#  MedAdapter: Efficient Test-Time Adaptation of Large
Language Models towards Medical Reasoning

Wenqi Shi♠, Ran Xu♡11footnotemark: 1, Yuchen Zhuang♠, Yue Yu♠,
Hang Wu♠, Carl Yang♡, May D. Wang♠

♠ Georgia Tech ♡ Emory University
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>Equal contribution.

###### Abstract

Despite their improved capabilities in generation and reasoning, adapting
large language models (LLMs) to the biomedical domain remains challenging due
to their immense size and corporate privacy. In this work, we propose
MedAdapter, a unified post-hoc adapter for test-time adaptation of LLMs
towards biomedical applications. Instead of fine-tuning the entire LLM,
MedAdapter effectively adapts the original model by fine-tuning only a small
BERT-sized adapter to rank candidate solutions generated by LLMs. Experiments
demonstrate that MedAdapter effectively adapts both white-box and black-box
LLMs in biomedical reasoning, achieving average performance improvements of
25.48% and 11.31%, respectively, without requiring extensive computational
resources or sharing data with third parties. MedAdapter also yields superior
performance when combined with train-time adaptation, highlighting a flexible
and complementary solution to existing adaptation methods. Faced with the
challenges of balancing model performance, computational resources, and data
privacy, MedAdapter provides an efficient, privacy-preserving, cost-effective,
and transparent solution for adapting LLMs to the biomedical domain.

MedAdapter: Efficient Test-Time Adaptation of Large
Language Models towards Medical Reasoning

Wenqi Shi♠††thanks:  Equal contribution., Ran Xu♡11footnotemark: 1, Yuchen
Zhuang♠, Yue Yu♠, Hang Wu♠, Carl Yang♡, May D. Wang♠ ♠ Georgia Tech ♡ Emory
University<EMAIL_ADDRESS><EMAIL_ADDRESS>

##  1 Introduction

Figure 1: Evaluation results on BioASQ. X-axis in log scale. Moderately sized
white-box LLMs consistently _underperform_ larger black-box LLMs, regardless
of fine-tuning on biomedical corpora. However, fine-tuning black-box LLMs
through APIs can pose potential _data privacy risks_ and incur _substantial
costs_.

Large language models (LLMs) OpenAI (2022); Achiam et al. (2023); Team et al.
(2023) have demonstrated superior generation and reasoning capabilities
compared to traditional BERT-size language models, primarily due to massive
number of parameters in LLMs and their extensive pre-training on vast textual
corpora. In the biomedical domain, researchers have developed LLMs that are
either pre-trained (Labrak et al., 2024; Bolton et al., 2024a) or fine-tuned
(Singhal et al., 2023; Chen et al., 2023b) on large-scale domain-specific
corpora to enhance performance on biomedical natural language processing (NLP)
tasks. However, tuning biomedical domain-specific LLMs poses substantial
challenges due to their _immense size_ and _corporate privacy_ , especially
with the constraints of academic resources and the sensitive nature of
clinical data.

Fine-tuning domain-specific LLMs significantly accelerates biomedical
discovery Ye et al. (2023) and improves patient care (Han et al., 2023;
Yunxiang et al., 2023; Wang et al., 2024). However, this process necessitates
complete access to internal parameters, which is currently limited to white-
box LLMs, such as LLaMA-2 (Touvron et al., 2023). Besides, even for moderately
sized LLMs with 7B parameters, the fine-tuning procedure requires substantial
computational resources (Bolton et al., 2024a), often exceeding the
capabilities of many academic and medical centers. Nevertheless, despite these
efforts and resource investments, the performance of these fine-tuned smaller
white-box LLMs (_e.g._ , LLaMA-2) still exhibits a significant discrepancy
with larger black-box LLMs (_e.g._ , GPT-3.5-Turbo), as shown in Figure 1.

Such intrinsic limitations of white-box LLMs intuitively motivate the
exploration of adapting black-box LLMs to the biomedical domain. While it is
possible to fine-tune black-box LLMs like GPT-3.5 (OpenAI, 2022) via third-
party APIs (Peng et al., 2023), this approach presents even greater
challenges: (1) Uploading patient data via APIs poses significant risks of
_privacy leakage_ and potential conflicts with Health Insurance Portability
and Accountability Act (HIPAA) compliance, including unauthorized third-party
access to personally identifiable information (PII) (Lukas et al., 2023; Marks
and Haupt, 2023); (2) Fine-tuning API services could incur prohibitively _high
financial and environmental costs_ (Luccioni et al., 2023), exceeding typical
academic budgets; (3) The opaque fine-tuning process, limited to very few
adjustable hyperparameters within a specific range, often results in
_suboptimal performance_ in downstream tasks (Sun et al., 2024).

To address these challenges, we introduce MedAdapter, a test-time adapter that
fine-tunes a BERT-sized language model (110M) to facilitate the adaptation of
both white-box and black-box LLMs for medical reasoning. Instead of fine-
tuning the entire large model from the source domain, MedAdapter fine-tunes a
small outcome-supervised adapter that ranks candidate solutions generated by
LLMs, effectively adapting the original large model to the target domain by
updating only a small subset of (extra) model parameters.

Extensive experiments on five biomedical QA datasets show the efficacy of
MedAdapter in adapting LLMs to downstream biomedical applications: (1) For
white-box LLMs, MedAdapter reaches 99.35% of supervised fine-tuning
performance using only 14.75% of the GPU memory; and (2) In black-box LLM
adaptation, it achieves comparable performance or even surpasses fine-tuning
APIs at only 15.59% of the budget, while also eliminating the risks associated
with private data sharing. When combined with train-time adaptation,
MedAdapter outperforms either train-time or test-time adaptation alone,
underscoring its utility as a flexible and complementary solution to existing
adaptation methods.

We summarize our contributions as follows:

  * •

We introduce MedAdapter, a unified post-hoc adapter designed to facilitate the
efficient test-time adaptation of both white-box and black-box LLMs for
medical reasoning.

  * •

Compared to supervised fine-tuning of white-box LLMs, MedAdapter achieves
effective domain adaptation using a BERT-sized language model with only 110M
parameters.

  * •

Compared to supervised fine-tuning of black-box LLMs via APIs, MedAdapter
offers a more privacy-preserving, cost-efficient, and transparent alternative,
eliminating the need for access to any model parameters.

  * •

Experiments demonstrate that MedAdapter effectively adapts both white-box and
black-box LLMs for medical reasoning, achieving average performance
improvements of 25.48% and 11.31%, respectively.

