Feedback Forensics: A Toolkit to Measure AI Personality

## Abstract

Abstract Some traits making a “good” AI model are hard to describe upfront. For example, should responses be more polite or more casual ? Such traits are sometimes summarized as model character or personality .
Without a clear objective, conventional benchmarks based on automatic validation struggle to measure such traits.
Evaluation methods using human feedback such as Chatbot Arena have emerged as a popular alternative.
These methods infer “better” personality and other desirable traits implicitly by ranking multiple model responses relative to each other.
Recent issues with model releases highlight limitations of these existing opaque evaluation approaches: a major model was rolled back over sycophantic personality issues, models were observed overfitting to such feedback-based leaderboards.
Despite these known issues, limited public tooling exists to explicitly evaluate model personality. We introduce Feedback Forensics : an open-source toolkit to track AI personality changes, both those encouraged by human (or AI) feedback , and those exhibited across AI models trained and evaluated on such feedback. Leveraging AI annotators, our toolkit enables investigating personality via Python API and browser app. We demonstrate the toolkit’s usefulness in two steps: (A) first we analyse the personality traits encouraged in popular human feedback datasets including Chatbot Arena , MultiPref and PRISM ; and (B) then use our toolkit to analyse how much popular models exhibit such traits.
We release (1) our Feedback Forensics toolkit alongside (2) a web app tracking AI personality in popular models and feedback datasets as well as (3) the underlying annotation data . 1 1 1 Code: github.com/rdnfn/feedback-forensics , Web app: app.feedbackforensics.com , Data: hf.co/datasets/rdnfn/ff-annotations

## 1 Introduction

Conventional benchmarks for evaluating large language models, such as MMLU [Hendrycks et al., 2021], do not capture many aspects of AI model behavior.
Beyond factual correctness and coding capabilities, traits such as *tone* or *style* also matter to users – but are more challenging to evaluate.
As illustrated in [Figure˜2](https://arxiv.org/html/2509.26305v1#S0.F2), not just the *content* but also the *manner* of responses is important for the user experience [Lambert, 2025]. Such behaviour traits relating to the manner of responses are sometimes collectively referred to as model *character* or *personality*. In this work, we take a closer look at *model personality* in this general sense, using the term *personality trait* to refer to any characteristic of a model’s responses that (1) distinguishes that model’s from other models’ responses and (2) is not commonly considered a model capability.(^2^22For example, we consider *writing style* as a personality trait but not *coding capabilities*. See [Section 4](https://arxiv.org/html/2509.26305v1#S4) for a discussion of how our definition relates to others in the literature.)

Due to the ambiguous nature of style and manner, *“good”* model personality is difficult to define explicitly. Conventional benchmarks based on multiple choice or other forms of automated validation cannot be applied directly. Evaluation methods based on feedback datasets, such as Chatbot Arena [Chiang et al., 2024], have emerged as a popular alternative. methods are able to capture subtle behaviour improvements, including in terms of personality – without needing to explicitly define what a *“good”* personality is. Instead, *“better”* personality is implicitly defined by ranking multiple model responses relative to each other. Given the implicit setup, our understanding of the concrete *personality changes* encouraged by such feedback datasets and *personality differences* between models is typically limited.

Recent issues with the personality of frontier models further highlight the limits of current evaluation methods. OpenAI recently rolled back a version of GPT-4o used in the ChatGPT interface over concerns of an *overly sycophantic* personality – excessively flattering and agreeing with users [OpenAI, 2025]. Concerns were also raised around the verbose and emoji-heavy personality of an experimental version of Llama-4-Maverick on Chatbot Arena [Wiggers, 2025]. These observations highlight the need for more robust tooling to measure personality traits – better tooling could make such drifts in personality more visible and help create models with more desirable traits.

Contributions.
We introduce *Feedback Forensics*, a Python toolkit to measure personality traits, and release a corresponding web app and annotation data:

- 1.
Open-source *Feedback Forensics* Python toolkit for measuring AI personality traits. Building on *Inverse Constitutional AI* (ICAI) by Findeis et al. [2025], we implement a comprehensive Python toolkit to measure personality traits *exhibited by models* and *encouraged by pairwise feedback data*. Our toolkit can be used to detect personality traits locally, either via Python API or in an interactive Gradio app.
- 2.
Web platform tracking personality in popular models and feedback datasets. In addition to the Python toolkit for local usage, we also provide a web platform to inspect personality traits observed in popular models and datasets, available at [app.feedbackforensics.com](https://app.feedbackforensics.com/).
- 3.
Annotation data from experiments. Accompanying our experimental results, we release the underlying AI-annotator-generated personality annotations publicly to enable further analysis, available at [hf.co/datasets/rdnfn/ff-annotations](https://hf.co/datasets/rdnfn/ff-annotations). See [Section˜C.2](https://arxiv.org/html/2509.26305v1#A3.SS2) for further details.

Figure: Figure 3: Illustration of Feedback Forensics’ method to measure personality traits. We take pairwise model response data as input, where each datapoint consists of a *prompt* (yellow) and two corresponding *model responses* (white). Optionally, additional metadata may be included (e.g. generating model for each response). In Step 1, we add *annotations* to each datapoint selecting *response A*, *response B*, *both* or *neither* responses. To understand personality traits encouraged by human preferences, we include a (1) *human annotation* (green) selecting the human-preferred response. Such annotations can be imported from external sources (e.g. Chatbot Arena) alongside the pairwise model response data. To understand the personality traits exhibited by a *target model* (e.g. a Claude model), we add a (2) *target model annotation* (red) using hard-coded rules on response metadata to select the response generated by the model (if available). Finally, using AI annotators, we add (3) *personality annotations* (blue) that select the response that exhibits a trait more (e.g. that is more confident). We collect one such annotation per datapoint and tested trait. In Step 2, we compare these annotations to compute personality metrics. To understand how much a specific personality trait is encouraged by human feedback (Result A), we compare *human annotations* (green) to *personality annotations* (blue) for that trait. High agreement (measured via *strength* metric, see [Section˜2](https://arxiv.org/html/2509.26305v1#S2)), indicates that the trait (or a highly correlated trait) is *encouraged* by human feedback. Low agreement indicates that the trait is *discouraged*. Similarly, to observe how much a target model exhibits a certain trait (Result B), we compare *target model annotations* (red) to that trait’s *personality annotations* (blue). High agreement indicates that the trait uniquely identifies the model (relative to other models in dataset), i.e. the *model exhibits the trait more than other models*. Low agreement indicates the model exhibits the trait *less than other models*.
Refer to caption: figures/03_method_illustration_v10.png

## 2 Method

[Figure˜3](https://arxiv.org/html/2509.26305v1#S1.F3) provides an introduction to Feedback Forensics’ approach for measuring personality traits. In the following, we provide further details on the individual steps and metrics.

Input: Pairwise Model Responses.
Our method uses *pairwise model response data* as input. Each datapoint of such a dataset consists of a *prompt* $p$, and two *model responses* $r_{A}$ and $r_{B}$, typically generated by different models. Optionally, additional metadata may be included (e.g. generating model for each response). This pairwise input data was either originally constructed to collect human feedback (e.g. in Chatbot Arena) or specifically set up to measure model personality traits.

Step 1: Annotate Data.
Given such pairwise model responses data, we add *annotations* to each datapoint. The pairwise format enables *relative* annotation of model responses: rather than evaluating model responses individually in *absolute* terms, we can annotate each pair’s responses relative to each other. The relative annotations used in Feedback Forensics either select *response A*, *response B*, *both* or *neither* responses.(^3^33Many variations exist on this basic recipe. Sometimes more annotation choices are included to add information about the *strength* or *confidence* of response selection (e.g. Miranda et al. [2025]) or to distinguish between ties where both responses equally well (*“tie-bothgood”*) or badly (*“tie-bothbad”*) satisfy the selection criterion (e.g. Chiang et al. [2024]). Further, in some datasets annotators rank more than two responses at the same time (e.g. Kirk et al. [2024]). Finally, whilst we only consider text-based, the pairwise preference setting has also been applied to other modalities such as images (e.g. Chou et al. [2025]). Many of these variations can be transferred to the basic form discussed above. For Feedback Forensics, we focus on processing pairwise preferences in this more basic form to enable direct comparison across many datasets.) If the annotation process fails, we set the annotation value to *invalid*.
In many cases, especially when annotating personality traits, creating such *relative* annotations is easier than *absolute* annotations. For example, it may be simpler to annotate the *relatively* friendlier response in each pair than come up with an *absolute* friendliness score consistent across responses.

