAI, 2023; Touvron
et al., 2023; Dubey et al., 2024; anthropic, 2024) have shown
remarkable capabilities in understanding and generating
human-readable text, achieving impressive performance
across tasks, spanning from question answering (Bisk et al.,
2020; Ni et al., 2024) and mathematical reasoning (Cobbe
et al., 2021; Hendrycks et al., 2021; Gao et al., 2024) to
open-ended dialogue (Li et al., 2023). Such abilities are
*Equal contribution
1National University of Singapore
2Carnegie Mellon University 3Sea AI Lab 4Princeton Univer-
sity 5Mila, University of Montreal. Correspondence to: Keyu
Duan <k.duan@u.nus.edu>, Yiran Zhao <zhaoyiran@u.nus.edu>,
Mi...
Score Matching with Missing Data
ICML 2025

Score Matching with Missing Data
Josh Givens 1 Song Liu 1 Henry W J Reeve 2
Abstract
Score matching is a vital tool for learning the dis-
tribution of data with applications across many
areas including diffusion processes, energy based
modelling, and graphical model estimation. De-
spite all these applications, little work explores
its use when data is incomplete. We address this
by adapting score matching (and its major exten-
sions) to work with missing data in a flexible set-
ting where data can be partially missing over any
subset of the coordinates. We provide two sep-
arate score matching variations for general use,
an importance weighting (IW) approach, and a
variational approach. We provide finite sample
bounds for our IW approach in finite domain set-
tings and show it to have especially strong perfor-
mance in small sample lower dimensional cases.
Complementing this, we show our variational ap-
proach to be strongest in more complex high-
dimensional settings which we demonstrate on
graphical model estimation tasks on both real and
simulated data.
1. Introduction
Over the last decade, score matching has established itself
as a powerful tool with downstream use in many areas of
machine learning. Examples include: energy based mod-
elling (Swersky et al., 2011; Bao et al., 2020; Li et al.,
2019b), mode-seeking clustering (Sasaki et al., 2014), and
perhaps most prominently of all Diffusion processes (Song
& Ermon, 2019; Song et al., 2021b; Tashiro et al., 2021;
Song et al., 2021a; Huang et al., 2021). Score matching
aims to learn the score of a distribution which is the gra-
dient of the log of the probability density function (PDF)
(s(x) = ∇x log p(x)). In contrast to modelling the density
directly, the score does not need to integrate to one meaning
there is no need to calculate a normalising constant. This
allows it to be much more more flexibly modelled than the
1School of Mathematics, University of Bristol, Bristol, UK
2School of Artificial Intelligence,...
NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric
ICML 2025

NICE
Data Selection for Instruction Tuning in LLMs
with Non-differentiable Evaluation Metric
Jingtan Wang 1 2 Xiaoqiang Lin 1 Rui Qiao 1 3 Pang Wei Koh 4 Chuan-Sheng Foo 2 5 Bryan Kian Hsiang Low 1 3
Abstract
Curating data for instruction tuning is crucial
for enhancing the performance of large language
models (LLMs). This work aims to select training
data for instruction tuning to improve the LLM
performance on specific tasks. Existing methods
often rely on next-token prediction (NTP) loss as
a proxy for target task performance due to the non-
differentiable nature of performance evaluation
metrics. They select training data points that are
most helpful in reducing validation loss. However,
there is a discrepancy between minimizing NTP
loss and maximizing performance (e.g., code pass
rate in code generation). To remedy this, we intro-
duce a novel Non-differentiable evaluation metric-
based InfluenCe Estimation (NICE), which lever-
ages the policy gradient to select the training data
that improves the performance. Moreover, NICE
can perform data selection in the absence of labels
(ground-truth responses) when the evaluation met-
rics do not require labels (e.g., a reward model can
output reward scores without supervision from
labels). Experimental results show that our ap-
proach outperforms existing data selection base-
lines that use NTP loss in diverse and realistic sce-
narios. Notably, subsets selected by NICE often
produce models that outperform those trained on
the full dataset. Our code is available at https:
//github.com/JTWang2000/NICE.
1. Introduction
Instruction tuning (Bai et al., 2022; Ouyang et al., 2022) is
a fine-tuning paradigm that enables large language models
1National University of Singapore 2Institute for Infocomm Re-
search (I2R), A*STAR, Singapore 3Singapore-MIT Alliance for
Research and Technology 4University of Washington 5Centre for
Frontier AI Research (CFAR), A*STAR, Singapore. Correspon-
dence to: Bryan Kian Hsiang Low <lowkh@comp.nus...
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
ICML 2025