##  2 MedAdapter: Adapting LLMs to Medical Reasoning

###  2.1 Preliminaries

Problem Formulation. Test-time adaptation refers to the process of customizing
models to test data that may exhibit distributional deviations from the
original training data. Given a pre-trained LLM $G$ and a training dataset
from the target domain
$\mathcal{D}=\\{(\mathbf{x}_{i},\mathbf{y}_{i})\\}_{i=1}^{|\mathcal{D}|}$,
where $\mathbf{x}_{i}$ typically describes a problem and $\mathbf{y}_{i}$
represents the ground-truth answer. The goal is to adapt the outputs of the
LLM $\mathbf{\hat{y}}_{i}^{s}\in\mathcal{Y}^{S}$ from the general source
domain to a specific target domain $\mathbf{y}^{t}\in\mathcal{Y}^{T}$ for each
input instance $\mathbf{x}_{i}$. Such adaptation can be crucial for enhancing
the capability of a LLM to exhibit biomedical domain-specific reasoning, which
may be underdeveloped in its original outputs. According to the accessibility
of model parameters, existing approaches can be categorized into two main
groups: (1) _white-box_ LLM adaptation, which allows full access to model
parameters, and (2) _black-box_ LLM adaptation, which permits no such access.

Figure 2: An overview of MedAdapter for efficient test-time LLM adaptation
towards medical reasoning.

White-box LLM Adaptation. With model parameters available in white-box LLMs,
the most direct approach for such tasks is supervised fine-tuning (Wei et al.,
2021; Ke et al., 2022; Chung et al., 2024) with the negative log-likelihood
objective on the training data:

$\displaystyle\mathcal{L}_{\text{SFT}}(G)=-\mathbb{E}_{(\mathbf{x},\mathbf{y})\sim\mathcal{D}}\sum_{t=1}^{T}\log
G(\mathbf{y}_{t}|\mathbf{y}_{<t},\mathbf{x}),$ (1)

where $G$ is referred to as a generator of the solutions. In practice, instead
of updating all model parameters, parameter-efficient fine-tuning (PEFT)
methods Houlsby et al. (2019); Liu et al. (2022); Hu et al. (2021) facilitate
the efficient adaptation of large pre-trained models to various downstream
applications by fine-tuning only a small subset of (additional) model
parameters, thereby significantly decreasing the computational and storage
costs.

Black-box LLM Adaptation. State-of-the-art LLMs, including GPT-4 Achiam et al.
(2023), Claude AI (2024), and Gemini Team et al. (2023), adhere to a trend of
non-disclosure of model parameters to the public. Consequently, fine-tuning
these black-box LLMs relies solely on fine-tuning web service APIs, such as
the OpenAI GPT-3.5-turbo fine-tuning API Peng et al. (2023), which lacks
transparency and incurs high costs. In response, recent black-box adaptation
methods Liu et al. (2024); Ormazabal et al. (2023); Huang et al. (2023) have
explored the adjustment of logit biases to modify the output token
probabilities in LLMs, thereby effectively increasing the frequency of tokens
from the target domain appearing in the output while penalizing those from the
source domain. However, such black-box adaptation methods remain inapplicable
to the latest cutting-edge black-box LLMs, such as GPT-3.5-turbo (OpenAI,
2022), due to the unavailability of token probabilities.

###  2.2 Candidate Solutions Generation

For each problem $\mathbf{x}_{i}$ in the training dataset $\mathcal{D}$, we
generate $k$ intermediate candidate reasoning solutions
$\\{\hat{\mathbf{s}}_{i,j}\\}_{j=1}^{k}$ (_e.g._ , chain-of-thought rationales
or multi-step reasonings) and the corresponding answer
$\\{\hat{\mathbf{y}}_{i,j}\\}_{j=1}^{k}$ using greedy decoding with the
language model generator $G$. With access to ground-truth answers
$\mathbf{y}_{i}$, we can verify the correctness of each generated solution
$\hat{\mathbf{y}}_{i,j}$ and assign a corresponding binary correctness label
$z_{i}$ as:

$\displaystyle z_{i}=\mathbbm{1}(\hat{\mathbf{y}}_{i,j}=\mathbf{y}_{i}),\
z_{i}\in\\{0,1\\}.$ (2)

With the generated solutions, we formulate a new dataset for the adapter
training, denoted as:

$\mathcal{D}_{\text{ada}}=\\{(\mathbf{h}_{i,j},z_{i})\mid 1\leq
i\leq|\mathcal{D}|,1\leq j\leq k\\}$,

(3)

where
$\mathbf{h}_{i,j}=[\mathbf{x}_{i}||\hat{\mathbf{s}}_{i,j}||\hat{\mathbf{y}}_{i,j}]$,
represents the concatenation of the medical question and the entire candidate
generation, and $z_{i}$ is a binary label indicating whether
$\hat{\mathbf{y}}_{i,j}$ is a correct or incorrect solution.

###  2.3 Outcome-Supervised Adapter

We train an outcome-supervised adapter (_i.e._ , verifier) that assesses the
probability of correctness for a candidate solution relative to a given
problem. During inference, the language model $G$ generates a set of candidate
solutions, and the one ranked highest by the verifier is selected as the final
answer, aligning closely with the target domain. More specifically, given a
medical reasoning problem $\mathbf{x}$ and its corresponding candidate
solutions $\hat{\mathbf{y}}$, the outcome verifier
($V:\mathcal{X}\times\mathcal{Y}\to\mathbb{R}$) assigns a normalized
adaptation score, ranging from 0 to 1, to each solution to indicate the
correctness.

In MedAdapter, we fine-tune a BERT-sized language model $\theta$ ($\sim 110$M
parameters), to function as an outcome-supervised adapter on
$\mathcal{D}_{\text{ada}}$. It employs a combination of language modeling and
binary classification as the objective function:

$\displaystyle\mathcal{L}_{\text{ada}}=z\log
V_{\theta}(\mathbf{h})+(1-z)\log(1-V_{\theta}(\mathbf{h})),$ (4)

where $z$ is the binary label verified against the ground-truth answer
provided in $\mathcal{D}_{\text{ada}}$, and $V_{\theta}(\mathbf{h})$ is the
sigmoid adaptation score of the question-solution pair $\mathbf{h}$ assigned
by the adapter model.