For our personality analysis, we add the following annotations to the input data:

- 1.
Human annotations (green in [Figure˜3](https://arxiv.org/html/2509.26305v1#S1.F3)). To identify the personality traits encouraged by human annotators, we add *human annotations* indicating the response preferred by humans (if available). We support loading such annotations alongside the pairwise model response input, for example when using Chatbot Arena data [Chiang et al., 2024].
- 2.
Target model annotations (red). To enable the analysis of the personality of a specific *target model*, we add annotations that always select that model’s response. These annotations are added by our toolkit using hard-coded rules based on the response metadata to determine if one, both or neither of the responses are from the target model.
- 3.
Personality annotations (blue). Finally, we use *AI annotators* (also referred to as *LLM-as-a-Judge*, Zheng et al. [2023]) to annotate which response exhibits a certain personality trait more. We collect one such annotation per personality trait (e.g. selecting the *more confident* response). For efficiency, our toolkit supports AI annotators that annotate multiple traits simultaneously (e.g. in a single forward-pass the annotator would return two annotations, the more confident *and* the friendlier response). To ensure high-quality annotations, our toolkit supports *cross-annotation*: collecting multiple annotations with different prompts for the same datapoint. Such cross-annotations are then combined via uniform or majority voting.

Step 2: Compute Metrics.
In the next step, we compute metrics based on these annotations. To quantify personality by comparing *personality* annotations to *human* or *target model* annotations, our toolkit supports computing the following main metrics (in Step 2 of [Figure˜3](https://arxiv.org/html/2509.26305v1#S1.F3)):

- 1.
Relevance. We define the *relevance* of one set of annotations over a given set of datapoints as $\texttt{relevance}=n_{\text{valid}}/n_{\text{total}}$,
where $n_{\text{valid}}$ is the number of datapoints with valid votes selecting one response over the other (*response A* or *response B*). This number excludes *tie* (*both*/*neither*) and *invalid* votes.
- 2.
Cohen’s kappa. Cohen’s kappa ($\kappa$) [Cohen, 1960] is a metric of inter-annotator agreement between two sets of annotations that measures agreement *beyond random chance*. It is defined as
$\kappa=\frac{p_{o}-p_{e}}{1-p_{e}},$
(1)
where $p_{o}$ is the observed proportion of datapoints where annotators agree, and $p_{e}$ is the proportion of datapoints for which agreement is expected by chance. $p_{e}$ can be estimated using the observed distribution of labels, as in $p_{e}=(n_{a_{1}=A}n_{a_{2}=A})/N^{2}+(n_{a_{1}=B}n_{a_{2}=B})/N^{2}$, where $n_{a_{i}=X}$ is the number of times annotator $i$ was observed voting for response in position $X$ and $N$ is the total number of observations. We use the efficient Scikit-learn [Pedregosa et al., 2011] implementation of Cohen’s kappa inside Feedback Forensics. For the computation of this metric, we only consider *valid* votes excluding *tie* (*both*/*neither*) and *invalid* votes.(^4^44When one of the annotators does not have access to the order of responses (e.g. because they are always shuffled) the expected chance agreement $p_{e}$ is $0.5$ by design, even if the other annotator is highly biased to one position (e.g. first response). We thus also include a version of Cohen’s kappa under this assumption, that one annotator has randomized order, setting $p_{e}$ to $0.5$. Given the known built-in randomization in our personality-selecting annotators, our implementation uses this kappa version for the strength metric computation.)
- 3.
Strength. Finally, for our specific use-case, we combine *Cohen’s kappa* with *relevance* to obtain a measure of *relevant agreement beyond chance*. We refer to this metric as *strength*, defined as
$\texttt{strength}=\kappa\times\texttt{relevance}.$
(2)
By weighting with relevance, we emphasize agreement that is widely applicable across the dataset. In our setting, this metric indicates whether a personality trait is widely relevant *and* highly correlated with the target annotations. The strength metric has some desirable properties: (a) range is limited from $-1$ to $1$, (b) magnitude above 0 indicates some relevance, (c) values above 0 indicate agreement beyond chance, (d) values below 0 indicate disagreement beyond chance, and (e) a zero value indicates no agreement or relevance, or both. Intuitively, zero value agreement and relevance similarly indicate that a personality trait is not informative about the target annotations. [Figure˜4](https://arxiv.org/html/2509.26305v1#S2.F4) further illustrates the interpretation of the strength metric.

Beyond these core metrics, our framework supports computing further metrics, see [Appendix˜B](https://arxiv.org/html/2509.26305v1#A2).

Figure: Figure 4: Interpretation of *strength* metric in both use-cases. At the top, interpretation of *strength* metric when comparing *human feedback* and *personality trait* annotations of a specific trait (Result A). At the bottom, interpretation of *strength* metric when comparing *target model* and *personality trait* annotations of a specific trait (Result B). Colour here indicates the *sign* and *magnitude* of the strength metric rather than annotation type.
Refer to caption: figures/05_strength_metric_interpretation_v4.png

Using and interpreting metrics. [Figure˜4](https://arxiv.org/html/2509.26305v1#S2.F4) illustrates the interpretation of the strength metric depending on the use-case. To understand how much a personality trait is encouraged by human preferences, we compare *human* (green in [Figure˜3](https://arxiv.org/html/2509.26305v1#S1.F3)) and that trait’s *personality* (blue) annotations (Result A). To understand whether a personality trait is exhibited by a model (Result B), we compare *target model* (red) annotations and that trait’s *personality* (blue) annotations.

### 2.1 Tested personality traits

Feedback Forensics can be used to evaluate a wide range of model traits. We provide two ways to choose the traits to be tested: either using our *manually curated personality trait set* or using *Inverse Constitutional AI* (ICAI) [Findeis et al., 2025] to automatically generate potential differentiating traits. Our experiments here focus on the manually curated personality traits to make them comparable across models and datasets, but users may use either approach to test different traits.

Manually curated traits. To construct the manually curated list, we collected instructions that select for known AI personality traits and can be given to an objective-following AI annotator. We refer to this list as PersonalitySelectionPrompts-v1 and make it publicly available in our repo. We identify personality traits based on three sources: (1) we consider the literature discussing model idiosyncrasies and annotation biases [Li et al., 2024a, Chen et al., 2025], (2) online discussions on how different models’ personalities differ,(^5^55See [Appendix D](https://arxiv.org/html/2509.26305v1#A4).) and finally (3) automatically identified objectives in human feedback datasets and differences between models within such datasets, discovered using the ICAI and VibeCheck [Dunlap et al., 2025] approaches. [Section˜H.1.1](https://arxiv.org/html/2509.26305v1#A8.SS1.SSS1) provides further details.

## 3 Experimental results

We demonstrate the use of our *Feedback Forensics* toolkit in two steps. First, in [Section˜3.1](https://arxiv.org/html/2509.26305v1#S3.SS1), we use the toolkit to measure the most and least encouraged personality traits in popular human feedback datasets. Then, in [Section˜3.2](https://arxiv.org/html/2509.26305v1#S3.SS2), we use our toolkit to investigate personality traits observable in popular models. In this section, we highlight notable observations for each experimental setting. We provide additional comprehensive results for each setting in [Appendix˜E](https://arxiv.org/html/2509.26305v1#A5), including a trait agreement correlation analysis ([Section˜E.1](https://arxiv.org/html/2509.26305v1#A5.SS1)) and comparison of AI to human personality trait annotations ([Section˜E.2](https://arxiv.org/html/2509.26305v1#A5.SS2)). Based on the latter results, we use Gemini-2.5-Flash with single-vote for all AI personality annotations in the following experiments. Finally, we include full dataset details including links and licenses in [Appendix˜C](https://arxiv.org/html/2509.26305v1#A3).

