# Clinical text generation: Are we there yet?

Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol

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Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol. Clinical text generation: Are we there yet?. Annual Review of Biomedical Data Science, 2025, 8, pp.173-198. 10.1146/annurev-biodatasci-103123-095202. hal-05055957

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Clinical Text Generation:
Are we there yet?

Nicolas Hiebel,<fnref n="1" /> Olivier Ferret,<fnref n="2" /> Karën Fort,<fnref n="3" /> and Aurélie Névéol<fnref n="4" />

XXXX. XXX. XXX. XXX. YYYY. AA:1-29

https://doi.org/10.1146/((please add
article doi))

Copyright © YYYY by the author(s).
All rights reserved

Keywords

natural language processing, large language models, controlled text generation, evaluation, domain adaptation

Abstract

Generative Artificial intelligence, operationalized as Large language Models, is increasingly used in the biomedical field to assist with a range of text processing tasks including text classification, information extraction or decision support. In this paper, we focus on the primary purpose of generative language models, namely the production of un-structured text. We review past and current methods used to generate text as well as methods for evaluating open text generation, i.e. in con-texts where no reference text is available for comparison. We discuss clinical applications that can benefit from high quality, ethically de-signed, text generation, such as clinical note generation and synthetic text generation in support of secondary use of health data. We also raise awareness to risks involved with generative AI such as overconfi-dence in outputs due to anthropomorphism and risk of representational and allocation harms due to biases.

<footnote n="1">¹Université Paris-Saclay, CNRS, LISN, F-91403, Orsay, France; email: nicolas.hiebel@universite-paris-saclay.fr</footnote>

<footnote n="2">²Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France; email: olivier.ferret@cea.fr</footnote>

<footnote n="3">³Université de Lorraine, CNRS, Inria, LORIA, F-54506, Vandœuvre-lès-Nancy, France; email: karen.fort@loria.fr</footnote>

<footnote n="4">⁴Université Paris-Saclay, CNRS, LISN, F-91403, Orsay, France; email: aurelie.neveol@cnrs.fr</footnote>
# 1. INTRODUCTION

Electronic Health Records (EHRs) contain a large proportion of textual unstructured data, which creates a bottleneck for automatic processing. Generative Artificial Intelligence (AI) is increasingly used in health applications because it offers the ability to process unstructured raw text and produce results expressed in fluent natural language. Generative AIs are built around Large Language Models (LLMs) trained on large text collections to predict the likelihood of the next word, given an input sequence of words (1). This ability to produce non-deterministic plausible word sequences can be leveraged to mimic dialogue, where a conversation turn is a likely follow-up to the beginning of a conversation. Language models can be further trained towards specific goals, such as text classification, using supervised training sets associating text samples with particular classes. In these applications, classification can be seen as an information extraction task, where information present in the input text is shortened or rephrased. It can also be cast as a predictive task where new information is inferred from the input text.

In a context where clinicians are vastly overburdened (2, 3, 4) and where interactions with EHR systems take a significant amount of their time (5, 6), tools that could alleviate clinicians’ workload would be welcome. Indeed, generative AI is increasingly used to assist with a range of text processing tasks, including text classification, information extraction, or decision support attempting to address crucial issues, such as fatal patient outcome prediction (7). However, while many AI applications are successful *in silico* on selected test sets, the results do not carry on well to extended testing in live clinical settings (8, 9). Text generation is not uniformly successful across languages and LLMs were shown to yield poorer performance in languages other than English, whether they are well-resourced (10) or not (11). Nonetheless, the use of dialogue systems producing fluent (English) language utterances as interfaces is triggering anthropomorphism in system descriptions (12) and leading users to place disproportionate trust in system outputs, a form of automation bias (13). In this review, we focus on the use of language models to generate unstructured data, in particular clinical text, intended for documenting patient trajectories or in support of secondary use of clinical data. We describe the range of algorithms used to generate text over the past few decades as well as evaluation methods. We review the current uses of generated text in the biomedical domain and comment on the limitations of these applications.

# 2. METHODS FOR TEXT GENERATION

In this section, we present a quick historical review of the evolution of text generation techniques and challenges. Figure 1 illustrates the evolution of text generation methods, starting with symbolic methods in the late 1960s, and highlights the increasing number of LLMs in recent years. Text generation methods have relied on a modular framework, which we describe in Section 2.1, for an extended period. Neural text generation methods are based on existing architectures that were initially intended for other tasks (image processing, machine translation). Recent methods have been developed at an increasingly fast pace, with major companies now releasing several models each year. Many LLMs were created focusing on different needs. Some focus on multilingual abilities (1), or on languages other than English (14). Others focus on specialized domains or tasks on which general language models still struggle, such as the medical domain (15, 16, 17, 18, 19).

In the remainder of the article, due to the current preeminence and success of neural
methods (20, 21), a significant part of the points we will develop will be related to these types of methods.

<figure><img src="image_2.png" /><figcaption>Figure 1: Timeline of the evolution of text generation methods and the recent expansion of LLMs.</figcaption></figure>

**Auto-regressive Language Model:**

A Deep Neural Network that recursively generates a probable next word based on an input sequence of words.

**Anthropomorphism:**

The attribution of human characteristics to non-human entities, such as language models.

**Bias in LLMs:**

A skewed and undesirable association in language representations which has the potential to cause harm.

**Prompt:** A sequence of words used as input to trigger further text generation (e.g. answering a question) from a language model.

## 2.1. The foundations of text generation

Text generation, in one form or another, has a history just as long as that of Natural Language Processing (NLP) as a whole, as evidenced by previous surveys (22, 23, 24, 20, 25). This history notably includes a wide range of work conducted in the medical field (26, 27), demonstrating that this domain is, more broadly, a favored application area of NLP. Until the late 1990s, text generation work followed a framework outlined in (28, 22), which defined both a set of tasks and an architecture for their implementation.

