Venue: NAACL 2025

# CAVE: Controllable Authorship Verification Explanations

Sahana Ramnath\({}^{\blacktriangledown}\) Karitik Pandey\({}^{\blacktriangledown}\) Elizabeth Boschee\({}^{\blacklozenge}\) Xiang Ren\({}^{\blacktriangledown}\)

\({}^{\blacktriangledown}\)Department of Computer Science, University of Southern California

\({}^{\blackdiamondsuit}\)Information Sciences Institute, University of Southern California

sramnath@usc.edu

###### Abstract

Authorship Verification (**A**v) (do two documents have the same author?) is essential in many real-life applications. Av is often used in privacy-sensitive domains that require an offline proprietary model that is deployed on premises, making publicly served online models (APIs) a suboptimal choice. Current offline AV models however have lower downstream utility due to limited accuracy (eg: traditional stylometry AV systems) and lack of accessible post-hoc explanations. In this work, we address the above challenges by developing a _trained_, _offline_ model **CAve** (Controllable Authorship Verification Explanations): Cave generates free-text Av explanations that are controlled to be (1) _accessible_ (uniform structure that can be decomposed into sub-explanations grounded to relevant linguistic features), and (2) easily verified for explanation-label _consistency_. We generate silver-standard training data grounded to the desirable linguistic features by a prompt-based method **Prompt-CAVE**. We then filter the data based on rationale-label consistency using a novel metric **Cons-R-L**. Finally, we fine-tune a small, offline model (LLaMa-3-8B) with this data to create our model Cave. Results on three difficult Av datasets show that Cave generates high quality explanations (as measured by automatic and human evaluation) as well as competitive task accuracy1.

Footnote 1: github.com/INK-USC/Controllable-AV-Explanations

## 1 Introduction

Authorship Verification (Av) Koppel and Winter [4] is the NLP task of determining if two input documents were written by the same author. Av is used for tasks such as plagiarism detection, forensic analysis (often in support of law enforcement), analysis of the spread of misinformation. Given the sensitive nature of this task, it is imperative to develop Av methods that are explainable, accessible and secure. Explainability ensures that the system's decisions can be understood and trusted by the user. Accessibility ensures easier downstream usability; for example, a well-structured free-text explanation with clearly defined topic points is more easily understood than unstructured explanations such as long lists of low-level features. Finally, security is necessary to safely use the Av system for proprietary data; a locally-hosted model on the user's server is more secure than an API call to an online model, which could potentially store the user's sensitive data. Av is also an extremely difficult task for humans, in contrast to other NLP tasks such as machine translation; this further necessitates the need for automatic, high-quality Av systems.

Early works performed Av by comparing handcrafted features such as n-grams van Dam [10], POS tags Moreau and Vogel [9], LIWC fea

Figure 1: Cave generates uniformly structured free-text explanations grounded in relevant linguistic features, that can be automatically verified for consistency.



tures (Uchendu et al., 2020); while these are explainable, they are hard to scale. Subsequent works employed neural architectures to improve scalability and accuracy, such as the usage of Siamese networks Araujo-Pino et al. (2020); Najafi and Tavan (2022); Boenninghoff et al. (2019) to assign authorship via distance between document embeddings. These methods, however, have little or no post-hoc explainability. This poses a huge problem: one cannot, for instance, make legal assertions about the authorship of a set of questionable documents solely on the basis of "a system said so" - decision makers (judges, university officials, intelligence analysts, etc.) need to know _why_. Recent years have seen the surge of large language models and their self-rationalization capabilities: Hung et al. (2023); Huang et al. (2024b) generate freetext rationales2 for Av from models such as GPT-4 OpenAI (2024b) in a zero/few-shot manner. These methods however face issues such as unreliability of online models (varying results/details across runs, deprecation of APIs), inconsistency of rationales with predicted label (C), and high expenses.

Footnote 2: We use terms explanation and rationale interchangeably.

In this work, we propose our model Cave**(Controllable **A**uthorship **V**erification Explanations), an _in-house_ model that generates authorship predictions along with high-quality, _free-text rationales_ that explain the former post-hoc. The rationales are structured as a set of sub-explanations (refer Table 1) grounded to AV-relevant linguistic features Hung et al. (2023); Boenninghoff et al. (2019); these sub-explanations have corresponding (intermediate) labels that provide further structure and also serve as a means to verify overall consistency with Cave's authorship prediction.