###  2.4 Best-of-$K$ Inference

During the inference stage, for each test question $\mathbf{x}_{i}$, we adopt
the best-of-$K$ inference, often referred to as rejection sampling. We
initially sample $K$ candidate solutions
$\\{\hat{\mathbf{s}}_{i,j},\hat{\mathbf{y}}_{i,j}\\}_{j=1}^{K}$ from the
generator $G$. The solution with the highest adaptation score is then selected
as the final answer:

$\displaystyle\hat{\mathbf{y}}_{i}=\arg\max_{j=1,\cdots,K}r_{\theta}([\mathbf{x}_{i}||\hat{\mathbf{s}}_{i,j}||\hat{\mathbf{y}}_{i,j}]).$
(5)

##  3 Experiments

###  3.1 Experimental Setups

Datasets. We evaluate MedAdapter on five widely used medical QA datasets: (1)
MedMCQA (Pal et al., 2022), (2) MedQA (Jin et al., 2021), (3) MMLU (Hendrycks
et al., 2020), (4) PubMedQA (Jin et al., 2019), and (5) BioASQ (Tsatsaronis et
al., 2015). Dataset statistics are available in Table 1. For detailed
information and examples from all datasets, please refer to Appendix A.

Table 1: Dataset statistics. Dataset | # Train | # Test | Source
---|---|---|---
MedMCQA (Pal et al., 2022) | 3000 | 4183 | Exam
MedQA (Jin et al., 2021) | 10178 | 1273 | Exam
MMLU (Hendrycks et al., 2020) | 1299 | 163 | Exam
PubMedQA (Jin et al., 2019) | 450 | 500 | Literature
BioASQ (Tsatsaronis et al., 2015) | 494 | 124 | Literature

Baselines. We conduct our main experiments using both white-box and black-box
backbone LLMs. Following Bolton et al. (2024a), we adopt accuracy as the main
evaluation metric for biomedical QA tasks. We employ the _Chain-of-Thoughts_
(CoT) results (Wei et al., 2022) as the baseline performance for all backbone
LLMs without adaptation.

For white-box LLM adaptation, we primarily compare MedAdapter against
_supervised fine-tuning_ , which updates all the internal model parameters and
serves as the upper-performance benchmark. We also adapt widely used open-
source LLaMA models (Touvron et al., 2023) across various versions and scales,
as well as medical domain-specific LLMs like BioMistral-7B Labrak et al.
(2024) for a comprehensive evaluation.

For black-box LLM adaptation, we focus on the comparison between MedAdapter
and _supervised fine-tuning_ using the Microsoft Azure OpenAI fine-tuning API
service (Peng et al., 2023). In addition, we compare MedAdapter with other
privacy-preserving solutions, including _self-consistency_ (Wang et al., 2023)
and medical domain-specific _retrieval-augmented generation_ (RAG) (Xiong et
al., 2024), which do not require uploading training data to third parties.

Implementation Details. In this work, we employ LongFormer-Base (110M)
(Beltagy et al., 2020) as the base language model for MedAdapter. We set $k=8$
for all generation of intermediate candidate reasoning solutions using
MedAdapter. For black-box LLM adaptation, gpt-3.5-turbo-1106 serves as the
main backbone LLM. For white-box LLM adaptation, we leverage LLaMA-2-7B as the
backbone LLM. During the fine-tuning phase, learning rates are set to $2e-5$
for MedAdapter and $2e-4$ for supervised fine-tuning and LoRA (Hu et al.,
2021), respectively. The global batch size is maintained at 8 for all white-
box adaptation experiments. In the case of LoRA, we train the base LLM with
$r=1024$, $\alpha=2048$, bfloat16 quantization, and DeepSpeed gradient
checkpointing (Rasley et al., 2020). All experiments are conducted on four
NVIDIA A100 GPUs, accommodating a maximum sequence length of $512$ tokens.
Prompt details are available in Appendix B.

###  3.2 Main Results

Table 2: Main results (accuracy) of adapting white-box LLMs to biomedical QA tasks. $\dagger$ denotes the upper bound in theory using supervised fine-tuning (SFT). Dataset ($\rightarrow$) | MedMCQA | MedQA | MMLU-Med | PubMedQA | BioASQ
---|---|---|---|---|---
Method ($\downarrow$) $/$ Metrics ($\rightarrow$) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%)
LLaMA-2-7B Touvron et al. (2023) | 16.00 | - | 16.42 | - | 20.13 | - | 17.00 | - | 16.13 | -
+MedAdapter | 32.00 | +16.00 | 32.52 | +16.10 | 27.67 | +7.54 | 58.00 | +41.00 | 62.90 | +46.77
+SFT† | 42.86 | - | 33.39 | - | 28.22 | - | 60.80 | - | 63.31 | -
BioMistral-7B Labrak et al. (2024) | 28.95 | - | 29.77 | - | 33.33 | - | 26.20 | - | 28.53 | -
+MedAdapter | 30.31 | +1.36 | 34.88 | +5.11 | 46.54 | +13.21 | 33.20 | +7.00 | 33.06 | +4.53
LLaMA-3-8B | 20.44 | - | 27.81 | - | 25.16 | - | 19.00 | - | 30.65 | -
+MedAdapter | 32.08 | +11.64 | 32.44 | +4.63 | 35.22 | +10.06 | 55.00 | +36.00 | 64.52 | +31.46
LLaMA-2-13B Touvron et al. (2023) | 19.66 | - | 28.04 | - | 22.01 | - | 47.40 | - | 19.66 | -
+MedAdapter | 32.00 | +12.34 | 37.47 | +9.43 | 33.96 | +11.95 | 63.60 | +16.20 | 65.32 | +45.66
Table 3: Main results (accuracy) of adapting black-box LLMs to biomedical QA tasks. $\dagger$ denotes the upper bound in theory, using supervised fine-tuning (SFT) via Microsoft Azure OpenAI fine-tuning API services. Dataset ($\rightarrow$) | MedMCQA | MedQA | MMLU-Med | PubMedQA | BioASQ
---|---|---|---|---|---
Method ($\downarrow$) $/$ Metrics ($\rightarrow$) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%)
gpt-3.5-turbo OpenAI (2022) | 49.74 | - | 61.51 | - | 59.75 | - | 56.00 | - | 84.68 | -
+MedAdapter | 59.02 | +9.28 | 68.66 | +7.15 | 73.58 | +13.83 | 73.40 | +17.40 | 93.55 | +8.87
+Azure-SFT† Peng et al. (2023) | 61.82 | - | 63.32 | - | 70.55 | - | 71.40 | - | 95.16 | -
+Self-Consistency (Wang et al., 2023) | 56.20 | - | 67.71 | - | 69.81 | - | 71.60 | - | 87.90 | -
+MedRAG Xiong et al. (2024) | 51.80 | - | 64.36 | - | 68.85 | - | 50.00 | - | 87.55 | -
+MedAdapter | 56.20 | +4.40 | 67.16 | +2.80 | 74.86 | +6.01 | 63.00 | +13.00 | 94.42 | +6.87
gpt-4 Achiam et al. (2023) | 69.48 | - | 83.90 | - | 85.53 | - | 69.20 | - | 92.74 | -
+MedAdapter | 72.09 | +2.61 | 84.13 | +0.23 | 87.42 | +1.89 | 77.40 | +8.20 | 95.97 | +3.23