Within this framework, the general architecture of a text generation system is organized around three main components, which structure the various choices guiding the text generation task:

* a macro-planner, also known as a *text planner*, responsible for choosing *what to say*. This component takes as input the generation goals and produces the plan for the text to be generated, specifying both the content of the text and how this content is structured at the textual level;
* a *micro-planner*, also referred to as a *sentence planner*, whose role is to choose how to express the result of the text planner (*how to say it*). In practice, the output of this component defines how a piece of content from the text plan is turned into sentences and specifies an abstract representation of each of them;

* a *linguistic realizer*, which aims to build the linguistic form of a sentence from its specification produced by the sentence planner.

Beyond these three main components, the framework defined by (22) identifies a number of more specific tasks. In this perspective, the *linguistic realizer* component constitutes a task on its own. On the other hand, the *text planner* component is broken down into two tasks:

* *content determination*, which aims at selecting the information that will be expressed in the generated text;

* *text structuring*, which defines the order in which the selected information will appear in the generated text.

Finally, the *sentence planner* component encompasses the following three tasks:

* *sentence aggregation*, which determines how information is expressed in the generated text, whether broken down into one or several sentences;

* *lexicalization*, which involves choosing the words and expressions used to convey the information within a sentence;

* *referring expression generation*, which, while seemingly similar to lexicalization, is less concerned with expressing a concept than with referencing an entity as unambiguously as possible.

This overall view is, of course, schematic, as the division into three pipeline-organized
components and the assignment of the generation tasks to these components do not account
for more nuanced interactions that transcend these boundaries. Furthermore, although this
global model is historically linked to symbolic approaches, statistical and neural approaches
do not render it obsolete. The task of linguistic realization, for instance, admits multiple
variants, which can be either symbolic or data-driven. These variants may take the form of
manually defined templates, representing fixed expression patterns adaptable to the specific
content being conveyed through the use of variables. They may also take the form of spe-
cialized grammars, which allow for greater flexibility of expression compared to templates.
Both of these approaches, exemplified in (29) for the former and (30) for the latter, are
often referred to under the generic term of rule-based or symbolic approaches, as opposed
to statistical or neural approaches, which are rooted in efforts such as (31) or (32) in the
context of linguistic realization tasks.

From a more architectural perspective, the distinction between symbolic and statistical
or neural approaches is not entirely decisive, although it has a significant influence. (20)
identifies three types of architectures for text generation systems:

* modular architectures, with the pipeline architecture of (22) as a typical instantiation;

* planning-based architectures, which can be seen as a form of modular architecture where the relationships between modules are dynamically established;

* global or integrated architectures, which tend to blur the boundaries between tasks.

While the first two types are more commonly associated with symbolic approaches, even though these architectures do not determine the nature of the modules they integrate, the
last type is predominant among statistical and especially neural approaches, which favor end-to-end methods.

More globally, we can note that symbolic methods of text generation make a clear distinction between defining the content to be generated, through planning methods for instance, and the way it is expressed. Moreover, while these methods allow for fine control over the generated content, they struggle significantly with integrating the notion of textual cohesion, which adversely affects the naturalness of the generated texts and generally limits their use to the production of short or highly formatted texts. Statistical and neural methods, in the context of integrated architectures, present the opposite characteristics. They are difficult to control, especially in terms of specifying the content to be generated, but they produce text with good properties of textual cohesion. They also present a highly integrated nature and do not clearly separate the phases of content planning and verbalization.

## 2.2. Statistical methods

In this section, we focus specifically on statistical methods within the context of integrated architectures, particularly those based on language modeling. We set aside a substantial body of work that relies on data-driven approaches for implementing individual tasks in text generation.

**Generative language models.** Language modeling is an NLP task that consists in learning the statistical properties, structures, and patterns of textual data to make predictions. Causal language modeling tackles this by framing it as a next-word prediction task: the goal is to estimate the probability of a word occurring after a given sequence of words. More formally, the objective is to estimate $P(y_n|y_1, y_2, ..., y_{n-1})$, where $y_1, y_2, ..., y_{n-1}$ is the sequence of words and $y_n$ the following word. Models trained with this objective are called causal language models. In this study, we refer to causal language models as generative language models for simplicity, as they are by far the most popular types of models for text generation. Generative language models have to be trained on vast amounts of text data to learn the probability distribution of words. In other terms, a model can only learn the specific characteristics of a word if the training corpus contains sufficient examples of its usage. Therefore, the modeling abilities of generative language models are limited to the corpus – and languages – they were trained on.

Once trained, generative language models are able to generate text using a textual input called a *prompt*. The generation process is iterative: one next word is predicted and then concatenated to the prompt in order to predict the following word. The model stops generating text when an end-of-sequence token is generated or when the maximum sequence length is reached.

For a long period, statistical language modeling primarily relied on counting word n-grams to estimate the probability of a sequence of words, although some approaches based on decision trees (33) or maximum entropy (34) were also explored. Starting in the early 2000s, the work of Bengio et al. (35), by developing neural language models that replaced counting tasks with learning tasks, ushered in a new era leading to today's LLMs. In what follows, we detail these two major approaches.

### 2.2.1. N-gram language models

N-gram language models build the probability distribution of the corpus they are trained on using the global statistics of the corpus. The probabil-
**Token:** An elementary unit (e.g., a word or sub-word) that is used to segment text and build language models.

**Context:** A sequence of tokens considered by language models to compute the probability of a token at the current position.