Since there is no human-written Av explanation data that can be used to train our in-house model, we adopt the model distillation strategy: West et al. (2022); Li et al. (2023, 2024) we engineer **Prompt-CAVE** to generate silver-standard train data in the format described above by prompting a large, oracle model (GPT-4-Turbo OpenAI (2024a)), and use that to supervised-finetune a smaller language model (LLaMa-3-8B Meta-AI (2024)). We also introduce our automatic metric Cons-R-L that measures the consistency between the rationale and the model's final label; Cons-R-L is used to filter the silver train data before finetuning Ramnath et al. (2023); Li et al. (2023), and also assess Cave during inference. Our experiments with three difficult Av datasets show that Cave leads to competitive task performances as

\begin{table}
\begin{tabular}{l|c} \hline \hline punctuation style: Both texts use a variety of punctuation, including commas, periods, and quotation marks, but Text 1 uses more diverse punctuation such as parentheses and hyphens. MAYBE \\ \hline special characters style, capitalization style: Text 1 uses continuous capitalization for emphasis (e.g., ‘WONDERFUL’, ’THRILLED’), which is not observed in Text 2. NO \\ \hline acronyms and abbreviations: Neither text makes significant use of acronyms or unusual abbreviations. YES \\ \hline writing style: Text 1 has a more personal, reflective style, sharing personal opinions and feelings about the movie. Text 2 provides a more detached, narrative-style review without personal input. NO \\ \hline expressions and idioms: Both texts avoid colloquial expressions and idioms, opting for a more formal format. YES \\ \hline tone and mood: Text 1 has a more varied tone, from enthusiasm to disappointment, while Text 2 maintains a consistent, somewhat formal and analytical tone. NO \\ \hline sentence structure: Text 1 features a mix of short and long sentences with more complex structures, while Text 2 tends to use more uniformly structured, intermediate-length sentences. MAYBE \\ \hline any other relevant aspect: The approach to movie critique is different; Text 1 is more about the impact on the viewer, while Text 2 focuses on plot summary and cinematic elements. NO \\ \hline final score: 0.375. \\ \hline output: NO \\ \hline \end{tabular}
\end{table}
Table 1: Output structure of Cave, Prompt-CAVE: The text (JSON keys) highlighted in blue depict the linguistic features used in the analysis, as well as the keys for final confidence score and predicted task output. The text underlined and highlighted in yellow depict the intermediate labels with respect to each linguistic feature, the confidence score value, and the predicted task label. We use the predicted label to measure task accuracy and all of them to measure rationale-label consistency.

 

well as high quality of rationales.

## 2 Our Method

To enable and improve downstream utility of authorship verification systems, we present **Cave**, our trained, _offline_ model that generates accessible free-text rationales (well-structured, and uniform structure across all datapoints). We further propose a novel metric **Cons-R-L** that leverages the structure of Cave's rationales to measure consistency between a rationale and its task (Av) label. Figure 2 demonstrated the visual pipeline to train Cave.

**Silver-standard data.** There exists no gold-standard, human-written datasets for AV _explanations_; hence, the first step to develop an Av model that can generate explanations is to create _silver-standard_ data for it. We propose our zero-shot prompt, **Prompt-CAVE**, that can be used with LLMs such as GPT-4-Turbo to generate silver training data. We adapt and extend PromptrAV Hung et al. (2023) to engineer Prompt-CAVE. We then use this silver data to train an in-house language model **Cave** to generate AV explanations.

**Prompt-CAVE and PromptsV.** Prior work PromptAV proposes a zero/few-shot prompt to generate free-text rationales that contain linguistic features relevant for \({\rm A}{\rm V}^{3}\). To our best knowledge, PromptAV was the first work in contemporary literature to generate natural language Av explanations (the second and latest work is Lip Huang et al. (2024b), which uses a different compilation of linguistic features)4. While PromptAV leads to high Av accuracies, its rationales are (1) inaccessible towards automatic evaluation5, and (2) potentially _inconsistent_ with their final label (detailed discussion and example in Appendix C), both of which makes the rationales unusable. In this work, we propose Prompt-CAVE, an extension of PromptAV that addresses these issues: (1) Prompt-CAVE generates rationales that are uniformly structured across datapoints; it generates a sub-rationale and label for each linguistic feature of analysis, under a fixed JSON format. This ensures ease of understanding, and enables any automatic quality measurement. (2) The aforementioned intermediate labels, along with the final score and the final label (also prompted for by Prompt-CAVE) allow for an _automatic consistency evaluation_ (described below), which leads to higher transparency and awareness.Table 10 shows Prompt-CAVE.

Footnote 4: We discuss a contemporaneous work Hu et al. (2019) in Appendix B.1

**Output structure (Table 1).** We define the _correct_ and _complete_ output structure of Cave/Prompt-CAVE to be a JSON structure where the linguistic features are keys; the model has to discuss the authorship of the texts grounded to each feature independently, via corresponding explanations and intermediate labels (YES/NO/MAYBE). The model also has to provide a score \(\in[0,1]\) that indicates its overall confidence in the shared authorship of the documents, and an overall predicted task label (YES/NO).

**Cons-R-L, automatic consistency metric.** Rationales that are inconsistent with the model's predicted label are risky and unusable in downstream applications Lyu et al. (2023). Prior works in self-rationalization check for consistency either by human analysis Chen et al. (2023b) or by training models Hase et al. (2020); Chen et al. (2023a); Wiegreffe et al. (2021) for the same. In our output format, the presence of intermediate labels allows the automatic verification of rationale-label consistency. We define a consistency metric **Cons-R-L** using two binary variables Cs-1 and Cs-2: Cons-R-L  (Cs-1  \(\wedge\) Cs-2).