#### White-box LLM Adaptation.

In Table 2, we summarize the experimental results of adapting white-box LLMs
for medical reasoning tasks. Across all five biomedical question-answering
datasets, MedAdapter consistently outperforms the original white-box LLM,
LLaMA-2-7B (Touvron et al., 2023), with an average performance improvement of
25.48%, demonstrating the adaptability of MedAdapter towards biomedical
domain-specific applications. Notably, MedAdapter demonstrates its efficiency
by achieving 93.33% of the performance level of the fully supervised fine-
tuning model while only updating an adapter comprising 110M parameters, which
constitutes merely 1.57% of the parameters (7B) of the original model.
Additionally, MedAdapter effectively improves the performance of domain-
specific LLMs like BioMistral-7B (Labrak et al., 2024) and general-domain LLMs
at different scales, such as LLaMA-3-8B and LLaMA-2-13B (Touvron et al.,
2023), demonstrating a generalizable solution for white-box LLM domain-
specific adaptation.

#### Black-box LLM Adaptation.

Table 3 presents a comprehensive evaluation of black-box LLM adaptation in
medical reasoning. As expected, black-box LLMs, with their extensive model
parameters and large pre-training corpora, significantly outperform white-box
LLMs (Table 2) across all five biomedical QA tasks. We also observe that
MedAdapter successfully adapts gpt-3.5-turbo (OpenAI, 2022) across all five
datasets, achieving an average performance improvement of 11.31% without
necessitating the sharing of local training samples with third parties.
Notably, MedAdapter achieves competitive or even superior performance compared
to supervised fine-tuning via Microsoft Azure APIs, which serve as the upper
bound in theory. This may be due to the opacity of the fine-tuning service,
which only allows access to a very limited number of adjustable parameters
within a prescribed range111In the Microsoft OpenAI fine-tuning service, users
are permitted to modify only three hyperparameters within a limited range: (1)
the number of epochs, (2) the batch size, and (3) the learning rate
multiplier., leading to suboptimal fine-tuning performance. Details for
parameter studies of supervised fine-tuning via Microsoft Azure APIs are
available in Appendix C.

MedAdapter demonstrates more effective adaptation compared to other privacy-
preserving methods, such as self-consistency (Wang et al., 2023) and MedRAG
(Xiong et al., 2024). We could also extend MedAdapter for RAG and more
advanced LLMs such as gpt-4 (Achiam et al., 2023), demonstrating a flexible
and generalizable solution for adapting black-box LLMs in medical reasoning.
Specifically, we observe only a slight improvement or even a decrease in
performance when adapting RAG-based methods compared to direct adaptations of
backbone black-box LLMs. This can be attributed to the conditional generation
nature of RAG, which typically results in less diverse candidate solutions for
MedAdapter to select from.

###  3.3 Cost Estimation

Table 4: Cost ($\$$) estimations of adapting black-box LLMs to biomedical QA tasks based on gpt-35-turbo-1106. Dataset ($\rightarrow$) | MedMCQA | MedQA | MMLU-Med | PubMedQA | BioASQ
---|---|---|---|---|---
Method ($\downarrow$) $/$ Costs ($\$$) | Training | Inference | Training | Inference | Training | Inference | Training | Inference | Training | Inference
gpt-3.5-turbo OpenAI (2022) | - | 1.37 | - | 0.67 | - | 0.06 | - | 0.16 | - | 0.03
+MedAdapter | 7.67 | 10.40 | 42.57 | 5.37 | 3.49 | 0.44 | 0.92 | 1.14 | 1.41 | 0.35
+Azure-SFT Peng et al. (2023) | 71.18 | 10.88 | 172.85 | 6.83 | 38.93 | 3.18 | 38.17 | 3.76 | 38.48 | 3.24
+OpenAI-SFT∗ | 23.07 | 32.87 | 195.45 | 16.10 | 4.01 | 3.12 | 15.76 | 1.34 | 6.77 | 1.05
∗This is an estimated cost, as the OpenAI-SFT is not compliant with HIPAA
regulations.

Table 4 compares the cost estimations of different black-box LLM adaptation
methods. Compared to the Microsoft OpenAI service, which achieves an average
improvement of 10.11% over the backbone LLM, MedAdapter obtains an improvement
of 11.31% at only 15.59% of the cost during the fine-tuning stage. This is
because MedAdapter relies on inference APIs ($1 per 1M token) to generate
candidate solutions, which is significantly less expensive than using fine-
tuning APIs ($8 per 1M token). Moreover, customized models accessed through
APIs incur 1.58$\times$ higher costs during the inference stage than
MedAdapter due to the increased prices for input ($3 per 1M tokens) and output
($6 per 1M tokens) usage compared to the original models ($1 per 1M tokens for
input usage and $2 per 1M tokens for output usage).

In addition, we also report an estimated cost through the OpenAI supervised
fine-tuning API 222https://openai.com/pricing without implementation due to
the conflict with HIPAA compliance, which is significantly higher than
MedAdapter in both the fine-tuning and inference stages. Notably, there are
differences between the Microsoft Azure OpenAI fine-tuning API service and the
OpenAI fine-tuning API: (1) Microsoft Azure service charges based on training
hours, including an additional hosting cost for model deployment, and (2)
OpenAI fine-tuning API incurs a higher cost per token for both training and
inference but does not include additional hosting fees.

