dataset-review/dataset-review huggingface dataset:
Investigating and Mitigating Catastrophic Forgetting in Medical Knowledge Injection through Internal Knowledge Augmentation Learning
NeurIPS 2025

Investigating and Mitigating Catastrophic Forgetting
in Medical Knowledge Injection through Internal
Knowledge Augmentation Learning
Yuxuan Zhou1, Xien Liu∗1, Xiao Zhang1, Chen Ning1, Shijin Wang2, Guoping Hu2, Ji Wu1,3,4
1Department of Electronic Engineering, Tsinghua University, Beijing, China
2iFLYTEK Research, Hefei, China
3College of AI, Tsinghua University, Beijing, China
4Beijing National Research Center for Information Science and Technology, Beijing, China
Abstract
Large Language Models (LLMs) are expected to possess comprehensive medical
knowledge to support real-world clinical applications. While domain-specific
fine-tuning effectively injects medical knowledge into LLMs, it often causes catas-
trophic forgetting of previously acquired knowledge and instruction-following ca-
pabilities. In this paper, we investigate this issue and reveal a pattern of proximity-
dependent forgetting: knowledge that is semantically or topically close to the
injected content is more likely to be forgotten, while unrelated knowledge shows
minimal degradation. Moreover, we observe that existing mitigation techniques
fail to address this type of forgetting effectively. Motivated by this observation
and inspired by human learning mechanisms, we propose InternAL (Internal
Knowledge Augmentation Learning), a novel approach that leverages LLMs’ own
internal knowledge to mitigate forgetting. InternAL first probes internal knowledge
closely related to the injection by prompting the model with questions derived
from the injected knowledge. This knowledge is then used to augment the original
injection dataset, guiding the model to retain related prior knowledge during train-
ing. Experimental results on multiple LLMs (LLaMA, Qwen) demonstrate that
InternAL significantly mitigates proximity-related forgetting while maintaining
strong knowledge injection performance. Our findings provide new insights into
the nature of catastrophic forgetting in medical knowledge injection and highlight
...
Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation
NeurIPS 2025

Magical: Medical Lay Language Generation via
Semantic Invariance and Layperson-tailored
Adaptation
Weibin Liao♣♠†, Tianlong Wang♣†, Yinghao Zhu♢,
Yasha Wang♣♠∗, Junyi Gao♡§∗, Liantao Ma♣♠∗
♣National Engineering Research Center For Software Engineering, Peking University
♠School of Computer Science, Peking University
♢School of Computing and Data Science, The University of Hong Kong
♡Centre for Medical Informatics, University of Edinburgh
§Health Data Research UK
{liaoweibin, tianlong.wang}@stu.pku.edu.cn, malt@pku.edu.cn
https://github.com/tianlwang/Magical.git
Abstract
Medical Lay Language Generation (MLLG) plays a vital role in improving the
accessibility of complex scientific content for broader audiences. Recent literature
to MLLG commonly employ parameter-efficient fine-tuning methods such as Low-
Rank Adaptation (LoRA) to fine-tuning large language models (LLMs) using
paired expert-lay language datasets. However, LoRA struggles with the challenges
posed by multi-source heterogeneous MLLG datasets. Specifically, through a
series of exploratory experiments, we reveal that standard LoRA fail to meet the
requirement for semantic fidelity and diverse lay-style generation in MLLG task. To
address these limitations, we propose Magical, an asymmetric LoRA architecture
tailored for MLLG under heterogeneous data scenarios. Magical employs a shared
matrix A for abstractive summarization, along with multiple isolated matrices B for
diverse lay-style generation. To preserve semantic fidelity during the lay language
generation process, Magical introduces a Semantic Invariance Constraint
to mitigate semantic subspace shifts on matrix A. Furthermore, to better adapt to
diverse lay-style generation, Magical incorporates the Recommendation-guided
Switch, an externally interface to prompt the LLM to switch between different
matrices B. Experimental results on three real-world lay language generation
datasets demonstrate that Magical consistently outperforms prompt-based methods,...
How Much Can We Forget about Data Contamination?
ICML 2025