**Cs-1:** Is the final score (which indicates the model's confidence in shared authorship) consistent with the final label? If the final label is 'YES', is

Figure 2: Pipeline to train Cave: We obtain silver train data from GPT-4-Turbo using Prompt-CAVE, filter it according to Cons-R-L and our output format. We then supervised-finetune a LLaMa-3-8B with the filtered data.

 

the final score \(\geq 0.5\)? Similarly, if the final label is 'NO', is the final score \(\leq 0.5\)?

**Cs-2**: Are the intermediate labels faithful (overall) to the final label? Since the model output is structured as a JSON, we can automatically extract the intermediate labels for each feature and count them. If the final label is 'YES' and count(YES) + count (MAYBE) > count(NO), then the labels are consistent. Similarly, if the final label is 'NO' and count(NO) + count(MAYBE) > count(YES) then the labels are consistent6.

Footnote 6: at this point, we assume all linguistic features have equal weightage towards shared authorship.

We define that is, a given output is consistent (Cons-R-L = 1) only if the final score, final label and the aggregate of the intermediate labels all align with each other.

**Training Cave.** We generate silver training data (\(D_{train}\)) from GPT-4-Turbo (\(T\)) using Prompt-CAVE (\(P\)).

\[D_{train}\text{ = }\{(x_{i},y^{t}_{i})\}\sim T(x_{i}|P) \tag{1}\]

where \(x_{i}\) represents the input document pairs and \(y^{t}_{i}\) represents the outputs (rationale + label) created from \(T\). We then filter \(D_{train}\) according to rationale-label consistency (Cons-R-L) and conformity to our desired structure, to get our final train set \(F_{train}\). Finally, we supervised fine-tune a smaller, offline language model (\(S\)) with \(F_{train}\); for a decoder-only model like LLaMa-3-8B, we train the model only on the output tokens.

\[E_{(x,y^{t})\text{-}F_{train}}S(y^{t}|x) \tag{2}\]

## 3 Experiments and Results

### Datasets, Training, Inference

We conduct experiments on three popular Av datasets: IMDb62 Seroussi et al. (2014), BlogAuth Schler et al. (2006) and FanFiection via the PAN-2020 Authorship Verification). Our datasets are diverse in domain (IMdb62: movie reviews, Blog-Auth: website blogs, FanFiection: movie/book fanfiction); we carefully select these datasets to test the generalizability of our method. For IMDb62 we use the test set of 1k text pairs from Hung et al. (2023). For Blog-Auth, we create our own test set by sampling 1k document pairs from the original blog authorship corpus. For FanFiection, we sample 1k document pairs from the PAN-2020 test set (which had 14k pairs). Our test sets have equal split of YES/NO labels.

**Creating the train set.** For IMDb62 and Blog-Auth, the original datasets consist of documents and their anonymous author IDs; we sample document pairs with equal amounts of same/different authors to create trainsets. For FanFiection, we take the 'small' train set provided by PAN-2020, and sample data from the same. We start off with a train set of ~1.8k documents pairs for each dataset7 and combine them with Prompt-CAVE and get silver rationale data from GPT-4-Turbo (temperature \(0.0\) and \(2\) responses per sample). Finally, we filter them according to the criteria in SS2 and use them for training. Appendix D details the data statistics after each stage of filtering.

Footnote 7: we ensure that there is no leakage between train/test datasets.

**Training and Inference.** We supervised, finetune a LLaMa-3-8B with LoRA Hu et al. (2021). We report all hyperparameters in Appendix E. We use greedy decoding during inference.

### Baselines

We present and compare with multiple baselines:

**Traditional stylometry.** We present a weak baseline Ikae (2021) from traditional stylometry. We represent input document pairs as the concatenation of their TF-IDF or word-count 8 features, and train a Support Vector Machine Cortes and Vapnik (1995) to classify their shared authorship status (hyperparameters in Appendix E).

Footnote 8: sklearn.feature_extraction.text TfidVectorizer, CountVectorizer

**Zero-shot, prior work.** We present three zero-shot baselines with GPT-4-Turbo (temperature \(0.0\)) and LLaMa-3-8B-Instruct (greedy decoding). The first baseline is chain-of-thought CoT Wei et al. (2022), a standard self-rationalization method that does not include any AV specific instructions. The second baseline is PromptAV Hung et al. (2023). We use the prompts for CoT and PromptAV reported in Hung et al. (2023) (Appendix C). Both these baselines report confidence scores (0-1, higher score means more confidence in shared authorship) in lieu of labels. We extract this confidence score by string matching for decimal numbers in the output; we measure accuracy by thresholding these scores at \(0.5\). _Consistency cannot be calculated for these two baselines_. The third zero-shot baseline is Prompt-CAVE itself.

**Fine-tuned.** Similar to how we train Cave, we generate silver CoT and PromptAV data from GPT-4-Turbo, filter them according to task cor 

rectness and train a LLaMa-3-8B with them. We report final train set sizes in Appendix E.

**Few-shot.** Finally, we present a few-shot baseline with Prompt-CAVE on GPT-4-Turbo and LLaMa-3-8B-Instruct. This baseline is strong, but undesirable owing to the high expenses of performing few-shot Av9. We have submitted the few shot prompts in supplementary material.

Footnote 9: Each document in our datasets has _on-average_ 1000 tokens; performing even a 4-shot baseline is highly expensive.