**Word embedding:** A vector representation used to position words in a vector space.

**Attention:** A mechanism that associates a weight to tokens in order to base predictions on the most salient portion of the context.

**Fine tuning:** The process of continuing model training towards increased effectiveness for a specific task.

ity of a word occurring after a sequence directly corresponds to the number of times the word appeared after the given sequence in the corpus according to the maximum likelihood estimation principle.

N-gram language models are Markovian: the probability of a word appearing only depends on a fixed number of preceding words. More precisely, for a n-gram language model, this probability is estimated by looking only at *n* − 1 words in the past. Coupled with additional smoothing strategies, this allows the model to generate sequences that do not necessarily appear in the training corpus (36).

**2.2.2. Neural language models.** Progress in automatic text generation has accelerated with the popularization of neural networks, made possible by increased computing power. Neural approaches bring a new paradigm for building language models. Instead of counting words, they learn a vector representation of words. Neural language models project words into an embedding space with the idea that similar words should have a similar representation. To this end, representations are computed with the assumption that similar words appear in similar contexts (37). Learning accurate representations therefore requires large text corpora that include many different contexts. Working with representations instead of word counts gives neural language models increased generalization capabilities. This approach was mainly investigated after the seminal work of Bengio et al. (35), based on Feed Forward Neural Networks, with two main milestones: first, the development of static language models, mainly represented by the *word2vec* models (38), in which one representation is statically assigned to each word; then, the introduction of contextual language models, typically represented by BERT (39), in which the representation of a word depends on the other words that surround it.

Generative neural networks are trained for the next word prediction task, as described in Section 2.2. This means that those models can be trained in an auto-regressive way. First, the training text sequence is split into sub-units (tokens). Then, the first element of the sequence is used as input from which the model predicts the second token and the weights of the model are updated depending on the prediction. The actual second word of the sequence is concatenated to the previous input and the process is repeated until the end of the sequence.

We now describe the main two neural network architectures that were successively used for implementing neural language models after Bengio et al. (35).

**Recurrent Neural Networks** The architecture of Recurrent Neural Networks (RNN) (40) was the first to capture recurrence by keeping track of the previous context using a dedicated “hidden state”, a vector representation of the previous context. At each generation step (i.e. each generated token), the representation of the predicted token is added to the hidden state so that the new context representation also takes the predicted token into account. The new hidden state is then re-injected into the network when predicting the following token.

The hidden state is a fixed-sized vector. Thus, the amount of information it is able to contain is limited. The representation of recently generated tokens progressively mitigates the impact of the “older” tokens of the sequence as the model keeps generating text. This is a known issue of RNNs as they struggle to go beyond short-term context.

Two variants of RNNs were created trying to fix this issue: Long Short-Term Memory networks (41) (LSTM) and Gated Recurrent Units (42) (GRU). Both architectures use
additional neurons that identify and select important information in the context to reduce information saturation in the hidden state. This allows both LSTMs and GRUs to keep track of the context for an extended time compared to standard RNNs.

RNNs and their variants process text sequentially, which means that they need to pro-
cess the token at position $n_i$ before processing the token at position $n_{i+1}$. This constitutes a
bottleneck when training the models, especially given the substantial amount of data needed
to train them. Despite this limitation, the first widely adopted contextual language model,
ELMo (43), was based on a two-layer bi-LSTM architecture, but was quickly superseded
by the transformer architecture.

**Transformers** The transformer architecture (44) leverages the attention mechanism to solve this bottleneck. Attention enables the processing of all the tokens of a sequence simultaneously, each token taking into account both previous and following contexts. Thus, attention allows for a more efficient use of computing power to process large quantities of data, significantly outperforming recurrent neural networks in computational efficiency and scalability.

The transformer architecture is made of two parts: an encoder and a decoder. The encoder transforms the input into a vector representation, and the decoder recursively predicts a sequence of elements given the vector representation produced by the encoder.

The complete encoder-decoder architecture or the decoder alone can be used to generate text. Those two types of architectures deliver state-of-the-art performance for most generation tasks in NLP. Models with encoder-decoder architectures were initially preferred for sequence-to-sequence tasks, such as summarization or data to text, where an accurate vector representation of the input is important. However, decoder-only models are significantly less computationally intensive to train.

Most transformer-based language models are trained using pretext tasks, which are designed to facilitate learning useful data representations rather than being the model's ultimate application. To learn textual representations, encoder models like BERT were initially primarily trained on word unmasking tasks, where the objective is to restore a sequence by predicting one of its tokens hidden from the model. However the growing popularity of decoder-only architectures has resulted in increased usage of the next-word prediction task within the teacher forcing paradigm. Finally, the pretext tasks for training encoder-decoder models such as T5 (45) or BART (46) tend to be more diverse, including token masking, token deletion, span masking, or sentence permutation.

The scaling up properties of transformers led to an increase in pre-trained language models size and output fluency. For example, GPT-2 and later GPT-3 demonstrated the ability of large-scale language models to reach state-of-the-art results on various NLP tasks without any explicit supervision (47, 48). Subsequent research focused on making LLMs more controllable (49, 50), and the release of ChatGPT in 2022 gave the general public unprecedented access to generative language models.

**Encoder-decoder:** A model that converts text input into a set of context vectors that can, in turn, be used to output text.

**Decoder only:** A model that directly predicts the next word based on an input sequence.

**2.3. Sampling in the generation process**

No matter how large LLMs become, their underlying architectures, although optimized (in ways we might not know since proprietary models do not disclose much information), are still based on the transformer architecture. As a result, the generation process of transformer-based models remains fundamentally the same as any generative language models.
As mentioned earlier, the generation process consists of generating tokens one by one following the previously generated sequence or prompt (the generation of a single token is referred to as one generation step). This process can be divided into two parts. First, the model associates a score to all the tokens of its vocabulary. The scores represent the likelihood of the corresponding tokens to follow the prompt. This is the raw output of the model. Then, the next token to be generated is selected from the vocabulary based on the scores assigned to each token.