### 3.1 AI personality changes encouraged by human feedback

In our first set of experiments, we illustrate Feedback Forensics’ use to investigate AI personality traits encouraged in popular human feedback datasets: crowd-sourced *Chatbot Arena* data [Chiang et al., 2024], cross-annotated *MultiPref* data [Miranda et al., 2025] and demographically diverse *PRISM* data [Kirk et al., 2024].

#### 3.1.1 Chatbot Arena: Tracking requested personalities across domains

Figure: Figure 5: Most encouraged (blue) and discouraged (red) personality traits in Chatbot Arena. We observe a strong emphasis on encouraging *better structured*, *more verbose* and *more confident* responses. On the other hand, *more concise* or *avoidant* responses are discouraged. All measurements using *strength* metric.

Chatbot Arena [Chiang et al., 2024] is a popular public leaderboard based on human feedback, using crowd-sourced annotations. We use a subsample of 10k out of 100k conversations from a dataset(^6^66Source: [https://hf.co/datasets/lmarena-ai/arena-human-preference-100k](https://hf.co/datasets/lmarena-ai/arena-human-preference-100k)) released alongside the *Arena Explorer* topic modelling pipeline by Tang et al. [2025], collected from June to August 2024 and limited to conversations in English. Further, we automatically add topic labels to each conversation in the dataset using the Arena Explorer pipeline.

Results. [Figures˜5](https://arxiv.org/html/2509.26305v1#S3.F5) and [6](https://arxiv.org/html/2509.26305v1#S3.F6) show investigating the Chatbot Arena data with our toolkit. In [Figure˜5](https://arxiv.org/html/2509.26305v1#S3.F5), we observe that responses that are *well formatted*, *verbose* but also *factually correct* and *confident* are encouraged. When considering human feedback across subsets focused on different writing tasks ([Figure˜6](https://arxiv.org/html/2509.26305v1#S3.F6)), we observe notable differences in encouraged traits depending on the domain.
We further validate these trait-based annotations in [Section˜E.1](https://arxiv.org/html/2509.26305v1#A5.SS1), which confirms intuitive correlations such as conciseness opposing verbosity.

Figure: Figure 6: Encouraged (blue) and discouraged (red) personality traits across three writing tasks on Chatbot Arena. We show the top 5 with largest max difference across rows. We observe differences across these tasks, such as *verbosity* and *structure* being more valued for *resume* and *songwriting* than for *email writing*, where *conciseness* is valued. All measurements using *strength* metric.

#### 3.1.2 MultiPref: Tracking differences across human and AI annotations

Figure: Figure 7: Encouraged (blue) and discouraged (red) personality changes across different human and AI annotators on MultiPref. Sorted by max difference across rows (top 5). We observe similar traits being encouraged and discouraged across annotator types but with *varying strength*. Expert human annotations encourage the same personality traits less strongly than non-expert human annotations. Similarly, all human annotations encourage the same traits less strongly than AI annotators. All measurements using *strength* metric.

Next, we illustrate Feedback Forensics’ use to analyse how different *annotator types* (expert & non-expert human and AI annotators) vary in terms of their preferred personality traits. We use 10k annotated conversations from the *MultiPref* dataset by Miranda et al. [2025]. In this dataset, each datapoint is annotated by two *expert* and two *non-expert human annotators* as well as an *AI annotator* based on gpt-4-turbo-2024-04-09. Overall, we analyse 50k annotations on this dataset. We split both the expert and non-expert annotations into two distinct sampled sets of 10k each, with one annotation per datapoint. These sets are sampled from multiple annotators (each annotating *part* of the 10k datapoints), but allow us to evaluate the robustness of our toolkit.

Results. In [Figure˜7](https://arxiv.org/html/2509.26305v1#S3.F7), we observe that (1) annotators across types show *overall similar preferences*, but (2) with *varying strength magnitude*. Expert human annotations encourage the same traits with less *strength*, *non-expert* annotations with more strength, and the AI annotations with the most strength. A potential explanation is that *AI annotations may be following simpler heuristics than human annotations* that can be more directly explained by our relatively simple personality traits. Similarly, non-expert human annotations may follow simpler heuristics than expert human annotations. Further, encouragingly, we also observe that the results for expert and non-expert human annotators are very consistent for the two example sets collected (maximum difference in strength of $0.02$).

#### 3.1.3 PRISM: Personality in controversial and value-laden conversations

We also investigate the *PRISM* dataset by Kirk et al. [2024] consisting of around 8k annotated conversations, focused on controversial and value-laden topics. Unlike other human feedback datasets, PRISM’s annotations come with extensive annotator metadata including demographic details.

Results.
We find that PRISM demonstrates similar preferences to Chatbot Arena in terms of *verbosity*, *confidence*, and *factual correctness* – but differs in terms of preferred tone and language, notably preferring more *polite* and *less casual* language.
[Figure˜17](https://arxiv.org/html/2509.26305v1#A5.F17) in [Appendix˜E](https://arxiv.org/html/2509.26305v1#A5) reports the full results.

### 3.2 Personality traits in models

Next, we demonstrate the use of *Feedback Forensics* to investigate *differences in personality traits* across models. First, in [Section˜3.2.1](https://arxiv.org/html/2509.26305v1#S3.SS2.SSS1), we investigate differences in personality across a wide range of popular models. Then, in [Section˜3.2.2](https://arxiv.org/html/2509.26305v1#S3.SS2.SSS2), we take a closer look at the differences between two versions of Llama-4-Maverick, one released publicly and the other used for evaluation on Chatbot Arena.

#### 3.2.1 Differences across model families and developers

Figure: Figure 8: Most differing personality traits across models. We observe strong personality differences across models: GPT-5 stands out for generating less verbose responses with less formatting (bold/italics), Grok-4 for using personal pronouns more (e.g. I/we/you), and Claude for having less extreme traits. All measurements are compared to GPT-4o, using *strength* metric.

We evaluate AI personality differences between six popular models from multiple providers. We prompt each model with 500 English-language prompts from the arena-human-preference-100k dataset (see [Appendix˜C](https://arxiv.org/html/2509.26305v1#A3)). The prompts were manually filtered for quality, including to avoid offensive content and personally identifiable information (PII). Each model’s response is compared to GPT-4o as a reference model. High strength values indicate that the model exhibits a trait more than GPT-4o, low values the opposite.

Results. [Figure˜8](https://arxiv.org/html/2509.26305v1#S3.F8) shows strong differences across models, with some, such as Gemini-2.5-Pro or Mistral-Medium-3.1, using notable markdown formatting in verbose responses, whereas GPT-5 behaves very differently with more concise and less formatted responses.

#### 3.2.2 Llama-4-Maverick: A closer look

Figure: Figure 9: Comparison of personality traits of the Chatbot Arena *(arena)* and publicly released *(public)* versions of Llama-4-Maverick. We observe that the arena version of Llama-4-Maverick is more *verbose*, *enthusiastic* and *engaging*, and uses *more formatting* than the publicly released version.

The open-weights model *Llama 4 Maverick* was released on 5 April 2025. Around the same time, a related but non-identical experimental model version was evaluated on Chatbot Arena (*Llama-4-Maverick-03-26-Experimental*). Some users reported that these two models appear to have notable differences. In this section, we use our toolkit to quantitatively dissect how exactly the chat behaviour of the public and this arena version of *Llama 4 Maverick* differ. We refer to the two versions of *Llama 4 Maverick* as the *public model* (used for open-weights release) and *arena model* (used on Chatbot Arena around 5 April 2025, full name: *Llama-4-Maverick-03-26-Experimental*), respectively.

We do not have direct access to the arena model, but the Chatbot Arena team released a dataset of responses generated by it (see [Appendix˜C](https://arxiv.org/html/2509.26305v1#A3)). With Feedback Forensics, we can use this data to directly compare the arena model’s behaviour to the public model’s, without requiring new responses from the no longer accessible arena model itself (as conventional benchmarks would). We generate corresponding responses using the same prompt with the public model and annotate the resulting pairs with our annotators. As shown in [Figure˜9](https://arxiv.org/html/2509.26305v1#S3.F9), we observe strong personality differences between these two models. Among other differences, the arena model is more *verbose*, *enthusiastic* and *engaging*.