### Empirical Results

Table 2 shows the empirical results. We report accuracy (Acc.) for all models, and consistency (Cons-R-L) for Cave and Prompt-CAVE. For all 3 datasets, Cave beats all relevant baselines in terms of rationale-label consistency. For FanFiction, Cave beats all baselines (including few-shot GPT-4-Turbo by 0.9%) in accuracy; for IMDb62 and Blog-Auth, Cave obtains competitive accuracies (2% and 4% less than the highest accuracy, which were PromptAV-SFT and CoST respectively).

**Why show two zero/few-shot baselines?** In addition to the strong (online) baseline of GPT-4-Turbo, we also show zero/few-shot results using a less-strong local model, LLaMa-3-8B-Instruct. The significant performance degradation there (for all cases except few-shot Blog-Auth) shows that it is insufficient merely to take existing approaches and run them using a local instruction-tuned model; our proposed distillation strategies are essential to get a high performance with local, offline models that face the disadvantage of having a much lower parameter size.

**Prompt-CAVE versus PromptAV, CoT.** We investigate the drop in accuracy as we go from CoT/PromptAV to Prompt-CAVE in the zero-shot baseline GPT-4-Turbo. We observe that a majority of this drop is caused by instruction following errors, such as not generating in a JSON format, or generating a 'MAYBE' as the final label instead of 'YES' or 'NO'. GPT-4-Turbo might still does produce the right score in the prediction (which is the indicator for the predicted label used by PromptAV and CoT), but since our evaluation requires the JSON format and a clear YES/NO label at the end, GPT-4-Turbo's performance decreases with Prompt-CAVE. For IMDb62, Blog-Auth and FanFiction respectively, the number of test datapoints (out of 1000) where GPT-4-Turbo doesn't follow the output format with zero-shot Prompt-CAVE are 47, 153 and 148 respectively; the number of datapoints where it produces a label of 'MAYBE' are 59, 45 and 103 respectively. However, as we move to _few-shot_ Prompt-CAVE or _fine-tuned_ Cave, we find that instruction-following errors are almost non-existent, and there is a huge improvement in accuracy as well as consistency! Appendix F further discusses practical advantages of Cave over GPT-4-Turbo, as well as experiments with more local models.

\begin{table}
\begin{tabular}{c c l r r r r r r} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{l}{**Test \(\rightarrow\)**} & \multicolumn{2}{l}{**IMD62**} & \multicolumn{2}{l}{**Blog-Auth**} & \multicolumn{2}{l}{**FanFiction**} \\ \cline{3-8}  & & **Train \(\downarrow\)** & Acc. & Cons-R-L & Acc. & Cons-R-L & Acc. & Cons-R-L \\ \hline SVM & - & Fine-tune & 59.7 & - & 56.2 & - & 55.7 & - \\ \hline \multirow{3}{*}{GPT-4-Turbo} & CoT & Zero-shot & 73.6 & - & 62.0 & - & 54.2 & - \\  & PromptAV & Zero-shot & 73.0 & - & 62.2 & - & 57.0 & - \\   [>>> 8B] & Prompt-CAVE & Zero-shot & 67.6 & 0.86 & 57.7 & 0.73 & 52.2 & 0.67 \\  & Prompt-CAVE & Few-shot & 71.3 & 0.94 & 64.0 & 0.97 & 61.7 & 0.86 \\ \hline \multirow{3}{*}{LLaMa-3-8B} & CoT & Zero-shot & 62.4 & - & 60.7 & - & 57.5 & - \\  & PromptAV & Zero-shot & 62.8 & - & 57.9 & - & 53.8 & - \\ -Instruct & Prompt-CAVE & Zero-shot & 36.7 & 0.16 & 38.8 & 0.25 & 45.5 & 0.17 \\  & Prompt-CAVE & Few-shot & 53.8 & 0.98 & 62.3 & 0.99 & 48.1 & 0.93 \\ \hline \multirow{3}{*}{LLaMa-3-8B} & CoT & Fine-tune & 65.7 & - & 64.6\({}^{\star}\) & - & 60.5 & - \\  & PromptAV & Fine-tune & 76.2\({}^{\star}\) & - & 61.3 & - & 58.7 & - \\ \cline{1-1} \cline{2-8}  & Cave & **Fine-tune** & **74.1** & **0.99\({}^{\star}\)** & **60.6** & **1.00\({}^{\star}\)** & **62.6\({}^{\star}\)** & **0.99\({}^{\star}\)** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Empirical results comparing baselines and Cave. We note that fine-tuned Cave is competitive with strong baselines such as zero-shot and few-shot GPT-4-Turbo, while having higher rationale quality (via consistency).

 



### In-house human study:

Av + explanations is a highly complex task; annotators require a considerable amount of training and practice to be skillful in their evaluation. Hence, for our evaluation, we opted to conduct a _small but high-quality_**in-house human study**, instead of a large-scale study with platforms such as Amazon MTurk.