Is Best-of-N the Best of Them?
Coverage, Scaling, and Optimality in Inference-Time Alignment
Audrey Huang 1 Adam Block 2 Qinghua Liu 2 Nan Jiang 1 Akshay Krishnamurthy 2 Dylan J. Foster 2
Abstract
Recent work on inference-time alignment has
established the beneﬁts of increasing inference-
time computation in language models,
but
naively scaling compute through techniques like
Best-of-N sampling can cause performance to de-
grade due to reward hacking. Toward a theoreti-
cal understanding of how to best leverage addi-
tional computation, we formalize inference-time
alignment as improving a pre-trained policys re-
sponses for a prompt of interest, given access to
an imperfect reward model. We analyze the per-
formance of inference-time alignment algorithms
in terms of (i) response quality, and (ii) compute,
and provide new results that highlight the impor-
tance of the pre-trained policys coverage over
high-quality responses for performance and com-
pute scaling: (1) We show that Best-of-N align-
ment with an ideal N can achieve optimal perfor-
mance under stringent notions of coverage, but
provably suffers from reward hacking when N
is large, and fails to achieve tight guarantees un-
der more realistic coverage conditions; (2) We
introduce InferenceTimePessimism, a new al-
gorithm which mitigates reward hacking through
deliberate use of inference-time compute, imple-
menting pessimism in the face of uncertainty; we
prove that its performance is optimal and scaling-
monotonic, i.e., does not degrade as N increases.
We complement our theoretical results with ex-
periments that demonstrate the practicality of our
algorithm across a variety of tasks and models.
1. Introduction
Inference-time computation has emerged as a new axis
for scaling in language models, which has led to dramatic
1Computer Science Department, University of Illinois at
Urbana-Champaign, Champaign, Illinois, USA 2Microsoft Re-
search, New York, New York, USA. Correspondence to: Audrey
Huang <audreyh5...
GraphBridge: Towards Arbitrary Transfer Learning in GNNs
ICLR 2025

Published as a conference paper at ICLR 2025
GRAPHBRIDGE:
TOWARDS ARBITRARY TRANSFER
LEARNING IN GNNS
Li Ju, Xingyi Yang, Qi Li, Xinchao Wang∗
National University of Singapore
{jujulili,xyang,liqi}@u.nus.edu, xinchao@nus.edu.sg
ABSTRACT
Graph neural networks (GNNs) are conventionally trained on a per-domain, per-
task basis. It creates a significant barrier in transferring the acquired knowledge
to different, heterogeneous data setups. This paper introduces GraphBridge, a
novel framework to enable knowledge transfer across disparate tasks and domains
in GNNs, circumventing the need for modifications to task configurations or graph
structures. Specifically, GraphBridge allows for the augmentation of any pre-
trained GNN with prediction heads and a bridging network that connects the input
to the output layer. This architecture not only preserves the intrinsic knowledge of
the original model but also supports outputs of arbitrary dimensions. To mitigate
the negative transfer problem, GraphBridge merges the source model with a con-
currently trained model, thereby reducing the source bias when applied to the tar-
get domain. Our method is thoroughly evaluated across diverse transfer learning
scenarios, including Graph2Graph, Node2Node, Graph2Node, and graph2point-
cloud. Empirical validation, conducted over 16 datasets representative of these
scenarios, confirms the framework’s capacity for task- and domain-agnostic trans-
fer learning within graph-like data, marking a significant advancement in the field
of GNNs. Code is available at https://github.com/jujulili888/GraphBridge.
1
INTRODUCTION
With the explosive growth of graph data, the application of Graph Neural Networks (GNNs) has
become increasingly widespread in domains (Jing et al., 2022; 2021b; Wu et al., 2020b; Yu et al.,
2018; Shah et al., 2020) such as recommendation systems (Gao et al., 2022; Wu et al., 2019) and
biopharmaceutics(Zitnik et al., 2018; Rathi et al., 2019). Despite their growing popularity, the ...
Autoformulation of Mathematical Optimization Models Using LLMs
ICML 2025