How Much Can We Forget about Data Contamination?
Sebastian Bordt 1 Suraj Srinivas 2 Valentyn Boreiko 1 Ulrike von Luxburg 1
Abstract
The leakage of benchmark data into the training
data has emerged as a significant challenge for
evaluating the capabilities of large language mod-
els (LLMs). In this work, we challenge the com-
mon assumption that small-scale contamination
renders benchmark evaluations invalid. First, we
experimentally quantify the magnitude of bench-
mark overfitting based on scaling along three di-
mensions: The number of model parameters (up
to 1.6B), the number of times an example is seen
(up to 144), and the number of training tokens (up
to 40B). If model and data follow the Chinchilla
scaling laws, minor contamination indeed leads to
overfitting. At the same time, even 144 times of
contamination can be forgotten if the training data
is scaled beyond five times Chinchilla, a regime
characteristic of many modern LLMs. Continual
pre-training of OLMo-7B corroborates these re-
sults. Next, we study the impact of the weight
decay parameter on example forgetting, showing
that empirical forgetting occurs faster than the cu-
mulative weight decay. This allows us to gauge
the degree of example forgetting in large-scale
training runs, indicating that many LLMs, includ-
ing Llama 3 405B, have forgotten the data seen at
the beginning of training.
1. Introduction
A core principle of machine learning is that a model should
not be trained on the test set used for evaluation (Donoho,
2017). For foundation models trained on Internet-scale
data, there are increasing concerns that this principle is
violated due to the leakage of benchmark evaluation data
into the training data (Xu et al., 2024; Oren et al., 2024).
Indeed, many LLM developers have found overlap between
1University of T¨ubingen, T¨ubingen AI Center, Germany 2Bosch
Research North America & Bosch Center for Artificial Intelligence
(BCAI), Sunnyvale, USA. Correspondence to: Sebastian Bordt
<sebastian.bo...
Understanding Model Reprogramming for CLIP via Decoupling Visual Prompts
ICML 2025

Understanding Model Reprogramming for CLIP via Decoupling Visual Prompts
Chengyi Cai * 1 Zesheng Ye * 1 Lei Feng 2 3 Jianzhong Qi 1 Feng Liu 1
Abstract
Model reprogramming adapts pretrained models
to downstream tasks by modifying only the in-
put and output spaces. Visual reprogramming
(VR) is one instance for vision tasks that adds
a trainable noise pattern (i.e., a visual prompt)
to input images to facilitate downstream classi-
fication. The existing VR approaches for CLIP
train a single visual prompt using all descriptions
of different downstream classes. However, the
limited learning capacity may result in (1) a fail-
ure to capture diverse aspects of the descriptions
(e.g., shape, color, and texture), and (2) a possi-
ble bias toward less informative attributes that do
not help distinguish between classes. In this pa-
per, we introduce a decoupling-and-reweighting
framework. Our decoupled visual prompts (DVP)
are optimized using descriptions grouped by ex-
plicit causes (DVP-cse) or unsupervised clusters
(DVP-cls). Then, we integrate the outputs of
these visual prompts with a probabilistic reweight-
ing matrix (PRM) that measures their contribu-
tions to each downstream class. Theoretically,
DVP lowers the empirical risk bound. Exper-
imentally, DVP outperforms baselines on aver-
age across 11 downstream datasets.
Notably,
the DVP-PRM integration enables insights into
how individual visual prompts influence classifi-
cation decisions, providing a probabilistic frame-
work for understanding reprogramming.
Our
code is available at https://github.com/
tmlr-group/DecoupledVP
1. Introduction
Model reprogramming (Vinod et al., 2020; Chen, 2024;
Hung et al., 2023) is shown to be an effective approach for
*Equal contribution 1School of Computing and Information Sys-
tems, The University of Melbourne 2School of Computer Science
and Engineering, Southeast University 3Idealism Technology (Bei-
jing). Correspondence to: Feng Liu <fengliu.ml@gmail.com>.
Proceedings of the 4...
Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment
ICLR 2025