We pick \(50\) document pairs per dataset (total of \(150\) pairs) and evaluate the rationales generated by their corresponding Cave. We analyze the strengths & weaknesses of Cave's rationales, which help to understand the utility of these rationales to downstream applications Joshi et al. (2023) and to learn about potential areas for improvement van der Lee et al. (2019). We analyze the generated rationales (for each linguistic feature as in Table 1) via three properties as described below; we employ 3 distinct annotators per datapoint. We also require the annotators to comment on why a rationale is unsatisfactory, if they find it to be so with respect to any property; this helps to perform a more detailed analysis at the instance level.

**1. Detail-Consistency:** Are the details mentioned in the rationale consistent with the input documents, or are they hallucinated? (eg:- if the rationale mentions that both documents use parantheses, but the documents don't, then the details are _hallucinated_)

**2. Factual-Correctness**: Are the rationales factually correct? (eg:- if the rationale says that the text was informal in tone, while it was actually formal, or if it misinterprets an acronym to be authorspecific slang, it is incorrect)

**3. Label-Consistency**Cave's output format has a distinct rationale and intermediate label (YES/NO/MAYBE) for each linguistic feature. As defined in SS2, we use these intermediate labels to check the overall consistency of the rationale with the final label. In this human evaluation however, we check whether each individual rationale is consistent with its corresponding label.

In Table 3, we show number of examples (out of 150) for which the three annotators agree that the rationale aligns with the property being analyzed. We also show aggregate statistics for each property across all linguistic features, i.e., \(\frac{sum(P1)}{150*8}\) where 8 is the number of linguistic features being analyzed.

**Aggregate Analysis.** Annotators find that Cave's rationales align with properties P1, P2, P3 **77.8%**, **84.6%** and **90.4%** of the cases respectively. That is, Cave hallucinates, presents incorrect details and produces inconsistent intermediate labels in 22.2%, 15.4% and 9.6% of the cases respectively.

* it's instead of it's), leading to Cave reasoning that these double quotes were indicative of shared authorship. Similarly, some documents in the Blog-Auth dataset used the term 'urlLink' to denote the presence of hyperlinks (perhaps a formatting pattern used by the blog platform)
- Cave however, took the presence of this unique term as an indicator of shared authorship. Since annotators view many datapoints in their analysis, they are able to identify these spurious patterns; models such as Cave and GPT-4-Turbo view these datapoints in isolation and hence do not dismiss these patterns as indicators of shared authorship.

**Qualitative examples & Annotator comments.** We report commonly observed rationale errors in Table 4. Hallucination of details was a common error, especially for punctuation style and special characters / capitalization; for example, Cave mentioning the presence of punctuation marks such as ellipses, parantheses, etc., when those did not exist in the document. One hypothesis is that LLMs are bad at these linguistic features since they deal with individual characters and not broad pieces of text like the other features. Another common error was the Cave's usage of topic of texts as a reason to indicate or refute shared authorship of two documents; topic cannot be an absolute factor

\begin{table}
\begin{tabular}{l c l l} \hline \hline
* ‘Detail-Consistency’, P2
- ’Factual-Correctness’, P3
- ‘Label-Consistency’. We report these numbers for all linguistic features used by Cave.**


in deciding authorship (Wegmann et al., 2022), and hence such labels must be 'MAYBE'. We also discuss random errors owing to computational limitations (such as limited context window in LMs) in Appendix H.

We report the human study numbers for individual datasets in Table 19. We also release the complete set of test-set rationales generated by Cave.

### Future steps

The first step towards handling these issues is to make downstream users aware about them, so that they can make an informed final decision with Cave's rationales; the independence between linguistic features make it easy for users to process them separately and use them as needed.

Ablation study with linguistic features.A straightforward solution is to simply identify the linguistic feature that is the most uncertain/problematic in the training data, and remove it from the datapoints: we perform this experiment as an ablation study. We identified from analyzing the silver data that 'punctuation style' has an intermediate label of 'MAYBE' in roughly 60-70% of the data for each dataset. We hypothesized that this feature was generally indecisive and unimportant; we removed it from the train data and fine-tuned a lLaMa-3-8B on the same - however, we found that the performance _reduced_ upon removal of this feature for all 3 datasets. This suggests that though this feature is generally indecisive, it does offer much needed information or context that the model depends upon; simply removing the feature will not improve the performance, and hence, we need more intricate methods such as weighing of linguistic features depending on the datapoint at hand.

Targeted training.As futute steps, we propose to perform targeted training that directly addresses the problems of (1) _hallucinated details_, and (2) _trivial reasonings from unimportant features_. For the hallucination issue, given that we have the input documents, we can potentially have a reward model that checks the entailment between the sub-explanations and the documents and verifies whether a detail is real or a hallucination; we can further perform reward-based learning with algorithms such as PPO (Schulman et al., 2017), Quark (Lu et al., 2022). For the latter issue, we can have _dynamic_ weighting of linguistic features for each example, which highlight only the features that contribute meaningful similarities or differences between the documents.