## 4 Related work

Automatically interpreting preference datasets.
We build on *Inverse Constitutional AI* (ICAI) [Findeis et al., 2025] for automatic detection of *principles* encoded in pairwise preference datasets.
We further extend the ICAI annotation pipeline for evaluation of our principles.

Understanding idiosyncrasies of language models.
Prior work by Dunlap et al. [2025] investigated LLM-based automatic detection of *“vibe”* differences between language models in a similar manner to ICAI’s approach to preference data.
We integrate some of the model behaviours found in this work into our curated personality selection set.
Relatedly, Sun et al. [2025] investigate model idiosyncrasies but focus on less personality-related features, such as characteristic words and phrases.
The authors find that model differences extend beyond simple word metrics, observing that specific models’ responses can often be identified equally well even after translation or rephrasing by another model,
supporting considering higher-level features as done in Feedback Forensics.

Human psychology in LLMs.
Jiang et al. [2023], Serapio-García et al. [2023],
Pellert et al. [2024] and Li et al. [2024b] investigate the application of human *psychometric* personality tests to LLMs.
Whilst some human psychology concepts transfer well, we think it is important to also investigate model personality independent of human personality.
Feedback Forensics takes an open-ended approach to defining personality and is able to capture subtle aspects of models, such as *sycophancy*, that more conventional human personality tests may miss.

Definition of LLM personality. In the context of LLMs, the terms model *personality*, *character*, *tone*, *style*, or *vibe* are often used with similar and overlapping meanings. Dunlap et al. [2025] define vibe generally as *“an axis along which a pair of texts can differ […] that is perceptible to humans”*. Lambert [2025] describes model character and personality as *“traits within the model [related to] the manner of its response, rather than the content”*. Serapio-García et al. [2023], following the psychology literature [Allport, 1937, Roberts and Yoon, 2022], describe personality more abstractly as *“encompass[ing] an entity’s characteristic patterns of thought, feeling, and behavior”*.
Aligning with the first two definitions above,
we use the term *personality trait* to refer to any characteristic of a model’s responses on a given distribution of prompts that distinguishes that model’s from other models’ responses.
We further focus on traits that are independent of the model’s capabilities.

Model evaluation based on human feedback.
*Chatbot Arena* [Chiang et al., 2024] is likely the most popular human feedback-based evaluation platform.
Over time multiple weaknesses in the evaluation protocol were observed and addressed,
e.g. controlling for over-emphasis of (markdown) styles [Li et al., 2024a] or of sentiment [Chen et al., 2025].
This motivates Feedback Forensics as a tool to study feedback data and the prevalence of such biases.

## 5 Limitations

Some limitations should be considered when using Feedback Forensics. Firstly, all measurements are *relative* to the underlying data distribution of prompts and responses. When measuring personality in a model, the strength of a trait is *relative* to reference models it is compared to. Similarly, when measuring the personality traits encouraged by feedback datasets the results are dependent on the distribution of prompts and responses. The same annotators may encourage different traits in different contexts (see differences between writing tasks in [Figure˜6](https://arxiv.org/html/2509.26305v1#S3.F6)). Secondly, we leverage AI annotators or LLM-as-a-Judge [Zheng et al., 2023] as part of our pipeline:
whilst trait agreement analysis shows annotators exhibit consistent behaviour across related traits ([Section˜E.1](https://arxiv.org/html/2509.26305v1#A5.SS1)) and we confirm strong agreement with human judgements ([Section˜E.2](https://arxiv.org/html/2509.26305v1#A5.SS2)),
LLM judges may also introduce their own biases and issues.
Results will also depend on the precise prompting and sampling strategies employed. Depending on the personality trait annotated, the results may vary. We strongly encourage manual inspection to go alongside the use of our framework to help mitigate potential issues. For some value-related traits, annotation may be inherently ambiguous and therefore noisy. We provide corresponding manual inspection tooling to assist with such analysis.
Finally, correlation does not imply causation: whilst annotations may correlate this does not necessarily mean that the original annotators followed a certain personality-selecting criterion. Nevertheless, correlating with selecting certain personalities may have (unintended) consequences during evaluation and training on such data – and is thus well worth being aware of.

## 6 Conclusion

We have introduced *Feedback Forensics*: an open-source Python toolkit to measure AI personality.
Our toolkit is able to *explicitly* measure a model’s personality traits that are not covered by conventional benchmarks
and were previously only *implicitly* covered by human feedback-based leaderboards, such as Chatbot Arena [Chiang et al., 2024].
We demonstrate our toolkit in two sets of experiments: (1) first we investigate the personality changes encouraged across popular human feedback datasets, including *Chatbot Arena* [Chiang et al., 2024], *MultiPref* [Miranda et al., 2025], and *PRISM* [Kirk et al., 2024]. Then, (2) we investigate personality differences across popular models, including from the GPT, Gemini, Mistral and Grok model families. Finally, we demonstrate the use of our tool to create an in-depth analysis of the personality differences between two widely-discussed Llama-4-Maverick versions.

Our contributions include the open-source *Feedback Forensics* toolkit (Apache-2.0), a web app for tracking AI personality traits in popular models and feedback datasets, and the underlying annotation data.(^7^77Code: [github.com/rdnfn/feedback-forensics](https://github.com/rdnfn/feedback-forensics), App: [app.feedbackforensics.com](https://app.feedbackforensics.com/))
We also include a tutorial for *getting started* with our toolkit in [Appendix˜A](https://arxiv.org/html/2509.26305v1#A1). We are excited to hear from the community how we can further extend *Feedback Forensics*: what additional models and datasets to analyse in our web app, what metrics and features to add to our toolkit.

#### Ethics statement

Impact. We hope that our toolkit can help improve the community’s understanding of previously opaque and potentially harmful model characteristics.
As such, we are optimistic that our toolkit will have a positive societal impact overall.
However, the limitations discussed in [Section˜5](https://arxiv.org/html/2509.26305v1#S5) should be kept in mind to avoid taking the results out of context to potentially amplify stereotyping or discrimination.

Datasets and Human Subjects.
We publish all datasets that were produced for this submission.
While these include human inputs in the form of prompts, those are sourced from previously published datasets which are duly referred to.
Novel aspects of the data lie in curation and AI judge annotations using the Feedback Forensics toolkit to enable analysis of the dataset.
The exception to this is the human study discussed in [Section˜E.2](https://arxiv.org/html/2509.26305v1#A5.SS2), in which we also provide novel human annotations to compare our AI annotators against.
Annotations were collected from one of the authors, who consents to this data being published.

Reproducibility.
All experimental results are reproducible using our open-source Feedback Forensics python toolkit and the datasets published with this paper.
We rely on API-based language models for our experiments.
Exact reproduction is contingent on these models remaining available, though our method can be applied with alternative models if needed.
Our primary contribution is the method of analysis, which is largely agnostic to the specific backbone language model used.
All datasets combine prior public datasets with LLM annotations generated using our toolkit (except for the human study in [Section˜E.2](https://arxiv.org/html/2509.26305v1#A5.SS2)), enabling full reproduction of the annotation process.

LLM Usage.
The authors used LLMs as general-purpose research tools.
This included text editing assistance, occasional drafting of short text snippets, programming assistance, and discussion of concepts and ideas.
The authors were the primary contributors and remain fully responsible for all aspects of the research and the published artifacts.

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## Appendix

## Appendix A Tutorial

In this Appendix, we provide a short tutorial on getting started with using Feedback Forensics locally. See our repository for full documentation ([github.com/rdnfn/feedback-forensics](https://github.com/rdnfn/feedback-forensics)).

### A.1 Installation

To begin using Feedback Forensics, install the package via pip:

### A.2 Getting started

After installation, you can start the Feedback Forensics app locally with:

This command launches the Feedback Forensics Gradio interface on localhost port 7860 (http://localhost:7860). See [Figure˜10](https://arxiv.org/html/2509.26305v1#A1.F10) for a screenshot of the interface.