Autoformulation of Mathematical Optimization Models Using LLMs
Nicol´as Astorga * 1 Tennison Liu * 1 Yuanzhang Xiao 2 Mihaela van der Schaar 1
Abstract
Mathematical optimization is fundamental to
decision-making across diverse domains, from
operations research to healthcare. Yet, translat-
ing real-world problems into optimization models
remains a difficult task, often demanding special-
ized expertise. This paper approaches the prob-
lem of autoformulation: the automated creation
of solver-ready optimization models from natu-
ral language problem descriptions. We identify
three core challenges of autoformulation: (1) the
vast, problem-dependent hypothesis space, (2) ef-
ficient and diverse exploration of this space un-
der uncertainty, and (3) evaluation of formulation
correctness against problem description. To ad-
dress these challenges, we present a novel method
leveraging Large Language Models (LLMs) with
Monte-Carlo Tree Search, exploiting the hierar-
chical nature of optimization modeling to gener-
ate and systematically explore possible formula-
tions. To enhance search efficiency, we introduce
symbolic pruning to eliminate trivially equiva-
lent search paths (branches), and employ LLM-
based evaluation of partial formulations to guide
search. Empirical analysis on linear and mixed-
integer programming benchmarks demonstrates
our method’s effectiveness, with significant per-
formance gains from both LLM-based value esti-
mation and symbolic pruning techniques.
1. Introduction
Mathematical optimization has long been a cornerstone of
decision-making processes across various domains, from
supply chain management (Bramel & Simchi-Levi, 1997)
and healthcare resource allocation (Delgado et al., 2022) to
portfolio optimization (Mokhtar et al., 2014). These prob-
*Equal contribution
1DAMTP, University of Cambridge,
Cambridge, UK 2ECE, University of Hawaii at Manoa, Hon-
olulu, USA. Correspondence to: Nicol´as Astorga, Tennison Liu
<{nja46,tl522}@cam.ac.uk>.
Proceedi...
Provable Meta-Learning with Low-Rank Adaptations
NeurIPS 2025

Provable Meta-Learning with Low-Rank Adaptations
Jacob L. Block
UT Austin
jblock@utexas.edu
Sundararajan Srinivasan
UT Austin
sundararajans@utexas.edu
Liam Collins
Snap, Inc.
lcollins2@snapchat.com
Aryan Mokhtari
UT Austin & Google Research
mokhtari@austin.utexas.edu
Sanjay Shakkottai
UT Austin
sanjay.shakkottai@utexas.edu
Abstract
The power of foundation models (FMs) lies in their capacity to learn highly expres-
sive representations that can be adapted to a broad spectrum of tasks. However,
these pretrained models require additional training stages to become effective for
downstream applications. In the multi-task setting, prior works have shown em-
pirically that specific meta-learning approaches for preparing a model for future
adaptation through parameter-efficient fine-tuning (PEFT) can outperform standard
retraining methods, but the mechanism of the benefits of meta-learning has been
largely unexplored. We introduce a framework for generic PEFT-based meta-
learning to learn a model that can easily adapt to unseen tasks. For linear models
using LoRA, we show that standard retraining is provably suboptimal for finding
an adaptable set of parameters and provide strict performance guarantees for our
proposed method. We verify these theoretical insights through experiments on
synthetic data as well as real-data vision and language tasks. We observe significant
performance benefits using a simple implementation of our proposed meta-learning
scheme during retraining relative to the conventional approach.
1
Introduction
Foundation Models (FMs) learn rich representations that are useful for a variety of downstream tasks.
The first stage of FM training is referred to as pretraining, where a combination of massive public,
proprietary, and synthetic sources of data is used to learn a general-purpose model from scratch
[1–4]. However, due to the enormous cost of training state-of-the-art models on such large datasets,
pretraining is largely infeasible for most. Thus, the ...
Field Matching: an Electrostatic Paradigm to Generate and Transfer Data
ICML 2025

Field Matching: an Electrostatic Paradigm to Generate and Transfer Data
Alexander Kolesov * 1 2 Stepan I. Manukhov * 1 3 Vladimir V. Palyulin 1 Alexander Korotin 1 2
Abstract
We propose Electrostatic Field Matching (EFM),
a novel method that is suitable for both genera-
tive modeling and distribution transfer tasks. Our
approach is inspired by the physics of an electri-
cal capacitor. We place source and target distri-
butions on the capacitor plates and assign them
positive and negative charges, respectively. Then
we learn the electrostatic field of the capacitor
using a neural network approximator. To map the
distributions to each other, we start at one plate
of the capacitor and move the samples along the
learned electrostatic field lines until they reach
the other plate. We theoretically justify that this
approach provably yields the distribution transfer.
In practice, we demonstrate the performance of
our EFM in toy and image data experiments. Our
code is available at https://github.com/
justkolesov/FieldMatching
1. Introduction
The basic task of generative modeling is to learn a transfor-
mation between two distributions accessible by i.i.d. sam-
ples. The typical scenarios considered are noise-to-data
(Goodfellow et al., 2014) and data-to-data (Zhu et al.,
2017). These are usually referred to as the unconditional
data generation and data translation, respectively.
Physics is often at the heart of the principles of generative
modeling. One of the first attempts to link generative mod-
els and physics was made in Energy-Based models (LeCun
& Huang, 2005, EBM). They parameterize data distribu-
tions using the Gibbs-Boltzmann distribution density and
generate data through simulation of Langevin dynamics (Du
& Mordatch, 2019; Song & Kingma, 2021).
*Equal contribution 1Skolkovo Institute of Science and Tech-
nology, Moscow, Russia 2Artificial Intelligence Research Institute,
Moscow, Russia 3Lomonosov Moscow State University, Faculty of
Physics, Moscow, Russia. Corr...
Conformal Tail Risk Control for Large Language Model Alignment
ICML 2025