###  3.4 Parameter Efficiency

Table 5: GPU memory (GiB) usage estimations of adapting white-box LLMs to biomedical QA tasks.  Dataset ($\rightarrow$) | BioASQ
---|---
Method ($\downarrow$) $/$ Memory (GiB) | Training | Inference | Acc. ($\%$)
LLaMA-2-7B Touvron et al. (2023) | - | 25.42 | 16.13
+MedAdapter | 11.60 | 33.00 | 62.90
+SFT-LoRA Hu et al. (2021) | 54.76 | 34.65 | 50.00
+SFT | 78.65 | 25.42 | 63.31

Table 5 evaluates the GPU memory (GiB) usage of different white-box LLMs
adaptation methods, including PEFT methods like LoRA (Hu et al., 2021).
Compared to supervised fine-tuning of a LLaMA-2-7B (Touvron et al., 2023),
MedAdapter achieves competitive performance while only fine-tuning a
110M-parameter model, using 14.75% of the GPU memory. Compared to other
parameter-efficient adaptation methods, such as LoRA (Hu et al., 2021), which
updates approximately 170M parameters, MedAdapter demonstrates a 12.90%
improvement in model performance while utilizing only 21.18% of the GPU
memory. We also observe MedAdapter requires a slightly higher GPU memory usage
during the inference stage, as it requires loading the original model.
However, this usage remains lower than that required for supervised fine-
tuning or LoRA.

###  3.5 Scale-up Analysis

In Figure 3, we explore the impact of scaling up the base model of MedAdapter
from 110M to 2.7B parameters, utilizing both general-domain and biomedical
domain-specific language models. Additional model details for the scale-up
analysis are available in Appendix D. Interestingly, we observe very limited
or no improvement with the increase in model size, potentially due to the
following reasons: (1) MedAdapter serves as a scoring function that heavily
relies on language comprehension rather than generative capabilities, which is
a natural fit to encoder-only model; and (2) the limited fine-tuning data
available may allow smaller models to more effectively capture underlying
patterns within the candidate solutions. Additionally, domain-specific
language models exhibit slightly superior performance, likely due to the
integration of more targeted knowledge during their pre-training phase.

(a) General LMs.

(b) Biomedical LMs.

Figure 3: Scale-up performance on multiple general and biomedical domain-
specific language models (LMs) serving as the base model of MedAdapter.
General LMs include LongFormer-Base (110M), LongFormer-Large (330M), Phi-1.5
(1.3B), and Phi-2 (2.7B). Biomedical LMs include Clinical-LongFormer (110M)
and BioMedLM (2.7B). The dashed line denotes the performance of the base
model, gpt-3.5-turbo.

###  3.6 Complementary Analysis

Table 6: Results of combining training-time and test-time adaptation of both black-box and white-box LLMs for biomedical QA tasks. Bold indicates the best performance for each task. Dataset ($\rightarrow$) | MedMCQA | MedQA | MMLU-Med | PubMedQA | BioASQ
---|---|---|---|---|---
Method ($\downarrow$) $/$ Metrics ($\rightarrow$) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%) | Acc. (%) |  $\Delta$ (%)
LLaMA-2-7B Touvron et al. (2023) | 16.00 | - | 16.42 | - | 20.13 | - | 17.00 | - | 16.13 | -
+MedAdapter | 32.00 | +16.00 | 32.52 | +16.10 | 27.67 | +7.54 | 58.00 | +41.00 | 62.90 | +46.77
+SFT | 42.86 | - | 33.39 | - | 28.22 | - | 60.80 | - | 63.31 | -
+MedAdapter | 44.85 | +1.99 | 40.61 | +7.22 | 35.85 | +7.63 | 68.00 | +7.20 | 66.94 | +3.63
+SFT-LoRA Hu et al. (2021) | 28.95 | - | 23.89 | - | 24.54 | - | 55.00 | - | 50.00 | -
+MedAdapter | 35.69 | +6.74 | 28.04 | +4.15 | 31.90 | +7.36 | 62.90 | +7.90 | 60.48 | +10.48
gpt-3.5-turbo OpenAI (2022) | 49.74 | - | 61.51 | - | 59.75 | - | 56.00 | - | 84.68 | -
+MedAdapter | 59.02 | +9.28 | 68.66 | +7.15 | 73.58 | +13.83 | 73.40 | +17.40 | 93.55 | +8.87
+Azure-SFT Peng et al. (2023) | 61.82 | - | 63.32 | - | 70.55 | - | 71.40 | - | 95.16 | -
+MedAdapter | 65.50 | +3.68 | 68.89 | +5.57 | 76.73 | +6.18 | 77.00 | +5.60 | 95.97 | +0.81

In Table 6, we perform a complementary analysis to demonstrate the flexibility
of MedAdapter by integrating both train-time and test-time adaptation.
MedAdapter yields additional performance improvement of 5.53% and 4.37% for
white-box and black-box LLMs, respectively, over train-time adaptation (_i.e._
, supervised fine-tuning). When combined with train-time adaptation,
MedAdapter outperforms either train-time or test-time adaptation alone,
demonstrating its utility as a flexible solution that complements existing
train-time adaptation (_e.g._ , LoRA) (Hu et al., 2021) and even test-time
adaptation (_e.g._ , MedRAG in Table 3) Xiong et al. (2024).

##  4 Related Works

#### Train-Time Adaptation of White-Box LLMs.

To enhance the biomedical domain-specific capabilities of LLMs, prior research
has employed large-scale domain-specific corpora to customize white-box LLMs
for medical reasoning, including: (1) _Pre-Training_ on domain specific
corpora (Luo et al., 2022; Chen et al., 2023b; Bolton et al., 2024a); (2)
_Fine-Tuning_ with medical instructions (Han et al., 2023; Labrak et al.,
2024; Yunxiang et al., 2023; Wu et al., 2024; Xu et al., 2024b), and (3)
_Parameter-Efficient Fine-Tuning (PEFT)_ (Gema et al., 2023). Pre-training or
fine-tuning LLMs necessitates substantial computational resources,
particularly as model sizes continue to increase, which may not be readily
accessible to academic or medical researchers (Bolton et al., 2024b). For
example, Biomistral (Labrak et al., 2024) requires significant computational
resources, which entails approximately 5K computing hours of A100 80GB GPU.
While PEFT-based adaptation (Ding et al., 2023) offers a practical solution by
fine-tuning only a small subset of parameters, this approach might yield
suboptimal performance.

BioMedLM Bolton et al. (2024a) strikes a balance between domain-specific model
performance and computational cost by exclusively pre-training a moderately
sized LLM (2.7B) on a substantial biomedical corpus comprising 34.6 billion
tokens. Alternatively, MedAdapter offers a different test-time solution by
leveraging the emerging generative capabilities of LLMs, avoiding exclusive
training on large-scale domain-specific data while utilizing significantly
fewer model parameters.