Figure: Figure 10: Screenshots of Gradio app interface showing the dataset configuration and metrics view. See [app.feedbackforensics.com](https://app.feedbackforensics.com/).
Refer to caption: figures/screenshots/with_logo.png

### A.3 Investigating your own dataset

#### A.3.1 Setting up API keys

Before analysing your dataset, you need to annotate it with personality-selecting annotators. This requires setting API keys in a secrets.toml file as described in the main repo README.

#### A.3.2 Annotating your data

To annotate your dataset, run:

Replace example.csv with your dataset file. Your data must follow the ICAI standard format with columns text_a, text_b, and preferred_text.

#### A.3.3 Visualizing results

After annotation completes, view the results with:

### A.4 Advanced options

For more configuration options, you can use ICAI directly:

The parameters annotator.skip and s0_skip_principle_generation reduce costs by skipping unnecessary steps. Set s0_skip_principle_generation=false to generate new principles beyond the standard set.

### A.5 Programmatic usage

Feedback Forensics can be used within Python scripts:

All experimental figures included in this paper were created using this Python API for metrics computation and (partially) for plotting.

## Appendix B Additional metrics

In addition to the core metrics described in [Section˜2](https://arxiv.org/html/2509.26305v1#S2), our toolkit also supports computing additional metrics including:

- 1.
Agreement. We define the *agreement* between two sets of annotations as
$\texttt{agreement}=n_{\text{agreed}}/(n_{\text{agreed}}+n_{\text{disagreed}})$,
where $n_{\text{agreed}}$ and $n_{\text{disagreed}}$ are the number of datapoints where the two annotation sets agree and disagree, respectively. We only consider datapoints where both annotations are non-tie votes for this metric.

## Appendix C Datasets

### C.1 External datasets

In the following we provide further details on the datasets used throughout this paper.

- 1.
Chatbot Arena [Chiang et al., 2024]. Due to the ongoing collection of crowd-sourced data in Chatbot Arena, many different versions and releases of corresponding Chatbot Arena datasets exist. Throughout this work we use multiple different releases of Chatbot Arena datasets, described below.
(a)
Arena Explorer release (arena-human-preference-100k). Conversations in English, collected between June 2024 and August 2024. User prompts licensed under CC-BY-4.0, model outputs governed by terms of use of model providers. Source: [https://hf.co/datasets/lmarena-ai/arena-human-preference-100k](https://hf.co/datasets/lmarena-ai/arena-human-preference-100k)
(b)
Llama-4-Maverick release (Llama-4-Maverick-03-26-Experimental_battles). User prompts licensed under CC-BY-4.0, model outputs governed by terms of use of model providers. Source: [https://huggingface.co/spaces/lmarena-ai/Llama-4-Maverick-03-26-Experimental_battles/blob/main/data/clean-llama4.jsonl](https://huggingface.co/spaces/lmarena-ai/Llama-4-Maverick-03-26-Experimental_battles/blob/main/data/clean-llama4.jsonl)
(c)
MultiPref subset (chatbot_arena_conversations). Multipref itself is licensed under Open Data Commons Attribution License (ODC-By), the underlying Chatbot Arena data has two licenses: prompts under CC-BY-4.0, model outputs under CC-BY-NC-4.0. Source: [https://huggingface.co/datasets/lmsys/chatbot_arena_conversations](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)
- 2.
MultiPref [Miranda et al., 2025]. MultiPref combines prompts from prior datasets alongside newly sampled model outputs and human and model annotations. MultiPref itself is licensed under Open Data Commons Attribution License (ODC-By), licenses for the other subparts (Chatbot Arena, WildChat, ShareGPT, Anthropic Harmless/Helpful) are discussed above or below. Source [https://huggingface.co/datasets/allenai/multipref](https://huggingface.co/datasets/allenai/multipref).
- 3.
PRISM [Kirk et al., 2024]. License: Human-written texts (including prompts) licensed under CC-BY-4.0, model responses under CC-BY-NC-4.0 and further subject to original model provider terms of use. Source: [https://huggingface.co/datasets/HannahRoseKirk/prism-alignment](https://huggingface.co/datasets/HannahRoseKirk/prism-alignment)
- 4.
WildChat [Zhao et al., 2024]. Licensed under Open Data Commons Attribution License (ODC-By). Source: [https://huggingface.co/datasets/allenai/WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M).
- 5.
ShareGPT [Chiang et al., 2023]. No specific licensing information dedicated or link to this dataset found, we refer to the MultiPref dataset using ShareGPT for more details: [https://huggingface.co/datasets/allenai/multipref](https://huggingface.co/datasets/allenai/multipref)
- 6.
Anthropic Harmless/Helpful [Bai et al., 2022]. Licensed under MIT license. Source: [https://github.com/anthropics/hh-rlhf](https://github.com/anthropics/hh-rlhf)

### C.2 Annotation dataset

We are releasing our annotation dataset to encourage further research on personality traits in model responses. The data, collected for the experiments presented in this work, is available at [hf.co/datasets/rdnfn/ff-annotations](https://hf.co/datasets/rdnfn/ff-annotations) under the *Open Data Commons Attribution License* (ODC-By). Annotations were generated with the *Inverse Constitutional AI* (ICAI) pipeline [Findeis et al., 2025] with a fixed set of personality traits to test, using Google’s Gemini-2.5-Flash. Details regarding the models are provided in [Appendix˜F](https://arxiv.org/html/2509.26305v1#A6).

This dataset includes annotations for (subsets of) *Chatbot Arena* [Chiang et al., 2024], *MultiPref* [Miranda et al., 2025], *PRISM* [Kirk et al., 2024], as well as annotations for model generations collected for our experiments in [Section˜3.2](https://arxiv.org/html/2509.26305v1#S3.SS2). Note that we do *not* include prompts and responses from the original datasets, instead providing metadata (e.g., conversation_id) to enable merging with the base data. The model generations used for [Section˜3.2](https://arxiv.org/html/2509.26305v1#S3.SS2) are available separately from the annotation data at [hf.co/datasets/rdnfn/ff-model-personality](https://hf.co/datasets/rdnfn/ff-model-personality) (ODC-By license). The annotation data is sufficient for independent local analysis with the Feedback Forensics Gradio app, even without merging.

## Appendix D Online AI personality Discussions

As discussed in [Section˜2.1](https://arxiv.org/html/2509.26305v1#S2.SS1), we partly base our set of tested personality traits on online discussion on the topic:

- 1.
[https://x.com/lmarena_ai/status/1909397817434816562](https://x.com/lmarena_ai/status/1909397817434816562)
- 2.
[https://x.com/suchenzang/status/1908795054011146308](https://x.com/suchenzang/status/1908795054011146308)
- 3.
[https://x.com/techdevnotes/status/1908851730386657431](https://x.com/techdevnotes/status/1908851730386657431)

## Appendix E Extended experimental results

We extend on the results included in the main body by providing additional details.

### E.1 Trait agreement analysis

We analyse the agreement of the top and bottom 5 encouraged traits in Chatbot Arena data ([Figure˜5](https://arxiv.org/html/2509.26305v1#S3.F5)).
For each text pair, a personality trait annotator can either choose one of the texts or declare non-relevance.
We measure Cohen’s kappa $\kappa$ in cases where both principles were relevant and report the relevance overlap (number of cases where both traits relevant divided by number of cases where at least one relevant) for additional context.

Figure: Figure 11: Trait agreement heatmap. We measure weighted Cohen’s kappa between the top 5 and bottom 5 traits encouraged by Chatbot Arena annotations. The main colors indicate $\kappa$ values, the inner rectangles indicate the relevance overlap (both relevant divided by at least one relevant). Values with overlap above 0.2 are additionally bolded.
Refer to caption: x1.png

[Figure˜11](https://arxiv.org/html/2509.26305v1#A5.F11) confirms many intuitively plausible correlations, such as conciseness being opposed to verbosity and avoidant tone agreeing with refusal to answer.
It also allows for less immediately obvious but plausible observations, such as factual correctness agreeing with structured formatting, verbosity, examples and confidence – correlations that are likely often true, but may also be exaggerated by the annotating model’s biases (as discussed in [Section˜5](https://arxiv.org/html/2509.26305v1#S5)).