## 4 Discussion

### Robustness of Cs-1 and Cs-2

Cons-R-L, the automatic metric used to measure rationale-label consistency, is defined based on two binary variables (SS2): Cs-1 which measures con

\begin{table}
\begin{tabular}{|l|c|} \hline
* the label should have been MAYBE for both these cases. \\ \hline special characters style, capitalization style: Text1 shows a casual approach to capitalization, with instances of lowercase usage in contexts that typically require capitalization (e.g., ‘reilly instead of ‘really’). Text2 adheres to standard capitalization rules. & Confusion between capitalization and misspelling/typo. \\ \hline acronyms and abbreviations: Neither text makes significant use of acronyms or unusual abbreviations, maintaining a formal tone in dialogue without informal abbreviations. & Lack of acronyms does not indicate a formal tone – this rationale is even more incorrect when the original text is informal tone. \\ \hline acronyms and abbreviations: Neither text makes significant use of acronyms or abbreviations, which suggests a similarity in this aspect. However, the absence of such elements makes it difficult to draw a definitive conclusion. **TES** & In addition and factually incorrect: Both expressions were in Text-1, none in Text-2. \\ \hline special characters style, capitalization style: Both texts use colloquial expressions (‘buttoloud of dishes’, ’nagged the darn thing out of Shawn’). These expressions contribute to the informal tone of the texts. & Hallucination and factually incorrect: Both expressions were in Text-1, none in Text-2. \\ \hline \end{tabular}
\end{table}
Table 4: Sample rationales from various examples, to show observed error types by Cave
 

sistency between the final score and the final label, and Cs-2 which does the same between the final label and the aggregated intermediate labels. These variables were designed with common sense logic (such as, a final score \(\geq 0.5\) must indicate a final label of YES). But how robust are they statistically? We analyze the silver train data from GPT-4-Turbo with relevant questions below. We use the term _accepted_ for datapoints that satisfied the consistency condition being analyzed (Cs-1 or Cs-2), and _rejected_ for those that were filtered out. We split the analysis into 5 categories: accepted datapoints with a final label of YES/NO, and rejected datapoints with a final label of YES/NO/MAYBE (when the predicted label is 'MAYBE', it is always filtered out).

* these indicate that accepted datapoints are well away from the threshold boundary. For rejected data, GPT-4-Turbo on average assigns a score surrounding \(0.6\) for predicted labels of 'NO' and 'MAYBE' (there are no cases where the rejected data has a label of 'YES', i.e., where the label was 'YES' and the score < \(0.5\))
- this shows that rejected datapoints were _not_ rejected just because of a harsh thresholding10.

Footnote 10: harsh thresholding is when the final score is marginally crossing the boundary to be accepted, for example, a score of \(0.51\) for a label of ‘NO’ or \(0.49\) for a label of ’YSE’

* this indicates that the main support for the final labels of YES/NO come respectively from intermediate labels of YES/NO. For rejected data where the predicted label was NO, the aggregate intermediate labels came roughly to ~50% YES, indicating that the inconsistency was not due to indecision of the model with MAYBE's. For rejected data where the predicted label was MAVBEA, the aggregate intermediate labels are ~43-54% MAYBE, indicating a higher level of uncertainty in the model.

**Measuring uncertainty via score and labels:** As per our logic, a final score close to \(0.5\), and/or a high proportion of 'MAYBE' intermediate labels indicate a higher uncertainty of the model in its final prediction. To validate this hypothesis, we calculate the correlation11 between the fraction of MAYBE's in the intermediate labels, and 1 minus the absolute distance of the final score from \(0.5\)12. Formally,

Footnote 11: ffrom scipy.stats import pearsonr

Footnote 12: a lesser distance to 0.5 implies high uncertainty, so it is subtracted from 1 to make the final value higher if there is higher uncertainty

\[v_{1}=\frac{\text{count(MAYBE)}}{\text{num\_intermediate\_labels}}\]

\[v_{2}=1-|\text{final score}-0.5|\]

\[\text{correlation}=\text{PearsonCorr}(v_{1},v_{2})\]

We calculate this correla

\begin{table}
\begin{tabular}{|l|c|c|c|c|c|} \hline Predicted & \multicolumn{2}{c|}{**Accepted**} & \multicolumn{3}{c|}{**Rejected**} \\ Label \(\rightarrow\) & YES & NO & YES & NO & MAYBE \\ \cline{2-7} Dataset & \begin{tabular}{c} \% MAX: in \\ MAY+YES \\ \end{tabular} & \begin{tabular}{c} \% MAX: in \\ MAY+NO \\ \end{tabular} & \begin{tabular}{c} \%\% NA: in \\ MAY+NO \\ \end{tabular} & \begin{tabular}{c} \%\% NA: in \\ MAY+NO \\ \end{tabular} & \begin{tabular}{c} \%\% VS \\ \end{tabular} & 
\begin{tabular}{c} \%MAX: \\ MAY+NO \\ \end{tabular} \\ \hline IMDb62 & 15.7 & 38.8 & - & 51.2 & 45.7 \\ \hline Blood-AUTH & 22.1 & 24.6 & - & 50.0 & 54.3 \\ \hline FanFiction & 17.0 & 32.1 & - & 51.3 & 43.1 \\ \hline \end{tabular}
\end{table}
Table 6: Statistical analysis of Cs-2: % of intermediate labels

\begin{table}
\begin{tabular}{|l|c|c|c|c|c|} \hline  & \multicolumn{2}{c|}{**Accepted**} & \multicolumn{3}{c|}{**Rejected**} \\ \cline{2-5} Dataset & YES & NO & YES & NO & MAYBE \\ \hline IMDb62 & 0.89 & 0.33 & - & 0.63 & 0.65 \\ \hline Blog-Auth & 0.86 & 0.18 & - & 0.62 & 0.58 \\ \hline FanFiction & 0.87 & 0.30 & - & 0.63 & 0.62 \\ \hline \end{tabular}
\end{table}
Table 5: Statistical analysis of Cs-1: Average ‘final score’ generated by GPT-4-Turbo + Prompt-CAVE in the silver train data.