**Decoding** refers to the process of selecting tokens to generate. It is possible to select only the highest-scoring token (this is called “greedy decoding”), which makes the process deterministic. However, greedy decoding was found to produce monotonous and incoherent text and can lead to repetitive loops (51). Other decoding strategies were explored to address this problem, such as sampling strategies. Sampling amongst all the vocabulary is sub-optimal, since the very unlikely tokens should not be considered. Therefore, sampling strategies focus on selecting tokens only among the most probable options.

The most popular sampling strategies are *top-k* sampling and *nucleus* (also called *top-*p) sampling (51). Both strategies aim at getting rid of the tail of the distribution before sampling. *Top-*k only samples amongst the *k* most probable tokens while *top-*p samples amongst an extendable number of tokens so that the sum of their probabilities reaches the value *p*. While different, all sampling strategies will end up cutting the distribution tail arbitrarily, although the following tokens on the distribution might be acceptable or the previous tokens might be irrelevant.

In addition, the *temperature* parameter is generally used during generation. The *temperature* is used either to favor generation diversity by flattening the probability distribution (*temperature* > 1) or to increase the model confidence by accentuating the gaps in the probability distribution (*temperature* < 1).

**Illustration of the decoding process.** In Figure 2, we illustrate the effects of sampling and *temperature* on the probability distribution of the top 20 tokens for the prompt “If you have a fever, you should take some” with the GPT-2-large model.

In Figure 2a, we see the distribution when no *temperature* is applied. The red dotted-line represents the *top-*p threshold: only the tokens left to the line are considered during sampling. Modifying the *temperature* has two effects. First, it increases or flattens the distribution. The probability of the highest ranking tokens further increases at lower *temperatures* (Figure 2b), while the probabilities of all tokens get closer together with higher *temperatures* (Figure 2c). Also, the number of tokens considered during sampling changes since we use *top-*p sampling. The choice of the decoding parameters is typically optimized on a specific task or dataset, or for a specific goal (for example, favoring diversity). But it does not necessarily adapt to every possible situation. For example, the decoding parameters chosen in Figure 2a and 2b both get rid of the “ib” token, which produces “ibuprofen” with additional generation steps. The token is included in Figure 2c with a higher temperature, but increasing temperature also implies that more tokens are considered, among which undesirable ones might appear. The most relevant answer might not always be in the top position. Selecting amongst fewer tokens might get rid of relevant ones while selecting among more tokens might include irrelevant ones. Finding the right balance is not trivial, especially if we consider the potentially infinite number of situations the model may encounter.
<figure><img src="image_3.png" /><figcaption>(a) Temperature = 1 (unchanged distribution).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b) Temperature = 0.9.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(c) Temperature = 1.2.</figcaption></figure>

Figure 2: Output probabilities of top 20 tokens for the prompt: "If you have a fever, you should take some" with GPT2-large with top-p threshold with p=0.9 and different temperatures.

For this example, we use GPT-2-large, which has not been adapted to the medical domain but still proposes several tokens that seem to make sense. A model trained with additional medical content might get better results, but again, the internal process is the same for all transformer models. The model is very likely to consider or generate irrelevant answers at times, particularly when prompted on specific situations or specialized disciplines
that differ from its training data.

This may lead to potentially harmful generations, especially in a critical setting such as healthcare. In our example, the selection could include an output that is less efficient than the standard of care (e.g. "water" for fever treatment) or even fatal (e.g. "arsenic").

## 2.4. Controlling text generation in statistical models

Current text generation models have been shown to generate fluent text in some languages, hardly distinguishable from human-authored text (52). This addresses the *surface generation* or realization component of a generation system, but not the *content planning*. The control methods described in this section aim at guiding the generation process towards text that fits the desired output in terms of text genre (e.g., clinical case), target domain (e.g., oncology), task (e.g., summarizing input text), or even using specific terms or phrases (e.g. "history of smoking"). Depending on the level of control required (text-level features such as genre or targeted content such as terms), control can be achieved through continued training or by intervening at the inference stage (53).

### 2.4.1. Control through training.
At the training stage, generative models can be controlled in two ways: 1/ control by careful curation of a fine-tuning corpus used for continued unsupervised training of the model; and 2/ control through guidance, which involves adding a supervised layer of instruction to the language model.

**Class-conditional language models (CCLM) (54)** are trained on texts associated with control codes corresponding to predefined classes characterizing global narrative features such as text genre or polarity. Text from a specific class can be generated by prompting the model with the adequate control code. Control codes can also be combined to generate a text belonging to several classes - for example, generating a positive (polarity) movie review (genre).

**Instruction tuning** is a process used to enhance the capabilities of LLMs to follow instructions. This is achieved by fine-tuning the model on instruction datasets, where each example is a human written instruction (or prompt) paired with a corresponding response (55). With this additional training phase, the models reach better performance at following human instructions given as prompts. In fact, many of the models currently released come with a version pre-fine-tuned on an instruction dataset (for instance, Llama-3 (56) and Mixtral (57)).

**Fine-tuning strategies.** LLMs have a very large number of parameters. Entirely fine-tuning them requires expensive computational resources, takes time and space. Parameter-efficient fine-tuning methods (58, 59, 60) are designed to mitigate those issues by freezing the weights of the model and adding a small number of trainable parameters during fine-tuning, which are then used alongside the rest of the model during inference. This allows for a much more memory-efficient fine-tuning, thus facilitating the adaptation of generative models to other tasks, domains, or languages.