### E.2 Comparison of AI to human personality annotations

Our framework by default uses AI annotators to annotate personality traits. This setup raises the question whether AI annotations are suitable for annotating personality traits. Whilst other work has explored the agreement between general human and AI preference annotations [Li et al., 2024c, Zheng et al., 2023, Miranda et al., 2025], as far as we are aware, no prior work has previously explored AI annotators’ ability to annotate *personality traits* specifically. Thus, we conducted our own experiments to validate the use of AI annotators in the context of annotating personality traits.

Setup.
We collected human reference annotations for the top 5 and bottom 5 traits in Chatbot Arena data found by our toolkit using an earlier version of our AI annotator powered by GPT-4o-mini.
These human annotations were collected for 100 random comparisons of the same dataset, resulting in 1,000 trait-level human judgements overall.(^8^88These annotations were collected from one of the authors. We were unable to collect annotations from other sources due to resource constraints. We aimed to provide an unbiased sample nonetheless with blind labelling: Each comparison-trait pair was labelled without seeing LLM decisions. The annotator first assessed relevance, then if relevant, selected which response better expressed the trait.) We compare the human annotations against LLM votes from our standard single annotation setup, and an alternative multi-vote annotation setup requiring unanimous vote by multiple AI annotators.

These experiments serve two purposes: To choose a suitable AI annotator configuration (backbone model and single- or multi-vote) with high human agreement for the remaining experiments and to provide validation for that annotator.
We thus first evaluate the performance of different LLMs for our personality annotation task and then
evaluate whether re-annotating traits multiple times (*multi-vote*) helps improve AI annotator performance relative to simply annotating once (*single-vote*).
In multi-vote, we use unanimous voting to select one model output according to each trait. If there is no unanimous agreement, the trait is deemed not relevant for the datapoint. Note that the first experiments only use multi-voting.

Results. The results are shown in [Tables˜1](https://arxiv.org/html/2509.26305v1#A5.T1) and [2](https://arxiv.org/html/2509.26305v1#A5.T2). We consider the following metrics, reporting the mean and standard deviation over 3 random seeds:

- 1.
Relevance agreement (*Relevance*): fraction where human and LLM annotators agree on relevance of the trait (ignoring direction). Best shown in bold. Expected chance agreement when annotating randomly would be 0.5.
- 2.
Choice agreement (*Choice*): among comparisons where both deemed the trait relevant, fraction where human and LLM annotators choose the same side. Best shown in bold. Expected chance agreement when annotating randomly would be 0.5.

Observations. In the cross-model experiments shown in [Table˜1](https://arxiv.org/html/2509.26305v1#A5.T1), we observe far higher agreement with human choice for GPT-5-Mini and Gemini-2.5-Flash than for GPT-4o-Mini.
GPT-5-Mini overall outperforms the other models in terms of choice agreement, achieving a mean of 92% and a minimum of 81% across traits, with Gemini-2.5-flash a close second.
In terms of relevance, the agreement tends to be lower.
This matches the annotator’s observations during annotation, where relevance was often more ambiguous than choice.
Nevertheless, the results show that Gemini-2.5-Flash and GPT-5-Mini largely agree with human agreements, especially in terms of choice.

The single- vs multi-vote experiments in [Table˜2](https://arxiv.org/html/2509.26305v1#A5.T2) further show that multi-vote slightly improves both the mean relevance and choice agreement.
As the improvement is relatively small, it does not justify the higher (3x) costs in our experiments.

Choice of AI annotator.
Based on these results, we decided to use a single-vote Gemini-2.5-Flash annotator for most of our experiments.
Whilst GPT-5-mini has slightly higher agreement, the cost of running that model was notably higher - in particular because of the large number thinking tokens generated.
Similarly, multi-vote only provided very limited improvements relative to single vote for Gemini-2.5-flash. We thus choose to use single-vote to avoid the 3x cost of multi-vote.
If cost is no limitation, we would recommend using GPT-5-mini (or even larger models such as GPT-5) with multi-vote instead.

**Table 1: Model agreement with human annotations (mean and std, 3 seeds).**
|  | gpt-4o-mini | gpt-4.1-mini |  |  |
| --- | --- | --- | --- | --- |
| Trait | Relevance | Choice | Relevance | Choice |
| is more verbose | 0.53 ±0.02 | 0.90 ±0.02 | 0.70 ±0.02 | 0.92 ±0.02 |
| has more structured formatting | 0.48 ±0.02 | 1.00 ±0.00 | 0.68 ±0.01 | 0.87 ±0.02 |
| makes more confident statements | 0.61 ±0.01 | 0.57 ±0.05 | 0.56 ±0.01 | 0.78 ±0.16 |
| is more factually correct | 0.70 ±0.02 | 0.48 ±0.03 | 0.77 ±0.01 | 0.78 ±0.11 |
| more strictly follows the requested output format | 0.91 ±0.00 | 0.00 ±0.00 | 0.66 ±0.03 | 0.61 ±0.28 |
| is more concise | 0.55 ±0.02 | 0.98 ±0.03 | 0.71 ±0.02 | 0.91 ±0.01 |
| has a more avoidant tone | 0.92 ±0.00 | 0.00 ±0.00 | 0.94 ±0.00 | 1.00 ±0.00 |
| refuses to answer the question | 0.97 ±0.01 | 1.00 ±0.00 | 0.98 ±0.00 | 1.00 ±0.00 |
| ends with a follow-up question | 0.92 ±0.00 | 1.00 ±0.00 | 0.93 ±0.01 | 1.00 ±0.00 |
| is more polite | 0.75 ±0.02 | 0.83 ±0.24 | 0.76 ±0.01 | 1.00 ±0.00 |
| Min | 0.48 | 0.00 | 0.56 | 0.61 |
| Mean | 0.73 | 0.68 | 0.77 | 0.89 |

**Table 2: Single- vs multi-vote human agreement (Gemini-2.5-Flash, mean and std, 3 seeds).**
|  | Single-vote | Multi-vote |  |  |
| --- | --- | --- | --- | --- |
| Trait | Relevance | Choice | Relevance | Choice |
| is more verbose | 0.72 ±0.00 | 0.90 ±0.01 | 0.68 ±0.01 | 0.93 ±0.01 |
| has more structured formatting | 0.67 ±0.00 | 0.88 ±0.00 | 0.72 ±0.03 | 0.90 ±0.02 |
| makes more confident statements | 0.61 ±0.01 | 0.80 ±0.00 | 0.79 ±0.00 | 0.61 ±0.08 |
| is more factually correct | 0.86 ±0.00 | 0.61 ±0.04 | 0.87 ±0.01 | 0.75 ±0.04 |
| more strictly follows the requested output format | 0.77 ±0.00 | 0.67 ±0.00 | 0.94 ±0.01 | 1.00 ±0.00 |
| is more concise | 0.63 ±0.01 | 0.91 ±0.01 | 0.49 ±0.00 | 0.95 ±0.00 |
| has a more avoidant tone | 0.97 ±0.00 | 1.00 ±0.00 | 0.95 ±0.00 | 1.00 ±0.00 |
| refuses to answer the question | 0.98 ±0.00 | 1.00 ±0.00 | 0.97 ±0.00 | 1.00 ±0.00 |
| ends with a follow-up question | 0.90 ±0.01 | 1.00 ±0.00 | 0.91 ±0.01 | 1.00 ±0.00 |
| is more polite | 0.74 ±0.01 | 0.82 ±0.00 | 0.80 ±0.01 | 1.00 ±0.00 |
| Min | 0.61 | 0.61 | 0.49 | 0.61 |
| Mean | 0.78 | 0.86 | 0.81 | 0.91 |

### E.3 Extended pairwise feedback results

#### E.3.1 Across datasets

First, in [Figures˜12](https://arxiv.org/html/2509.26305v1#A5.F12), [13](https://arxiv.org/html/2509.26305v1#A5.F13) and [14](https://arxiv.org/html/2509.26305v1#A5.F14), we provide a comprehensive comparison of the personality traits encouraged by the three preference datasets considered.