 

Turbo train data; we obtain IMDb62: 0.66, Blog-Auth: 0.8, FanFiction: 0.6, all with \(p~{}\texttt{<}~{}0.01\). The high correlation scores validate our hypothesis.

### Analyzing signal strengths across linguistic features

In Table 7, we present a detailed analysis of the intermediate label (MAYBE) signal strengths across all linguistic features, as measured from the training data of each dataset. Punctuation style sees a high proportion of MAYBE's (60-70%) for all datasets, whereas features such as writing style, tone & mood etc., have a very low proportion. On average across all linguistic features, the proportion of 'MAYBE' is 20-25%, reinforcing the conclusion from SS4.1 that the majority support for the predicted label comes from decisive intermediate labels of 'YES' and 'NO', and not from the uncertain 'MAYBE'.

### Cave versus GPT-4-Turbo

In Appendix D, we present analyses of the silver training data generated by GPT-4-Turbo as well as human evaluation results of the same. In addition, here we compare rationales generated by Cave and GPT-4-Turbo on the test set in the following three aspects:

Does Cave yield less MAVBE's than GPT-4-Turbo?In terms of the final output label, since Cave is fine-tuned on data that has only YES/NO labels as the final output, it always produces only YES/NO and never a MAYBE; this is not the case with GPT-4-Turbo since we zero/few-shot prompt it. In terms of the intermediate labels, we report the % of MAYBE's averaged across linguistic features for zero-shot GPT-4-Turbo and Cave's predictions on the test set in Table 8. The proportions are found to be similar, with zero-shot GPT-4-Turbo having slightly more MAYBE's as its intermediate labels than Cave.

Does Cave yield less rejected samples with respect to Cs-1/Cs-2 than GPT-4-Turbo?From Table 2, we see that Cave has a higher ConsR-L, which means that it yields less rejected samples than GPT-4-Turbo;in our individual Cs-1 and Cs-2 analysis, we find that Cave has higher values for both. We report values below for Cave and zero-shot GPT-4-Turbo on the test set below in Table 9.

## 5 Conclusion

Existing methods for authorship verification face a lack of _accessible explainability_. In this work, we train an in-house language model Cave to generates structured and consistent explanations for Av. We present strong experimental results on three difficult Av datasets; we also present a human pilot study on the quality of generated rationales. Finally, we provide detailed discussions on the robustness of our consistency metric, and explore errors made by Cave due to factors such as hallucination, dataset biases, etc. to guide research in this area. As future work, we would like to explore reward metrics and reward-based learning for Av explanations.

\begin{table}
\begin{tabular}{l|c|c|c} \hline
**Language Feature** & **IMDb62** & **Blog-Auth** & **FanFiction** \\ \hline punctuation style & 68.4 \% & 60.2 \% & 59.4 \% \\ \hline special char/ & & & \\ capitalization & 21.2 \% & 23.9 \% & 20.7 \% \\ \cline{2-4} acronyms \& abbr. & 26.6 \% & 44.5 \% & 29.1 \% \\ \cline{2-4} writing style & 6.5 \% & 5.9 \% & 7.1 \% \\ \hline expressions/idoms & 24.6 \% & 14.4 \% & 23.2 \% \\ \cline{2-4} tone \& mood & 5.1 \% & 7.0 \% & 7.2 \% \\ \cline{2-4} sentence structure & 28.0 \% & 9.8 \% & 18.8 \% \\ \cline{2-4} any other & 17.5 \% & 8.6 \% & 8.8 \% \\ \hline avg. across features & 24.74 \% & 21.79 \% & 21.79 \% \\ \hline \end{tabular}
\end{table}
Table 7: Proportion of datapoints in the train set for each linguistic feature where the intermediate label is ‘MAYBE’. Punctuation style sees a high proportion of ’MAYBE’ labels (60-70% across the datasets), whereas other features see much lower proportions.

\begin{table}
\begin{tabular}{l c c c} \hline
**Dataset** & **GPT-4-Turbo** & **Cave** \\ \hline IMDb62 & 29.22 \% & 23.71 \% \\ Blog-Auth & 25.23 \% & 23.46 \% \\ FanFiction & 26.64 \% & 20.34 \% \\ \hline \end{tabular}
\end{table}
Table 8: Proportion of intermediate labels MAYBE’s averaged across linguistic features for zero-shot GPT-4-Turbo and Cave’s predictions on the test set.