### 2.4.2. Control at inference.
Control can also be added at the inference stage, in order to shift token probabilities towards the generation of text containing desired terms or phrases

**Surface generation or realization: Part of the generation process that takes as input the content to generate and turn it into a linear textual form.**
without additional training.

***Cooperative generation*** involves one or more other models, called discriminators, which guide the generative model during the generation process. Discriminators may act on the hidden layers of the generative models (61) or more commonly on the output scores of the model. This is called *weighted decoding*. Discriminators can be any type of models, such as classifiers (62) or other generative models (63).

***Decoding strategies.*** In Section 2.3, we presented the decoding process of text generation of language models and popular sampling strategies. The generation process being iterative, token by token, the generation of a whole sequence corresponds to selecting a probabilistic tree path. Therefore, tree search algorithms such as *Monte Carlo Tree Search* and *A*\* search can be combined with discriminators in order to find the most relevant response while adapting to the available computing time (64, 65). Between purely greedy decoding and decoding based on these more sophisticated strategies, it is important to note the existence of generic tree-search strategies, the most widely used of which is undoubtedly beam search (66). This method explores the tree of possibilities provided by the language model by maintaining at each step the *k* most probable options.

***Prompting strategies.*** Since GPT-3 (48), large language models are evaluated for their ability to adapt to unseen tasks with only a few examples (few-shot) or even zero examples (zero-shot). Prompt optimization is being explored and several strategies were found efficient to improve performance. One common strategy is *chain-of-thought prompting* (67), where the model is explicitly prompted to decompose the task at hand into smaller steps leading to the final result. Another approach, known as *self-verification* (68), involves prompting the model to review output generated in a previous step for potential errors. *Self-verification* particularly highlights the unpredictability of LLMs, as they can generate inaccurate text while simultaneously being capable of spotting their own inaccuracies (69).

## 2.5. Limitations of generative language models

Despite reaching state-of-the-art performance on many NLP tasks, generative language models still present significant limitations. First, the vast amount of data needed for their training makes it difficult to control the information fed into the model (70). Moreover, this reinforces the already existing imbalance for languages with fewer resources available to train models. Second, the black-box nature of neural networks makes it challenging to interpret the reasons behind a model’s output. Third, the generation process is not deterministic since it relies on sampling amongst several tokens at each step. This implies that a prompt may (or even will) lead to different text generations without having modified any parameter. In this section, we present how these inherent limitations materialize in real-world scenarios.

***Biases.*** Five sources of bias have been identified to affect NLP, including text generation systems: the data, the annotation process, the input representation, the models, and the research design (71). Substantial amounts of data are crawled from various sources on the Web to meet the training data requirements of large language models. This implies that (i) reviewing every data source is a colossal task that is rarely done extensively and (ii) as
big as these corpora are, they only represent a subset of languages and speaker experiences. During training, models can be exposed to inaccurate information, misinformation, hate speech, and other undesirable content. Coupled with the nature of neural networks, which aim to condense information for generalization, underrepresented populations or viewpoints are further marginalized while stereotypes become emphasized (72). As a result, LLMs exhibit many types of biases, with gender bias (73, 74) and racial bias (75) being the most documented (76). Such biases already exist in medicine and LLMs amplifies them. Filters against biased and harmful outputs are implemented by the developers of the LLMs as additional layers, which are updated *after* harmful generations are identified, if at all.

**Inexact text generation.** Generative language models are not always accurate when generating text with a specific goal or task. This inaccuracy can take several forms, as the generated text may be inconsistent, irrelevant, lacking some key information, or containing factually incorrect statements. The term “hallucinations” was originally used in text-to-text and data-to-text tasks to indicate specific errors where the generated text included information that was not present in the input (77, 78). In this context, hallucinations could be either factually correct or incorrect but current work on hallucinations (79) tends to put the focus on the semantic and factual correctness of the generation. However, we believe the term is now being used abusively to designate many types of generation errors. Moreover, the term “hallucination” wrongly suggests that it represents an abnormal functioning of the model, despite there being no fundamental difference in the generation process. It also denotes anthropomorphism by borrowing a term originally used to qualify human sensory perception.

Complementary to inexact text generation, the training objective of LLMs drives the model towards generating a plausible answer to a prompt, regardless of its accuracy. The answer must *appear* plausible. If prompted in a language or a domain on which the model has received little training, the generated output is likely to be coarse, making it easily apparent as flawed by a native speaker or an expert, but not necessarily by a novice (80).

**Memorization.** Despite the generalization capabilities of neural networks, studies have demonstrated that generative language models memorize parts of their training data (81, 82, 83). The extensive crawling of training corpora on the internet may include sensitive or copyright-protected information. In addition to the copyright and privacy issues of training and distributing a model trained on such data, memorization can lead to the extraction of this information, whether intentional or unintentional. However, memorization is difficult to quantify due to the size of the training corpora and the fact that they are not always publicly disclosed.

**Prompt brittleness.** As we mentioned in Section 2.4, the output quality of generative language models can be improved using prompting strategies. However, the performance is sensitive to various parameters (how many examples, how to select them) including very specific and non-interpretable ones (formulation, vocabulary choices...) (77, 78). This sensitivity leads to unpredictable results, with prompt optimization often relying on empirical testing on specific models and/or datasets without being scientifically justified.
## 3. EVALUATING SYNTHETIC TEXT

Evaluation of text generation has been a challenge since the birth of the discipline due to the nearly infinite range of possible acceptable solutions. In this section, we present the different methods used for evaluation, highlighting their advantages and drawbacks.