#### Train-Time Adaptation of Black-Box LLMs.

Black-box LLMs, such as GPT-4 (Nori et al., 2023a) and Gemini (Saab et al.,
2024), have set new standards for SOTA performance on biomedical NLP tasks
with their inherent capabilities. Despite these advancements, there remains
potential for improvement in domain-specific applications through domain
specialization (Chen et al., 2023a). For example, Jablonka et al. (2024) fine-
tune GPT models for a wide range of chemistry QA tasks, ranging from the
properties of materials to materials synthesis, using OpenAI fine-tuning APIs.
However, the OpenAI fine-tuning API is not compliant with HIPAA regulations
and cannot be used directly for biomedical data that contains patient
information. While the Microsoft Azure OpenAI fine-tuning API service is
HIPAA-compliant, it still poses significant risks when it comes to data
sharing through external APIs (Shi et al., 2024a) and entails substantial
costs for model fine-tuning and deployment. MedAdapter offers an alternative
approach for adapting black-box LLMs without the use of APIs, thereby greatly
enhancing data privacy during training and substantially reducing associated
API costs.

#### Test-Time Adaptation of LLMs.

Test-time adaptation involves customizing models to test data that may differ
in distribution from the original training data (Liang et al., 2023; Yu et
al., 2023). We adopt a slightly different definition of test-time adaptation
than several existing computer vision studies (Zancato et al., 2023; Karmanov
et al., 2024; Ahmed et al., 2024); we only require that target domain label
information remain invisible to the original LLM. Under this definition,
existing methods for test-time adaptation of LLMs towards medical reasoning
include: (1) _Prompting_ -based methods which directly inject medical
knowledge into prompts (Nori et al., 2023a; Lu et al., 2023; Xu et al., 2023;
Jin et al., 2024; Shi et al., 2024b); and (2) _Retrieval-Augmented Generation
(RAG)_ -based methods that leverage domain-specific corpus as plug-in (Xiong
et al., 2024; Shi et al., 2023; Jeong et al., 2024; Xu et al., 2024a).
MedAdapter introduces a third option for test-time adaptation of LLMs in
medical reasoning by training a small adapter to score the candidate solutions
generated by large models, thereby eliminating the need for fine-tuning the
original LLM while still effectively facilitating target domain adaptation.

##  5 Conclusion

In this work, we propose MedAdapter to address a unique challenge in adopting
LLMs in real-world clinical scenarios with limited computational resources and
strict privacy requirements. MedAdapter strikes a balance between effective
model adaptation and reasonable computational costs by employing a BERT-sized
language model as an adapter to select candidate solutions generated by larger
LLMs, thereby obviating the need to fine-tune the entire LLMs. Extensive
experiments across five biomedical datasets demonstrate the effectiveness of
MedAdapter in adapting both white-box and black-box LLMs. Notably, MedAdapter
(1) achieves 99.35% of the performance of supervised fine-tuning in white-box
adaptation while using only 14.75% of the GPU memory, and (2) achieves
comparable or even outperforms the performance of fine-tuning black-box LLMs
through APIs, eliminating the need to share data and incurring only 15.59% of
the costs. MedAdapter may offer a practical solution for effectively, privacy-
preservingly, cost-effectively, and transparently adapting LLMs to real-world
biomedical research and practice.

## Limitations

In this work, we propose MedAdapter for test-time adaptation of LLMs in
medical reasoning applications. However, we have identified several
limitations of MedAdapter: (1) Access to Label Information: While target
domain label information remains invisible to the original LLMs, MedAdapter
still requires access to such information to fine-tune a small adapter. This
may not be feasible in some real-world scenarios where label information is
restricted or unavailable. (2) On-Device Inference: In the adaptation of
black-box LLMs, the fine-tuning process does not share any data with third
parties through APIs; however, it cannot handle queries involving sensitive or
patient-identifiable information during the inference stage. Furthermore, the
extensive model parameters of black-box LLMs eliminate the possibility of on-
device inference. (3) Resource Limitations: Due to restricted access to fine-
tuning API services and budget constraints, our experiments with black-box
fine-tuning are limited to using GPT-3.5-Turbo via the Microsoft Azure fine-
tuning API service.

## Privacy and Ethics Statement

In strict adherence to the PhysioNet Credentialed Health Data Use Agreement
1.5.0333https://physionet.org/about/licenses/physionet-credentialed-health-
data-license-150/, we expressly forbid the dissemination of confidential
patient information to any third party, including via online services such as
APIs. To guarantee the responsible utilization of Azure OpenAI Service in
accordance with the guideline444https://physionet.org/news/post/gpt-
responsible-use, we have deliberately withdrawn from the human review process
by submitting the Azure OpenAI Additional Use Case
Form555https://aka.ms/oai/additionalusecase. It effectively precludes third
parties from accessing and processing protected health information (PHI) for
any purpose. We maintain a rigorous and ongoing monitoring process to ensure
our compliance with these guidelines and pertinent privacy legislation,
thereby upholding the highest ethical standards in the use of data throughout
our research and operational activities.

## Acknowledgments

This research has been partially supported by Accelerate Foundation Models
Academic Research Initiative from Microsoft Research, Wallace H. Coulter
Distinguished Faculty Fellowship, Petit Institute Faculty Fellowship, and
research funding from Amazon and Microsoft Research.

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##  Appendix A Dataset Details

We evaluate the domain adaptation capabilities of both white-box and black-box
LLMs in medical reasoning tasks using five well-established biomedical QA
datasets. We have selected these datasets due to their extensive utilization
in assessing the language comprehension and reasoning capabilities of LLMs in
the medical domain (Bolton et al., 2024a; Xiong et al., 2024; Tang et al.,
2023; Chen et al., 2023b; Luo et al., 2023; Zhou et al., 2023; Nori et al.,
2023b; Frisoni et al., 2024; Maharjan et al., 2024; Jeong et al., 2024).
Example questions and answers from each dataset are available in Table 9.

#### MedMCQA.

MedMCQA666https://medmcqa.github.io (Pal et al., 2022) is a large-scale and
comprehensive dataset for multi-choice (four-option) medical question
answering. It is derived from real-world medical entrance exam questions
(Indian AIIMS and NEET-PG) and consists of over 194,000 high-quality medical
questions. These questions cover 2,400 healthcare topics and 21 medical
subjects, exhibiting a wide range of topical diversity. The average token
length is 12.77.