Published as a conference paper at ICLR 2025
UNVEILING THE MAGIC OF CODE REASONING
THROUGH REFLECTIVE HYPOTHESIS
DECOMPOSITION AND AMENDMENT
Yuze Zhao1, Tianyun Ji1,∗, Wenjun Feng1,∗, Zhenya Huang1,2,†, Qi Liu1,2,
Zhiding Liu1, Yixiao Ma1, Kai Zhang1, Enhong Chen1
1State Key Laboratory of Cognitive Intelligence,
University of Science and Technology of China
2Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
yuzezhao@mail.ustc.edu.cn huangzhy@ustc.edu.cn
ABSTRACT
The reasoning abilities are one of the most enigmatic and captivating aspects of
large language models (LLMs). Numerous studies are dedicated to exploring and
expanding the boundaries of this reasoning capability. However, tasks that em-
body both reasoning and recall characteristics are often overlooked. In this paper,
we introduce such a novel task, code reasoning, to provide a new perspective for
the reasoning abilities of LLMs. We summarize three meta-benchmarks based
on established forms of logical reasoning, and instantiate these into eight specific
benchmark tasks. Our testing on these benchmarks reveals that LLMs continue
to struggle with identifying satisfactory reasoning pathways. Additionally, we
present a new pathway exploration pipeline inspired by human intricate problem-
solving methods. This Reflective Hypothesis Decomposition and Amendment
(RHDA) pipeline consists of the following iterative steps: (1) Proposing potential
hypotheses based on observations and decomposing them; (2) Utilizing tools to
validate hypotheses and reflection outcomes; (3) Revising hypothesis in light of
observations. Our approach effectively mitigates logical chain collapses arising
from forgetting or hallucination issues in multi-step reasoning, resulting in per-
formance gains of up to 3×. Finally, we expand this pipeline by applying it to
simulate complex household tasks in real-world scenarios, specifically in Virtu-
alHome, enhancing the handling of failure cases. We release our co...
Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
ICML 2025

Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing
Censored Data with Recursively Imputed Trees
Tomer Meir 1 Uri Shalit 1 2 Malka Gorfine 2
Abstract
Tailoring treatments to individual needs is a cen-
tral goal in fields such as medicine. A key step to-
ward this goal is estimating Heterogeneous Treat-
ment Effects (HTE)—the way treatments impact
different subgroups. While crucial, HTE estima-
tion is challenging with survival data, where time
until an event (e.g., death) is key. Existing meth-
ods often assume complete observation, an as-
sumption violated in survival data due to right-
censoring, leading to bias and inefficiency. Cui
et al. (2023) proposed a doubly-robust method
for HTE estimation in survival data under no hid-
den confounders, combining a causal survival for-
est with an augmented inverse-censoring weight-
ing estimator. However, we find it struggles un-
der heavy censoring, which is common in rare-
outcome problems such as Amyotrophic lateral
sclerosis (ALS). Moreover, most current meth-
ods cannot handle instrumental variables, which
are a crucial tool in the causal inference arsenal.
We introduce Multiple Imputation for Survival
Treatment Response (MISTR), a novel, general,
and non-parametric method for estimating HTE
in survival data. MISTR uses recursively imputed
survival trees to handle censoring without directly
modeling the censoring mechanism. Through ex-
tensive simulations and analysis of two real-world
datasets—the AIDS Clinical Trials Group Proto-
col 175 and the Illinois unemployment dataset we
show that MISTR outperforms prior methods un-
der heavy censoring in the no-hidden-confounders
setting, and extends to the instrumental variable
setting. To our knowledge, MISTR is the first non-
parametric approach for HTE estimation with un-
observed confounders via instrumental variables.
1Department of Data and Decisions Sciences, Technion, Haifa,
Israel 2Department of Statistics and Operations Research, Tel Aviv
Univers...
S4S: Solving for a Fast Diffusion Model Solver
ICML 2025