Conformal Tail Risk Control for Large Language Model Alignment
Catherine Chen 1 Jingyan Shen 2 Zhun Deng 3 Lihua Lei 4 5
Abstract
Recent developments in large language models
(LLMs) have led to their widespread usage for
various tasks. The prevalence of LLMs in society
implores the assurance on the reliability of their
performance. In particular, risk-sensitive applica-
tions demand meticulous attention to unexpect-
edly poor outcomes, i.e., tail events, for instance,
toxic answers, humiliating language, and offen-
sive outputs. Due to the costly nature of acquiring
human annotations, general-purpose scoring mod-
els have been created to automate the process of
quantifying these tail events. This phenomenon in-
troduces potential human-machine misalignment
between the respective scoring mechanisms. In
this work, we present a lightweight calibration
framework for blackbox models that ensures the
alignment of humans and machines with provable
guarantees. Our framework provides a rigorous
approach to controlling any distortion risk mea-
sure that is characterized by a weighted average
of quantiles of the loss incurred by the LLM with
high confidence. The theoretical foundation of our
method relies on the connection between confor-
mal risk control and a traditional family of statis-
tics, i.e., L-statistics. To demonstrate the utility of
our framework, we conduct comprehensive exper-
iments that address the issue of human-machine
misalignment.
1. Introduction
Large Language Models (LLMs) have proven to be per-
vasive in society with applications across various settings,
*Equal contribution
1Institute for Computational and Math-
ematical Engineering, Stanford University, CA 2Department of
Industrial Engineering and Operations Research, Columbia Uni-
versity, NY 3Department of Computer Science, UNC Chapel
Hill, NC 4Graduate School of Business, Stanford University, CA
5Department of Statistics (by courtesy), Stanford University, CA.
Correspondence to: Catherine Chen <cyc2...
Identifying latent state transitions in non-linear dynamical systems
ICLR 2025

Published as a conference paper at ICLR 2025
IDENTIFYING LATENT STATE TRANSITIONS IN
NON-LINEAR DYNAMICAL SYSTEMS
C¸ a˘glar Hızlı
Aalto University
caglar.hizli@aalto.fi
C¸ a˘gatay Yıldız
University of T¨ubingen
T¨ubingen AI Center
Matthias Bethge
University of T¨ubingen
T¨ubingen AI Center
ST John
Aalto University
Pekka Marttinen
Aalto University
ABSTRACT
This work aims to recover the underlying states and their time evolution in a
latent dynamical system from high-dimensional sensory measurements. Previous
works on identifiable representation learning in dynamical systems focused on
identifying the latent states, often with linear transition approximations. As such,
they cannot identify nonlinear transition dynamics, and hence fail to reliably
predict complex future behavior. Inspired by the advances in nonlinear ICA, we
propose a state-space modeling framework in which we can identify not just
the latent states but also the unknown transition function that maps the past
states to the present. Our identifiability theory relies on two key assumptions: (i)
sufficient variability in the latent noise, and (ii) the bijectivity of the augmented
transition function. Drawing from this theory, we introduce a practical algorithm
based on variational auto-encoders. We empirically demonstrate that it improves
generalization and interpretability of target dynamical systems by (i) recovering
latent state dynamics with high accuracy, (ii) correspondingly achieving high future
prediction accuracy, and (iii) adapting fast to new environments. Additionally, for
complex real-world dynamics, (iv) it produces state-of-the-art future prediction
results for long horizons, highlighting its usefulness for practical scenarios.
1
INTRODUCTION
We focus on the problem of understanding the underlying states of a dynamical system from its
high-dimensional sensory measurements. This task is prevalent across various fields, including
reinforcement learning (Hafner et al., 2019a) and robotics (Levi...