Figure: Figure 12: Comparison of investigated human feedback datasets in terms of *strength*. As usual, positive strength is shown in blue and negative strength in red. MultiPref annotations considered here are a combination of all expert and non-expert human votes. Sorted by max difference. Whilst overall the personality traits each have similar strength across preference datasets, we observe some exceptions: annotations in Chatbot Arena do not appear to prefer *polite* models as the other datasets do. Similarly, Chatbot Arena annotations do (approximately) not actively encourage *less harmful* responses or responses with *ethical considerations*.

Figure: Figure 13: Comparison of investigated human feedback datasets in terms of *relevance*. Strong relevance is shown in blue. We observe notable differences between the datasets that are likely explained by the difference in domains. Whereas MultiPref and Chatbot Arena include a lot of text with *structured formatting* (above 60%), PRISM (focused on value-laden topics) does not (below 30%). On the other hand we observe that *friendlier* and *more polite* tone appear to be more relevant in the PRISM context.

Figure: Figure 14: Comparison of investigated human feedback datasets in terms of *Cohen’s kappa* ($\kappa$). As with *strength*, positive $\kappa$ is shown in blue and negative $\kappa$ in red. We observe why the strength metric is helpful: whilst some personality traits have high $\kappa$ here, their relevance to the overall dataset is minimal (as seen in [Figure˜13](https://arxiv.org/html/2509.26305v1#A5.F13)), for example *inappropriate language*.

#### E.3.2 Chatbot Arena

Figure: Figure 15: Extended list of most (blue) and least (red) encouraged personality traits in Chatbot Arena.

#### E.3.3 MultiPref

Figure: Figure 16: Extended list of most (blue) and least (red) encouraged personality traits in MultiPref.

#### E.3.4 PRISM

Figure: Figure 17: List of most (blue) and least (red) encouraged personality traits in PRISM.

### E.4 Extended model results

#### E.4.1 General model comparison

[Figures˜18](https://arxiv.org/html/2509.26305v1#A5.F18), [19](https://arxiv.org/html/2509.26305v1#A5.F19) and [20](https://arxiv.org/html/2509.26305v1#A5.F20) *strength*, *relevance*, and *Cohen’s kappa* metrics for each model for all tested traits. These figures provide a more comprehensive view of the results shared in [Section˜3.2.1](https://arxiv.org/html/2509.26305v1#S3.SS2.SSS1).

Figure: Figure 18: Full results for models in terms of *strength*. Sorted by maximum difference.

Figure: Figure 19: Full results for models in terms of *relevance*. Sorted by maximum difference.

Figure: Figure 20: Full results for models in terms of *Cohen’s kappa ($\kappa$*). Sorted by maximum difference.

#### E.4.2 Llama-4-Maverick analysis

Figure: Figure 21: Extended comparison of personality traits of the Chatbot Arena *(arena)* and publicly released *(public)* versions of Llama-4-Maverick.

## Appendix F Models

Throughout our experiments we use a diverse set of models from multiple providers. Below is a list of all models used, including their *full name* (including provider) and the *short name* used in the paper (in brackets). All models used via [https://openrouter.ai/](https://openrouter.ai/).

- 1.
Anthropic
(a)
anthropic/claude-4 (Claude 4)
- 2.
Google
(a)
google/gemini-2.5-pro (Gemini-2.5-Pro)
(b)
google/gemini-2.5-flash (Gemini-2.5-Flash)
- 3.
Meta
(a)
meta-llama/llama-4-maverick (Llama-4-Maverick)(^9^99Note that, in addition, responses from a different non-public version of Maverick were used in [Section 3.2.2](https://arxiv.org/html/2509.26305v1#S3.SS2.SSS2))
- 4.
Mistral
(a)
mistralai/mistral-medium-3.2 (Mistral-Medium-3.1)
- 5.
OpenAI (used directly via OpenAI API, [https://openai.com/api/](https://openai.com/api/))
(a)
openai/gpt-4.1-2025-04-14 (GPT-4.1)
(b)
openai/gpt-4o-2024-08-06 (GPT-4o)
(c)
openai/gpt-4o-mini-2024-07-18 (GPT-4o-mini)
(d)
openai/gpt-5-2025-08-07 (GPT-5)
(e)
openai/gpt-5-mini-2025-08-07 (GPT-5-mini)
(f)
openai/gpt-oss-20b (GPT-oss-20b)
- 6.
xAI
(a)
x-ai/grok-4 (Grok-4)

## Appendix G Compute resources

The overall compute costs for all new annotations created as part of the experiments included in this paper version is approximated to be slightly less than 100 USD.

## Appendix H Prompts

### H.1 Personality selection prompts

#### H.1.1 Trait selection process

*This section extends the description of the trait selection process in [Section˜2.1](https://arxiv.org/html/2509.26305v1#S2.SS1). For comprehensibility, we briefly repeat part of this section here.*

To construct the manually curated list, we collected instructions that select for known AI personality traits and can be given to an objective-following AI annotator. We refer to this list as PersonalitySelectionPrompts-v1 and make it publicly available in our repo. We identify personality traits based on three sources: (1) we consider the literature discussing model idiosyncrasies and annotation biases [Li et al., 2024a, Chen et al., 2025], (2) online discussions on how different models’ personalities differ,(^10^1010See [Appendix D](https://arxiv.org/html/2509.26305v1#A4)) and finally (3) automatically identified objectives in human feedback datasets and differences between models within such datasets, discovered using the ICAI and VibeCheck [Dunlap et al., 2025] approaches. This provided us with a large source of potential traits.

To select the final set of traits, we iteratively used the following criteria on potential traits: (a) is the trait considered relevant according to multiple sources, (b) did the trait empirically perform well in feedback forensics experiments, and (c) did we consider the trait to be potentially interesting/insightful to users. If we found a trait to satisfy one or (ideally) more of these criteria, and there was no equivalent or similar trait already in the trait list, we added the trait to the list. Overall we collected 40 traits with this process. We are planning to keep iterating and updating the standard set of traits tested by our toolkit. Further, our toolkit allows users to provide their own list of traits to test instead, or in addition, to our standard list.

#### H.1.2 Traits

We make available manually curated set of prompts, named PersonalitySelectionPrompts-v1. In Listing LABEL:lst:personalityv1 below, we include the complete list of 40 selection criteria. The construction process is described in [Section˜2.1](https://arxiv.org/html/2509.26305v1#S2.SS1) and above.

Figure: Listing 1: PersonalitySelectionPrompts-v1

## Appendix I Annotator prompt

To instruct our annotators, we use the prompt shown in Listing LABEL:lst:annotatorprompt from the *Inverse Constitutional AI* [Findeis et al., 2025] package. To enable compute-efficient annotation, the annotator is asked to annotate multiple personality traits at the same time. We thank all contributors to the package for their help improving this and the other prompts in the ICAI package.