S4S: Solving for a Fast Diffusion Model Solver
Eric Frankel 1 Sitan Chen 2 Jerry Li 1 Pang Wei Koh 1 3 Lillian J. Ratliff 1 Sewoong Oh 1
Abstract
Diffusion models (DMs) create samples from a
data distribution by starting from random noise
and iteratively solving a reverse-time ordinary dif-
ferential equation (ODE). Because each step in
the iterative solution requires an expensive neural
function evaluation (NFE), there has been sig-
nificant interest in approximately solving these
diffusion ODEs with only a few NFEs without
modifying the underlying model. However, in the
few NFE regime, we observe that tracking the
true ODE evolution is fundamentally impossible
using traditional ODE solvers. In this work, we
propose a new method that learns a good solver
for the DM, which we call Solving for the Solver
(S4S). S4S directly optimizes a solver to obtain
good generation quality by learning to match the
output of a strong teacher solver. We evaluate S4S
on six different pre-trained DMs, including pixel-
space and latent-space DMs for both conditional
and unconditional sampling. In all settings, S4S
uniformly improves the sample quality relative to
traditional ODE solvers. Moreover, our method
is lightweight, data-free, and can be plugged in
black-box on top of any discretization schedule
or architecture to improve performance. Building
on top of this, we also propose S4S-Alt, which
optimizes both the solver and the discretization
schedule. By exploiting the full design space of
DM solvers, with 5 NFEs, we achieve an FID
of 3.73 on CIFAR10 and 13.26 on MS-COCO,
representing a 1.5× improvement over previous
training-free ODE methods.
1University of Washington
2Harvard University
3Allen
Institute for AI. Correspondence to:
Eric Frankel <er-
icsf@cs.washington.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
Figure 1. Abstraction of the S4S approach. In low-NFE environ-
ments, co...
U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models
ICLR 2025

Published as a conference paper at ICLR 2025
U-SHAPED AND INVERTED-U SCALING BEHIND
EMERGENT ABILITIES OF LARGE LANGUAGE MODELS
Tung-Yu Wu, Melody Lo
National Taiwan University
{b08901133, peiyulo}@ntu.edu.tw
ABSTRACT
Large language models (LLMs) have been shown to exhibit emergent abilities
in some downstream tasks, where model performance stagnates at first and then
improves sharply and unpredictably with scale beyond a threshold. In this work,
we investigate the phenomenon by grouping questions based on difficulty level
and provide a possible explanation for emergent abilities. Specifically, we observe
U-shaped scaling for hard questions and inverted-U scaling followed by steady
improvement for easy questions. The two scaling patterns initially offset each
other, causing stagnant overall performance. The performance starts to soar when
the scaling pattern of easy questions reverts from inverse to standard scaling,
leading to emergent abilities. Based on this finding, we propose a simple yet
effective pipeline, called Slice-and-Sandwich, to predict the emergence threshold
and model performance beyond the threshold. Our code is publicly available at
https://github.com/tony10101105/ExpEmergence.
1
INTRODUCTION
Large language models (LLMs) (Team et al., 2023; Achiam et al., 2023; Brown, 2020; Touvron
et al., 2023a;b; Workshop et al., 2022; Li et al., 2023; Jiang et al., 2024) have shown strong potential
in various downstream applications (Jumper et al., 2021; Fawzi et al., 2022; Naveed et al., 2023;
Kaddour et al., 2023). Though the training-loss scaling law has been well established (Kaplan
et al., 2020; Hoffmann et al., 2022), the literature is inconclusive regarding how performance on
downstream tasks scales. In particular, for certain downstream tasks (Srivastava et al., 2023; Lin
et al., 2022a; Pilehvar & Camacho-Collados, 2019), LLMs display emergent abilities: performance
stagnates even when model training compute scales up hundredfold, and then improves sharp...
Isolated Causal Effects of Natural Language
ICML 2025

Isolated Causal Effects of Natural Language
Victoria Lin 1 Louis-Philippe Morency 1 Eli Ben-Michael 1
Abstract
As language technologies become widespread, it
is important to understand how changes in lan-
guage affect reader perceptions and behaviors.
These relationships may be formalized as the
isolated causal effect of some focal language-
encoded intervention (e.g., factual inaccuracies)
on an external outcome (e.g., readers’ beliefs).
In this paper, we introduce a formal estimation
framework for isolated causal effects of language.
We show that a core challenge of estimating iso-
lated effects is the need to approximate all non-
focal language outside of the intervention. Draw-
ing on the principle of omitted variable bias, we
provide measures for evaluating the quality of
both non-focal language approximations and iso-
lated effect estimates themselves. We find that
poor approximation of non-focal language can
lead to bias in the corresponding isolated effect
estimates due to omission of relevant variables,
and we show how to assess the sensitivity of ef-
fect estimates to such bias along the two key axes
of fidelity and overlap. In experiments on semi-
synthetic and real-world data, we validate the abil-
ity of our framework to correctly recover isolated
effects and demonstrate the utility of our proposed
measures.
1. Introduction
The widespread use of language technologies has given
rise to an ever-expanding amount of human- and machine-
generated text data. From this vast body of data emerges
the opportunity to understand how information contained in
language relates to real-world outcomes. Elucidating these
relationships can help answer scientifically interesting ques-
tions and provide interpretability to texts and the models
that generate them. For instance, what language attributes
1Carnegie Mellon University, Pittsburgh, PA, USA. Correspon-
dence to: Victoria Lin <vlin2@andrew.cmu.edu>.
Proceedings of the 42 nd International Conference on Machine
Learni...
Alignment of Large Language Models with Constrained Learning
NeurIPS 2025