### 3.1. Reference-based automatic metrics

For many text generation tasks, the goal is to generate a text from structured data or from an existing text (e.g., in the case of translation or summarization). It is therefore possible to have an idea of the type of response expected from the model, although considering every possible correct answer is impossible. Many evaluation metrics for text generation use a gold standard human-written reference to compare the candidate with. Several references can be provided for the evaluation to cover as many scenarios as possible.

**Text edition metrics** count the number of substitutions, insertions, and deletion required to convert the candidate into the reference. Such metrics can work at character level, such as the Levenshtein distance (84), and at word level, such as the *Word Error Rate* (WER) (85). For text generation, working at word-level is more common.

**Text overlap metrics** use string matching to measure the number of overlapping elements between the candidate and the reference. Text overlap metrics can be character-based (chrf (86, 87)), word-based (BLEU (88), ROUGE (89)), or n-gram-based (BLEU-2/3/4, ROUGE-L). Some also use stemming and synonyms for better robustness (METEOR (90)).

**Embedding metrics** were developed to overcome the lack of flexibility of the previous metrics by computing the embeddings of the words in the candidate and in the reference. Similarity is computed between the word embeddings of the candidate and those of the reference after word alignment. Static embedding (WMD (91)) and then contextual embeddings (BERTScore (92)) were explored.

**Score prediction models** address the evaluation of text generation as a classification task, trying to predict a rating given a candidate-reference pair. Instead of computing it, the metric is learnt. Those models need to be trained on a dataset of sentence pairs with human ratings. The most popular model of this type is BLEURT (93).

Reference-based metrics only assess the proximity between candidate generated text and one or several references without addressing the evaluation of the intrinsic quality of the generated text. Additionally, for some open text generation tasks gold standard references are not available for comparison with candidate generated text.

### 3.2. Reference-free automatic metrics

Reference-free automatic metrics focus on the intrinsic quality of the generated text. Different criteria can be used to determine the intrinsic quality of generated text such as grammaticality, fluency, consistency, diversity, and usefulness.

It is difficult to aggregate all these criteria as one single metric. A generated text might be perfectly fluent without being consistent, or useful without being fluent. In other words,
the intrinsic quality of generated text is multidimensional.

Consequently, a thoughtful evaluation of intrinsic text quality should aim to cover as many dimensions as possible, highlighting text strengths and weaknesses.

***Diversity metric.*** Self-BLEU (94) is a metric assessing diversity at the corpus level. Self-BLEU averages the BLEU scores between all the documents in a corpus. Since BLEU measures the overlap between two texts, Self-BLEU globally assesses how much textual documents in a corpus are similar to one another. The diversity measures by Self-BLEU is only lexical as BLEU works on string matching between words. A high Self-BLEU score suggests that the documents within the corpus are similar, thus indicating a lack of diversity in the corpus.

***Fluency.*** The perplexity metric evaluates how well textual data matches the probability distribution of a language model. The language model chosen to compute this metric should be considered high quality in the language and domain of the text. Perplexity is often defined by how *"surprised"* a language model is when looking at a specific text. The more surprised it is, the further away the text is from the known distribution of the language model.

***Usefulness.*** When text generation is task-oriented, the usefulness of the generated text is probably the most important quality criterion. The previous metrics do not reflect the potential impact of using the generated text. Automatically evaluating the usefulness of text is usually achieved by training NLP models on the generated text and comparing it to an NLP model trained on real data. This is also known as extrinsic evaluation (95). Using generated text on a task it was not explicitly evaluated on must be done with caution, as this generated text is not necessarily suitable for every task.

Automatic evaluation can provide valuable insights about text generation quality on a large scale. However, automatic metrics are not always sufficient to capture subtle nuances of intrinsic text quality.

## 3.3. Manual evaluation

Manual evaluation is a good practice for evaluating the quality of the generated text, as it can be more varied, creative, and targeted towards criteria that are not covered by automated metrics (96, 97). Nevertheless, manual evaluation also comes with a number of challenges.

Manually evaluating text quality is costly. The evaluated text might be long and spotting inconsistencies might require a decent amount of focus. Thomson et al. (98) reported that it took on average 20 to 30 minutes for an annotator to evaluate a 300-word text and cost about US$30.

Also, the evaluators' skills must be adapted to the task. It is at least necessary for the evaluator to speak the language in which the text is written in order to judge the quality of the text. The necessary skills might be more specific when dealing with specific domains with their own lexicon and writing conventions. The term “skill” is to be taken in its broadest sense. For instance, while LLMs are often praised for their drafting abilities, they still fall short in the comparison with human authors in creative writing, at least in
Spanish (10).

As we mentioned in 3.2, the intrinsic quality of a text can be assessed through different criteria. Those criteria are often judged on numerical or Likert scales (99). Generally, the more precise the criteria, the more costly the manual evaluation. On the other hand, general or vague criteria will facilitate evaluation but reduce reproducibility and inter-annotator agreement (95). A compromise has therefore to be found between difficulty and accuracy of manual evaluation. Terms such as “fluency” and “consistency” naturally appear as appropriate evaluation criteria. However, there is no standardized terminology for the quality criteria in text generation. Many popular terms are defined in different ways and overlap with one another (100). This is also an obstacle to reproducibility.

The manual evaluation of text quality using specific quality criteria is costly and hard to reproduce. For this reason, it is also difficult to compare the results of a system to those of previous systems and claim improvement. Manual evaluation using preferential judgments seeks to avoid these disadvantages. This type of evaluation involves presenting several texts simultaneously to the evaluators. Evaluators may select the text they consider to be the best or rank them from worst to best. This approach facilitates evaluation and gets better inter-annotator agreement (101).