Figure: Listing 2: Personality-selecting annotator prompt

## Peer Reviews

confidence: 2
contribution: 2
initial_rating: 4
presentation: 3
questions: - Can you expand the human validation beyond the single-author study (e.g., multi-annotators replication on 500 pairs) and report inter-rater κ and human–AI agreement per trait?
- Would the conclusions hold with a different judge model family (e.g., GPT and Qwen models) or when using multi-vote annotation?
soundness: 3
strengths: - **Clear motivation & scope.** The paper tackles an under-explored aspect of alignment (implicit stylistic bias in feedback training) and provides a practical, reproducible framework to study it. The engineering contribution (toolkit + web UI + datasets) is substantial and well-documented.
- **Comprehensive case studies.** Analyses on Chatbot Arena, MultiPref, and PRISM uncover consistent, interpretable patterns, the cross-model comparison and the Llama-4-Maverick public vs Arena contrast demonstrate the toolkit’s utility in post-hoc auditing.
summary: This paper proposes Feedback Forensics, an open-source toolkit (with a Python API and a Gradio/Web app) for explicitly measuring “AI personality”, stylistic and tone-level traits that are typically only implicitly captured by human-preference evaluations (e.g., Chatbot Arena). The method augments pairwise response datasets with three kinds of annotations: (i) human preference labels, (ii) “target-model” selectors to isolate a specific model’s outputs, and (iii) AI-annotator personality labels that choose which response exhibits a trait more (e.g., verbosity, confidence). It then computes trait-level agreement using relevance, Cohen’s κ, and a combined “strength = κ × relevance” metric to quantify how much a trait is encouraged by feedback or exhibited by a target model. Using this toolkit, the authors (A) analyze trait preferences in Chatbot Arena, MultiPref, and PRISM, and (B) compare trait profiles across popular models and between two versions of Llama-4-Maverick. The paper releases code, an interactive app, and AI-generated annotation data.
weaknesses: - **Human validation is minimal.** The entire human–AI agreement study (Appendix F.2) relies on a single author annotator (100 pairs ≈ 1 000 trait-level judgments). While κ ≈ 0.4–0.6 suggests moderate agreement, the absence of multiple raters, confidence intervals, or inter-rater reliability limits claims of external validity.
- **Statistical under-specification.** Metrics are reported as single values without uncertainty estimates or variance analysis. Because strength = κ × relevance is multiplicative and κ depends on class imbalance, small differences may not be significant.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: The authors frame many traits in a way that implies a preference. However, many traits are not inherently "bad," just different (like human personalities). Could the toolkit be used to explore the utility of different personality profiles? For example, in a high-stakes domain like finance or medicine, a "cautious" and "less confident" personality that "acknowledges limitations more" might be strongly preferred. How do you see the tool being used to define optimal, domain-specific personalities rather than just tracking a single "better" one?

To follow up, do different personalities imply performance differences in certain capabilities? Do the authors have any evidence or hypotheses about whether optimizing for these "personality" traits has a positive or negative causal effect on a model's underlying capabilities in, for example, complex reasoning, math, or code generation? Are we, as a community, creating "personalities" that we find pleasing but that might mask trade-offs in downstream task performance?
soundness: 3
strengths: * Solid Implementation and Usability: This is a well-executed project. The authors have delivered not just a concept but a functional, open-source toolkit. Having interacted with the web application, it's clear the tool is polished, intuitive, and provides a valuable, accessible resource for the community to inspect models and datasets directly.

* Comprehensive Experiments: The utility of the toolkit is demonstrated through a comprehensive set of experiments. The analysis spans multiple popular feedback datasets (Chatbot Arena, MultiPref, PRISM) and a wide array of modern, popular LLMs, providing a broad and relevant snapshot of the current landscape.
summary: This paper introduces Feedback Forensics, an open-source Python toolkit, accompanying web application, and annotation dataset designed to measure and track the "personality" of Large Language Models (LLMs). The core methodology involves using AI annotators (i.e., LLM-as-a-Judge) to evaluate pairs of model responses against a set of curated personality traits (e.g., "is more verbose," "makes more confident statements"). The authors demonstrate two primary use cases for this toolkit: analyzing feedback datasets (e.g., Chatbot Arena, MultiPref, PRISM) and analyzing models (e.g., Gemini 2.5 etc).
weaknesses: * A Measurement Tool, Not an Explanatory Science: The paper is good at describing personality traits but does not offer a deep investigation into why they emerge or how they are encoded, or any other scientific understanding / insights into related problems. The method—collecting pairwise preferences based on predefined, human-articulated traits—feels like a more granular and automated version of existing platforms like Chatbot Arena. It is "forensics" in the sense of identifying a trait, but not in the sense of uncovering the underlying cause (e.g., is a trait an artifact of RLHF, a result of data mixture, or an emergent property of the architecture, or how should we expect models to change under different conditions?).

* Limited Scope on "Control": The work is focused on "measuring" and "tracking." It does not propose new mechanisms for controlling these behaviors beyond the implied (and already standard) method of simply creating a preference dataset for a desired trait and training on it. The contribution is thus more of a high-quality data-gathering and computation tool rather than a new scientific framework for understanding or steering model personality.

---

confidence: 4
contribution: 3
initial_rating: 4
presentation: 2
questions: - The current framework treats high-level "personality traits" as discrete, binary, or categorical labels, but many relevant features of AI personality (e.g., confidence, politeness) are multifaceted and can vary on a spectrum. Is there any discussiom about this point?
- How does Feedback Forensics compare quantitatively and qualitatively with prior personality measurement techniques for LLMs (e.g., Big Five surveys, direct style benchmarks, or recent works)? Can you report performance or findings on shared benchmarks?
- How do the personality trait measurements produced by Feedback Forensics translate into actionable improvements in the overall performance or reliability of LLMs, beyond simply characterizing stylistic differences? For example, unlike MBTI-style personality assessments—which primarily offer descriptive labels and do not directly affect task performance or user satisfaction—can your toolkit's results be used to guide model training, calibrate feedback datasets, or optimize model deployment in ways that measurably enhance the utility, safety, or user experience of LLMs? Have you evaluated or do you anticipate any concrete downstream impacts (such as reducing undesirable behaviors or increasing user trust) based on trait monitoring and adjustment?
soundness: 3
strengths: - This paper makes a substantial and timely contribution by introducing an open-source, extensible toolkit for measuring and tracking AI model personality traits. 
- Its operationalization of "personality measurement" within the context of AI model development seems signifgant. While prior works often qualitatively discuss model style or provide ad hoc definitions, this toolkit formalizes and systematizes personality trait assessment across datasets and models, drawing on techniques from Inverse Constitutional AI and enriching them with a modular annotation and metric computation pipeline. 
- The authors provide both a Python API and a browser-accessible web app, lowering the barrier for practitioners to inspect personality drift or trait encouragement in large models and feedback datasets. The toolkit fills an important methodological gap left by conventional benchmarks, providing the community with a much-needed lens on a critical qualitative dimension of model behavior.
summary: This paper introduces Feedback Forensics, an open-source toolkit designed to measure and analyze the "personality" traits of AI language models—characteristics in model responses such as politeness, verbosity, or tone that influence user experience but are not captured by standard benchmarks. Leveraging both human and AI-generated annotations of pairwise model response data, the toolkit quantifies which traits are encouraged by human (or AI) feedback and which traits are exhibited by various AI models. The authors illustrate their method through a Python API and an interactive web app, releasing not only the toolkit and infrastructure but also underlying annotation datasets. The toolkit aims to provide transparency and actionable insights into model personality, helping researchers and practitioners identify, track, and optimize desirable conversational behaviors across models and feedback-based training regimes.
weaknesses: - The most salient is the lack of direct comparison to existing baselines in the personality measurement domain. There is now a growing body of work proposing frameworks or metrics to quantify AI or LLM "personality", such as evaluations based on the Big Five personality model, or Model Personality Evaluations [1][2]. Without benchmarking Feedback Forensics against such prior methods, it is difficult to clearly assess its incremental value, or to know under what conditions it outperforms, complements, or perhaps fails compared to alternatives.
- The trait taxonomy employed in Feedback Forensics is not extensively justified: it is not clear if the set of personality traits is exhaustive or robust to more subtle variations in conversational style, and there may be gaps in coverage (e.g., cross-cultural or context-dependent personality facets).
- Since the annotators performing trait detection are themselves LLMs, and as these same (or similar) LLMs contribute to both feedback and target models, there is a risk that toolkit outputs merely reflect the biases of the model(s) rather than an independent grounded measurement. This could create feedback loops whereby models overfit to their own interpretations of style and personality. Also, from my point of view, It is unclear how robust trait detection and measurement are to changes in prompt phrasing, dialogue context, or user intent. No ablation studies or error analyses are presented to ensure that detected personality differences are intrinsic to models, and not artifacts of particular prompt distributions.

[1] Evaluating and Inducing Personality in Pre-trained Language Models 
[2] https://arxiv.org/abs/2406.17675


Decision: Reject