#### MedQA.

MedQA777https://github.com/jind11/MedQA (Jin et al., 2021) is a multi-choice
question-answering dataset collected from the professional medical board exam,
the United States Medical License Exams (USMLE). It comprises 12,723 questions
sourced from a comprehensive collection of 18 English medical textbooks that
have been extensively utilized by medical students and USMLE candidates.
Questions in MedQA cover a wide range of topics in clinical medicine,
necessitating responses with professional expertise and complex multi-hop
reasoning across multiple pieces of evidence. The average question and option
length is 116.6 and 3.5, respectively.

#### MMLU.

MMLU888https://github.com/hendrycks/test (Hendrycks et al., 2020) is a
comprehensive multi-task language understanding test dataset that encompasses
57 tasks across various domains such as mathematics, history, computer
science, law, and _etc._ In our experiments, we specifically focus on a subset
of seven medical reasoning-related tasks (Singhal et al., 2023), including
clinical knowledge, college biology, college medicine, high school biology,
medical genetics, professional medicine, and virology.

#### PubMedQA.

PubMedQA999https://pubmedqa.github.io (Jin et al., 2019) is a biomedical
question and answering dataset derived from PubMed abstracts. It contains 1k
expert-annotated multi-choice question-and-answer samples based on 211.3k
PubMed articles. The task of PubMedQA is to provide answers to research
questions with yes/no/maybe responses based on the corresponding abstracts.
The average question and context length is 14.4 and 238.9, respectively.

#### BioASQ.

BioASQ101010https://github.com/AKSW/BioASQ-AT (Tsatsaronis et al., 2015) is a
large-scale biomedical semantic indexing and question-answering dataset. It
includes tasks related to information retrieval (Task A) and machine reading
comprehension (Task B). Similar to PubMedQA (Jin et al., 2019), BioASQ
leverages biomedical scientific articles, providing text fragments that serve
as the ground truth for machine reading comprehension. Following Xiong et al.
(2024), we focus on 618 machine reading comprehension questions (Task B) with
binary (yes/no) answers from the most recent five years (from 2019 to 2023).
The average token length of each question is 17.

##  Appendix B Prompt Details

###  B.1 MedMCQA

The prompting format for MedMCQA dataset is listed as follows:

<MedMCQA> Prompt

Use the step-by-step method as shown in the

example to answer the question. You should

give the explanation steps and final answer

based on the provided context.

Example:

Q: What is the most probable poal of entry of

Aspergillus? (A) Puncture wound, (B) Blood,

(C) Lungs, (D) Gastrointestinal tract

A: Aspergillus species are widely distributed

on decaying plants, producing chains of

conidia.

Aspergillus species unlike Candida species do

not form the pa of normal flora of humans.

They are ubiquitous in the environment; hence

transmission of infection is mostly

exogenous.

Aspergillus transmission occurs by inhalation

of airborne conidia.

Risk Factors for invasive aspergillosis are:

Glucocoicoid use (the most impoant risk

factor) Profound neutropenia or Neutrophil

dysfunction Underlying pneumonia or COPD,

tuberculosis or sarcoidosis Antitumor

necrosis factor therapy.

#### C.

Here is your question. Please respond to this

question based on the context and by

adhering to the given format: provide

step-by-step reasoning (one sentence per

line), then give the final answer (A/B/C/D)

after ’####’.

###  B.2 MedQA

The prompting format for MedQA dataset is listed as follows:

<MedQA> Prompt

Use the step-by-step method as shown in the

example to answer the question. You should

give the reasoning steps and final answer

based on the provided context.

Example:

Q: A 21-year-old sexually active male complains

of fever, pain during urination, and

inflammation and pain in the right knee. A

culture of the joint fluid shows a bacteria

that does not ferment maltose and has no

polysaccharide capsule. The physician

orders antibiotic therapy for the patient.

The mechanism of action of action of the

medication given blocks cell wall synthesis,

which of the following was given? (A)

Gentamicin, (B) Ciprofloxacin, (C)

Ceftriaxone, (D) Trimethoprim.

A: The symptoms and culture results suggest a

bacterial infection that affects both the

urinary tract and joints, indicating a

systemic infection.

Bacteria that do not ferment maltose and lack a

polysaccharide capsule could indicate a

variety of bacteria, but the treatment

approach focuses on the mechanism of action

of the antibiotic rather than the specific

bacteria.

Antibiotics that block cell wall synthesis are

typically beta-lactams, which include

penicillins and cephalosporins.

Gentamicin is an aminoglycoside antibiotic,

which works by inhibiting protein

synthesis.

Ciprofloxacin is a fluoroquinolone, which works

by inhibiting bacterial DNA gyrase and

topoisomerase IV, affecting DNA replication.

Ceftriaxone is a third-generation cephalosporin,

which works by inhibiting cell wall

synthesis.

Trimethoprim is an antibiotic that inhibits

bacterial dihydrofolate reductase,

affecting folic acid synthesis.

#### C.

Here is your question. Please respond to this

question based on the context and by

adhering to the given format: provide

step-by-step reasoning (one sentence per

line), then give the final answer (A/B/C/D)

after ’####’.

###  B.3 MMLU-Med

The prompting format for MMLU-Med dataset is listed as follows:

<MMLU-Med> Prompt

Use the step-by-step method as shown in the

example to answer the question. You should

give the reasoning steps and final answer

based on the provided context.

Example:

Q: What size of cannula would you use in a

patient who needed a rapid blood

transfusion (as of 2020 medical knowledge)?

(A) 18 gauge, (B) 20 gauge, (C) 22 gauge,

(D) 24 gauge.

A: The gauge of a cannula indicates its

diameter: the smaller the number, the

larger the diameter of the cannula.

A larger diameter cannula allows for the rapid

administration of fluids, including blood.

In emergency situations requiring rapid

transfusion, a larger cannula is preferred

to ensure quick delivery of blood to the

patient.

An 18 gauge cannula is larger than the 20, 22,

and 24 gauge options and is commonly used

for rapid transfusions.

#### A.

Here is your question. Please respond to this

question based on the context and by

adhering to the given format: provide

step-by-step reasoning (one sentence per

line), then give the final answer (A/B/C/D)

after ’####’.

###  B.4 PubMedQA

The prompting format for PubMedQA dataset is listed as follows:

<PubMedQA> Prompt

Use the step-by-step method as shown in the

example to answer the question. You should

give the reasoning steps and final answer

based on the provided context.

Example:

Q: Do familiar teammates request and accept

more backup?

A: Transactive memory theory extends to

high-stress environments in which members’

expertise is highly overlapping.

Teammates’ shared mental models about one

another increase the likelihood that they

will request and accept backup.

#### Yes.

Here is your question. Please respond to this

question based on the context and by

adhering to the given format: provide

step-by-step reasoning (one sentence per

line), then give the final answer

(Yes/No/Maybe) after ’####’.