Alignment of Large Language Models with
Constrained Learning
Botong Zhang
University of Pennsylvania
bzhang16@seas.upenn.edu
Shuo Li
Amazon
lishuo1@seas.upenn.edu
Ignacio Hounie
University of Pennsylvania
ihounie@seas.upenn.edu
Osbert Bastani
University of Pennsylvania
obastani@seas.upenn.edu
Dongsheng Ding∗
University of Tennessee, Knoxville
dongshed@utk.edu
Alejandro Ribeiro
University of Pennsylvania
aribeiro@seas.upenn.edu
Abstract
We study the problem of computing an optimal large language model (LLM)
policy for the constrained alignment problem, where the goal is to maximize
a primary reward objective while satisfying constraints on secondary utilities.
Despite the popularity of Lagrangian-based LLM policy search in constrained
alignment, iterative primal-dual methods often fail to converge, and non-iterative
dual-based methods do not achieve optimality in the LLM parameter space. To
address these challenges, we employ Lagrangian duality to develop an iterative
dual-based alignment method that alternates between updating the LLM policy via
Lagrangian maximization and updating the dual variable via dual descent. In theory,
we characterize the primal-dual gap between the primal value in the distribution
space and the dual value in the LLM parameter space. We further quantify the
optimality gap of the learned LLM policies at near-optimal dual variables with
respect to both the objective and the constraint functions. These results prove that
dual-based alignment methods can find an optimal constrained LLM policy, up to
an LLM parametrization gap. We demonstrate the effectiveness and merits of our
approach through extensive experiments conducted on the PKU-SafeRLHF and
Anthropic HH-RLHF datasets.
1
Introduction
Large language models (LLMs), built upon the transformer architecture [43], have become a core
tool for a wide range of natural language processing tasks (e.g., code generation [17], translation [50],
robotics [37]). Central to these remarkable capabilities ...
TrajAgent: An LLM-Agent Framework for Trajectory Modeling via Large-and-Small Model Collaboration
NeurIPS 2025

TrajAgent: An LLM-Agent Framework for Trajectory
Modeling via Large-and-Small Model Collaboration
Yuwei Du∗, Jie Feng∗†, Jie Zhao, Yong Li†
Department of Electronic Engineering, BRNist, Tsinghua University, Beijing, China
{fengjie,liyong07}@tsinghua.edu.cn
Abstract
Trajectory modeling, which includes research on trajectory data pattern mining
and future prediction, has widespread applications in areas such as life services,
urban transportation, and public administration. Numerous methods have been
proposed to address specific problems within trajectory modeling. However, the
heterogeneity of data and the diversity of trajectory tasks make effective and reliable
trajectory modeling an important yet highly challenging endeavor, even for domain
experts. In this paper, we propose TrajAgent, a agent framework powered by
large language models (LLMs), designed to facilitate robust and efficient trajectory
modeling through automation modeling. This framework leverages and optimizes
diverse specialized models to address various trajectory modeling tasks across
different datasets effectively. In TrajAgent, we first develop UniEnv, an execution
environment with a unified data and model interface, to support the execution
and training of various models. Building on UniEnv, we introduce an agentic
workflow designed for automatic trajectory modeling across various trajectory
tasks and data. Furthermore, we introduce collaborative learning schema between
LLM-based agents and small speciallized models, to enhance the performance
of the whole framework effectively. Extensive experiments on four tasks using
four real-world datasets demonstrate the effectiveness of TrajAgent in automated
trajectory modeling, achieving a performance improvement of 2.38%-69.91% over
baseline methods. The codes and data can be accessed via https://github.
com/tsinghua-fib-lab/TrajAgent.
1
Introduction
With the rapid development of web services and mobile devices [68, 6], large-scale trajectory data,
suc...