However, evaluation by preference can be influenced by several factors, such as the order in which the examples are presented, and may favor one system or give the impression that the difference between two systems is greater than it really is (102).

Finally, several meta-studies analyzed text generation papers with manual evaluation (99, 100) and noticed that the documentation of the manual evaluation setup is often incomplete. This includes the characteristics of the annotators (number of annotators, inter-annotator agreement), the definition of criteria, statistical significance, and other evaluation modalities (such as the evaluation interface).

The difficulties brought by manual evaluation also make the creation of new metrics difficult, as observing the correlation of a metric with human judgments logically depends on the human judgments used. The metrics themselves are therefore difficult to evaluate. Well-established metrics have been re-evaluated several times on recent datasets and they present poor correlation with human judgments (103, 104, 105).

## 3.4. Evaluating automatically generated biomedical text

Working in a specialized domain such as healthcare involves dealing with specific writing styles and specialized vocabularies. Standard automatic metrics already showed some limitations on non-specialized texts, and they do not correlate well with human judgments in healthcare either (106).

Several reference-based automatic metrics were created or adapted to account for the characteristics of the medical domain. External knowledge sources such as the Unified Medical Language System (UMLS) (107) are typically used to focus the similarity on medical concepts. These medical concepts can be extracted in the candidate and reference to be compared (MEDCON (108)). Medical knowledge graphs can also be used, either to add weights to the medical terms in existing metrics (Clinical BERTScore (109)) or to compute the similarity between the knowledge graph embeddings of the medical concepts (MIST (110)). Ben Abacha et al. (110) fine-tuned a BLEURT model on medical texts with error scores to obtain a Clinical BLEURT model.

As for standard automatic metrics, the validity of specialized metrics can be assessed
by computing their correlation with human judgments on biomedical texts. Moramarco et al. (106) and Ben Abacha et al. (111) computed the correlation between standard metrics and human ratings on clinical note generation tasks. Moramarco et al. (106) found that the metric with the highest overall correlation was the Levenshtein distance, a character-editing distance metric not originally designed for evaluating text generation, while Ben Abacha et al. (111) found the best correlation with BLEURT. In both studies, however, the authors analyzed the correlation among various human judgment criteria, and none of the metrics demonstrated the highest correlation with all of them. Metrics specialized for the medical domain were tested on several clinical note generation datasets (110). Although the specialized metrics showed a better overall correlation with human judgments, the results varied significantly across the datasets.

Those studies highlight the challenges of evaluating text generation for biomedical texts. Standard metrics are not sufficient, and even the validity of specialized metrics must be assessed across various datasets, tasks, and even languages. Additionally, correlation with human ratings can help bring out a ranking among metrics but needs to be broken down to precise criteria to really estimate the errors that the metrics are able to capture. As with standard text, multiple metrics must be included to cover as many criteria as possible.

For manual evaluation, the challenges presented in Section 3.3 persist. The criteria selected for evaluating generation quality must be adapted to the applications of the generation system and the evaluators must be experts in the specific domain to accurately assess the quality of the generation. Manual evaluation is time-consuming and clinicians may have limited availability. Designing the evaluation interface to be ergonomic and engaging may help increase involvement. For example, Hiebel et al. (112) developed a Game With A Purpose (GWAP) to carry out manual evaluations of French synthetic clinical texts.

In addition to intrinsic text quality, the manual evaluation of text generation systems should also take into account their performance when deployed in realistic settings to accurately assess the potential impacts of their deployment (113).

# 4. TEXT GENERATION IN HEALTHCARE

Text generation for healthcare goes beyond EHRs to cover social media content (114) and dialogue/counseling systems. While some systems are being deployed, psychologists reflect on the impact these systems can have on patient health (115). There is a fine line between generating text and practicing healthcare. We believe that applications of text generation in healthcare should be designed to assist with the creation of texts geared towards the documentation of healthcare processes or the support of secondary use of healthcare data, rather than as substitutes for clinical decisions or care.

## 4.1. Applications

**Automatic generation of documents for the EHR.** Clinicians spend a significant amount of time writing reports that document patients' medical history at each step of the care process. Assisted writing is perceived as a potential time gain to ease clinicians' workload. Several efforts have investigated methods to produce discharge summaries from existing clinical notes in English (116) and other languages such as Italian (117) and Swedish (118). Direct patient/doctor interactions are also considered as input for summarization (119, 111), which can be based on real-time transcriptions (120). Medical images,
which are instrumental to medical diagnosis, are also used as input for the generation of reports describing the image reading and interpretation. These multimodal studies primarily address English (121, 116, 122) due to the lack of specific resources in other languages.

However, it is important to develop a system that allows clinicians to easily verify the model outputs for potential inaccuracies. A significant risk in this scenario is that clinicians may develop an over-reliance on the system, potentially overlooking inaccuracies that could affect subsequent patient treatments (123).

***Dialogue systems.*** The rise of telemedicine led to the development of online platforms that allow clinicians to provide care to patients remotely. Telemedicine improves access to healthcare, as patients are not required to travel to a doctor's office for treatment, alleviating issues such as distance and mobility. Dialogue systems in healthcare (124) are gaining attention for their possible various applications in this context, where they can help patients access to healthcare more easily while allowing clinicians, among other possibilities, to monitor their patients more closely and reduce their workload by employing tools such as symptom checkers for implementing triage functionalities. Most experiments on this task address English or Chinese (125, 126, 127, 128), but recent efforts aim to create resources in other languages such as French (129).