###  B.5 BioASQ

The prompting format for BioASQ dataset is listed as follows:

<BioASQ> Prompt

Use the step-by-step method as shown in the

example to answer the question. You should

give the reasoning steps and final answer

based on the provided context.

Example:

Q: Can losartan reduce brain atrophy in

Alzheimer’s disease?

A: Losartan is primarily used for hypertension

and may indirectly affect factors

associated with Alzheimer’s disease

progression.

Despite potential neuroprotective effects, such

as reducing inflammation and oxidative

stress, there is limited direct evidence

linking losartan to reduced brain atrophy

in Alzheimer’s disease.

Clinical trials specifically targeting this

outcome are necessary to establish a

definitive effect.

#### no

Here is your question. Please respond to this

question based on the context and by

adhering to the given format: provide

step-by-step reasoning (one sentence per

line), then give the final answer (yes/no)

after ’####’.

Table 7: Grid search of fine-tuning GPT-3.5-Turbo through Microsoft Azure fine-tuning API service. Bold denotes the optimal results chosen as a reference for Azure-SFT in Table 3.  LRM | Epoch | MedMCQA | MedQA | MMLU | PubMedQA | BioASQ
---|---|---|---|---|---|---
0.1 | 3 | 57.87 | 63.32 | 64.78 | 68.80 | 95.16
1 | 3 | 59.69 | 62.92 | 70.55 | 68.60 | 95.97
0.1 | 5 | 61.82 | 60.75 | 67.48 | 71.40 | 91.94
Table 8: Details of base language models (LMs) for scale-up analysis. Type | Size | Model
---|---|---
General LM | 110M | LongFormer-Base (Beltagy et al., 2020)
General LM | 330M | LongFormer-Large (Beltagy et al., 2020)
General LM | 1.3B | Phi-1.5 (Li et al., 2023)
General LM | 2.7B | Phi-2 (Li et al., 2023)
Biomedical LM | 110M | Clinical-LongFormer (Li et al., 2022)
Biomedical LM | 2.7B | BioMedLM (Bolton et al., 2024a)
Table 9:  Example questions and options extracted from all datasets, with correct answers indicated in bold. Datasets |  Questions |  Answers
---|---|---
MedMCQA (Pal et al., 2022) |  Question: "Which drug is a selective COX 2 inhibitor?" |  A. Celecoxib B. Acetaminophen C. Ketorolac D. Aspirin
MedQA (Jin et al., 2021) |  Question: "A 68-year-old male comes to the physician for evaluation of right flank pain. He has a history of diabetes and peripheral artery disease. His blood pressure is 160/90 mm Hg. Physical examination shows abdominal tenderness and right flank tenderness. An ultrasound shows dilation of the right ureter and renal pelvis. Which of the following is the most likely underlying cause of this patient’s condition?" |  A.Renal artery stenosis B.Benign prostatic hyperplasia C.Diabetic nephropathy D.Common iliac artery aneurysm E.Urethral stricture
MMLU (Hendrycks et al., 2020) |  Question: "The process of translation requires the presence of:" |  A.mRNA, tRNA, and ribosomes B.mRNA, ribosomes, and RNA polymerase C.DNA, mRNA, and RNA polymerase D.chromatin, DNA, and amino acids.
PubMedQA (Jin et al., 2019) |  Question: "Is the combination with 2-methoxyestradiol able to reduce the dosages of chemotherapeutices in the treatment of human ovarian cancer?" Context: "The endogenous estradiol metabolite, 2-methoxyestradiol (2ME), has been shown to be a potent inhibitor of cell growth and a strong anti-angiogenic substance. … As a model two different human ovary cancer cell lines were used. All cell lines were incubated with equimolar concentrations of 2ME (0.8-25 microM) and the chemotherapeutics epirubicine, doxorubicine, paclitaxel, docetaxel, carboplatin, vinorelbine, 5-fluorouracil and mafosfamide. Proliferation was measured after four days using the ATP-chemosensitivity test. … For both ovary cancer cell lines a significant additive effect of 2ME with epirubicine and carboplatin was observed at the lower concentration range of these chemotherapeutic substances." |  yes, no, maybe
BioASQ (Tsatsaronis et al., 2015) |  Question: "Does BNP increase after intensive exercise in athletes?" Context:"NT-pro-BNP was significantly elevated postexercise in both adults and adolescents and remained above baseline at $24h$ in both groups. … The rise in BNP in older athletes may reflect a reversible, mainly diastolic left ventricular dysfunction. Plasma BNP concentrations were higher in both the judo and marathon groups than in controls, and positively correlated with LV mass as well as with deceleration time. Such exercise significantly increased ANP and BNP levels in healthy men, and the increases could be partially attributed to myocardial damage during the race." |  yes, no

##  Appendix C Parameter Studies of Azure-SFT

We conduct parameter studies on fine-tuning GPT-3.5-Turbo using the Microsoft
Azure fine-tuning API service, as detailed in Table 7. The training loss curve
is depicted in Figure 4. The Azure-SFT service offers only a very limited
number of adjustable hyperparameters, such as the learning rate multiplier
(LRM) and the number of epochs, which leads to a lack of transparency and
results in suboptimal fine-tuning performance (Table 3).

(a) MedMCQA (LRM=0.1, Epoch=3)

(b) MedMCQA (LRM=1, Epoch=3)

(c) MedMCQA (LRM=0.1, Epoch=5)

(d) MedQA (LRM=0.1, Epoch=3)

(e) MedQA (LRM=1, Epoch=3)

(f) MedQA (LRM=0.1, Epoch=5)

(g) MMLU (LRM=0.1, Epoch=3)

(h) MMLU (LRM=1, Epoch=3)

(i) MMLU (LRM=0.1, Epoch=5)

(j) PubMedQA (LRM=0.1, Epoch=3)

(k) PubMedQA (LRM=1, Epoch=3)

(l) PubMedQA (LRM=0.1, Epoch=5)

(m) BioASQ (LRM=0.1, Epoch=3)

(n) BioASQ (LRM=1, Epoch=3)

(o) BioASQ (LRM=0.1, Epoch=5)

Figure 4: Loss function curve of fine-tuning GPT-3.5-Turbo through Microsoft
Azure fine-tuning API service.

##  Appendix D Base Language Model Details for Scale-up Analysis

Table 8 describes details of the base model of MedAdapter in scale-up
analysis, ranging from 110M to 2.7B parameters.