Direct interaction between patients and generative models in an open framework such as a dialogue system poses numerous risks, including the need for securing the confidentiality of health information shared in the conversations. Patients may interact with the system freely while often lacking the expertise to identify harmful and inaccurate system outputs, exposing themselves to all the limitations presented in Section 2.5. Automatic systems should assist clinicians rather than replace them. We argue that this application approaches the latter and should be handled with extreme caution.

***Text simplification.*** Several studies showed the importance of transparency and the inclusion of patients in their care process (130, 131). Text simplification contributes to improving patients understanding of their medical condition and care. Symbolic systems rely on the retrieval of synonyms for difficult terms to ease understanding (132, 133) including in languages other than English (134).

Text simplification may involve direct interaction with patients, presenting similar risks in such cases. However, the interaction framework is narrower than that of a dialogue system, as it focuses on specific elements to simplify. In this context, interactions must be restricted to prevent open interactions with the system and to mitigate the risks of exposing patients to harmful and inaccurate content.

***Data augmentation and data sharing.*** In sensitive domains such as healthcare, data sharing is bound by confidentiality and there is only a small amount of freely available corpora for healthcare, especially in languages other than English (135). Text generation represents an opportunity for creating new textual resources that NLP models can be trained or tested on. The generated texts can either be complementary to already existing data (136) or in substitution of real data, with a focus on patient privacy (137, 138, 139, 140). Even for data sharing, many studies utilize the English corpus MIMIC-III (141) as a testing ground
as it is the biggest medical corpus available. Experiments on other languages exist but on smaller corpora (142, 143).

Data augmentation and data sharing are indirect applications of text generation in health-
care, as they do not directly involve clinicians or patients, and therefore the risks are dif-
ferent. Data sharing could lead to the leak of sensitive information about patients (81, 83),
and the evaluation methodologies used to assess those risks are far from perfect<fnref n="1" />. Moreover,
the utility of synthetic data is often evaluated on a limited set of downstream tasks and its
use on other tasks could potentially be harmful.

## 4.2. Text generation systems need better evaluation before and during deployment

Recent advances in the field of artificial intelligence have generated great enthusiasm among both the general public and the research community, particularly with ChatGPT and other LLMs.

LLMs are indeed powerful tools for text generation and hold significant potential for various applications. However, we believe that there is sometimes an overemphasis on what LLMs can do on any given topic, while neglecting their limitations. Premature deployment can reduce trust in the models and over-hyping the technology increase caution (144, 145), therefore risking the potential benefits it may offer.

LLMs are prone to anthropomorphism because of their ability to produce human-like text. We argue that this anthropomorphism reduces users' critical assessment of the responses and leads to the belief that LLMs can perform any task. However, LLM users should be knowledgeable on the prompted topic and critical of the models' output, as when and how their limitations manifest is not always obvious. For healthcare, any task involving direct interaction between patients and LLMs should be approached with extreme caution, if not entirely avoided, as patients often lack the medical knowledge required to critically assess the models' output.

Assessing when a generative language model is ready for deployment is a complex issue, given the challenges of both automatic and manual evaluation on the one hand, and the known limitations of these models on the other. Models that perform well on a given task, domain, or specialty do not necessarily perform as well outside of their experimental settings. Previously deployed predictive systems in healthcare have been found to have decreasing performances over time (146) or when deployed outside of their original setting (147), and generative systems may display the same behaviors. This variability is also true for evaluation metrics, which can have varying performances even within a narrow scope such as clinical note generation (110).

Besides, it is crucial to separate the quality of the textual outputs produced by a gen-
eration system and the actual impact of deploying the generation system in a real-world

<footnote n="1">¹These methodologies typically involve repeatedly querying the model with specific prompts and verifying whether the generated output is present in the training data. Thus, access to the training data is required, and its size may render the verification process costly. Furthermore, closed models may incorporate additional protections as they are updated, making the results obsolete. For instance, Nasr et al. (83) found that when ChatGPT was prompted to repeatedly generate a single word indefinitely, it ended up generating large sections of its training data. This issue was corrected in a later version of the model.</footnote>
setting. High scores on output quality metrics do not necessarily imply that deploying the
system will benefit users. In addition to convenience of use and time efficiency, expectations
of a good system may vary depending on the task, specialty, hospital, and even individual
clinicians (106, 3). Automatically generated drafts requiring post-editing do not necessar-
ily save clinicians time (148) and may require a higher cognitive load than writing from
scratch (149).

Auditing models in real-world settings is challenging and costly, yet absolutely essential for ensuring safe deployment both in the short and long term (113, 150, 8). Even a system that is efficient and useful at a given time must be regularly audited and updated to ensure its performance remains optimal (146). Audits and evaluations must be tailored to the specific needs of clinicians, who should be involved at every step of the process, as these needs may vary from one clinician to another (106, 3).

5. CONCLUSION

In this survey, we presented the operating principle of generative models and their lim-
itations. Additionally, we highlighted the challenges of evaluating text generation, both
broadly and specifically within the context of healthcare, whether through automatic or
manual methods.

Using generative models for any task, domain, or language should account for these lim-
itations to ensure safe usage. This involves focusing on use cases where users can critically
assess the quality of the model's output. In healthcare, this means avoiding applications
that involve direct interactions with patients.

The most promising applications of generative models in healthcare are assisting clinicians with the creation of text for documenting care in the EHR and in support of secondary use of clinical data. The systems should be developed in close collaboration with clinicians to ensure that they are designed to meet the individual needs of each clinician and that the verification process is as straightforward as possible. A system that is tedious to use may either discourage clinicians from using the system, or encourage them to skip the verification process and over-rely on the system.

ACKNOWLEDGEMENTS

This work is supported by funding from the French "Agence Nationale pour la Recherche" under grant agreement CODEINE ANR-20-CE23-0026-01.

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