 



## Limitations

**Bias propagation from GPT-4-Turbo to Cave, and mitigation:** Since there is no human-written explanation data for Av, we used silver data generated by GPT-4-Turbo; however, this can lead to _biases13_ present in GPT-4-Turbo to unconsciously propagate to Cave. The datasets we use in this paper are not highly bias-prone; however, researchers/users who use our framework on their dataset might deal with biases that stem from their dataset (such as gender bias, racial bias, etc., for example: 'Document-1's writing style is depictive of demographic-X whereas Document-2's writing style is depictive of demographic-Y'). For users dealing with sensitive data for Av, we recommend they use bias mitigation strategies from relevant literature such as Counterfactual Role Reversal Gupta et al. (2022) for gender bias, reducing bias in the upstream model before fine-tuning (UBM Jin et al. (2021), strategic pretraining Feng et al. (2023)), ensembling models with varying/opposing biases (partisan ensemble Feng et al. (2023)), etc.

Footnote 13: We adopt the definition of bias from Jin et al. (2021); Blodgett et al. (2020): disparate model performance on different subsets of data which are associated with different demographic groups

**Hallucination.** As we see in our human study, Cave's rationales sometimes have _hallucinated_ details, especially for certain linguistic features. Hallucination is a common problem faced when using LMs Ji et al. (2023); Huang et al. (2023); prior works Zhang et al. (2023); Shi et al. (2024) have proposed task-specific strategies to alleviate the same. In our work, our proposed output format for Cave allows for downstream users to analyze each linguistic feature independently, making it easier to _identify_ hallucinations. We caution users to be aware of this issue, and to always manually read and verify the rationales before using them.

**Completeness of rationale.** We would like to emphasize here that our models have not been explicitly trained for _completeness_; that is, apart from any wrong information the generated explanation may have, it could also have missed some crucial similarity/dissimilarity between the documents. We discuss rationale properties such as completeness in Appendix K.

**Trust.**Lipton (2018) discusses that the definition of _trust_ in a model is subjective: some users only trust well-understood models; other users might be inclined to trust well-performing models regardless of how (not) explainable they are. A recent work Sun et al. (2024) defines various _dimensions_ for trust for language models, such as truthfulness, safety, robustness, etc. Our improvements in terms of accessibility of rationales and automatic quality evaluation contribute to improved trust in certain dimensions; as future work, we would like to evaluate and improve on other dimensions of trust, including higher accuracy, more datasets, more OOD evaluation, etc.

**Practical limitations.** Lastly our method has practical limitations faced by any NLP task; our models are restricted by the size of the context window allowed by the model (both input and output text) (also keeping in mind restrictions due to computational costs, financial costs and GPU size available). This can lead to data missed by the model in its reasoning.

Data.All the datasets that we use in our work are released publicly for usage and have been duly attributed to their original authors. Note that while we perform authorship verification, all the datasets we use have been _anonymized_ by their respective creators. We do not have access to or use the names of individual people.

### Reproducibility

For all our experiments, we report (1) the complete hyperparameter setting and any bounds explored (Appendix E) as well as the sizes and versions/pretrained-model links of all models used, (2) the time taken per experiment, and infrastructure used, (3) descriptions of datasets, and demonstrations used to sample rationales from GPT-4-Turbo. Further, we have released all our codes and our training/test data at github.com/INK-USC/Controllable-AV-Explanations.

Llama-3 Usage and License.Note that we were granted access to download and use LLAMA-3-8B from [https://huggingface.co/meta-llama/Meta-LLama-3-8B](https://huggingface.co/meta-llama/Meta-LLama-3-8B). While we train a local LLAMA-3-8B, we do not release the trained model; we submit only the script and data used to train the model. Also note that we adhere to the intended use case for LLAMA-3-8B, i.e., "Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks."



## Acknowledgements

This research is supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the HIATUS Program contract #2022-2207220006. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. We thank the human annotators for their help with the pilot study. Finally, we thank all our collaborators at USC INK Research Lab and ISI for their constructive feedback on this work.

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## Appendix A Prompt-CAVE

Table 10 shows the full Prompt-CAVE.

## Appendix B Related Work

### Contemporaneous work

InstructAVHu et al. (2022) is a contemporaneous work that also extends PromptAV to generate silver training data to fine-tune a smaller language model. InstructAV uses a _gold-label-aware 

zero-shot prompt to get silver data from ChatGPT, which are then filtered for quality (they perform consistency verifications based on identifying specific phrases in the generated rationale). In contrast, we prompt GPT-4-Turbo (without providing the gold label, in order to obtain unbiased rationales from GPT-4-Turbo) to generate explanations that are structured with respect to the various linguistic features, as well as intermediate labels (which allow for easy verification of consistency); we filter the silver data by checking of consistency between the different steps of the rationale and the final label. Our method Cave differs from InstructAV in the specific format of the output used, as well as the metrics we subsequently use to verify/ensure rationale quality - **in addition to creating accurate and high-quality models, we also have the goal of making the process of authorship verification more transparent and accessible for end-users**.

### Authorship Analysis

Classical AV systems use inter-textual distance to quantify the likelihood for two documents to share the same author [123, 85, 90]. These verification techniques rely on expert-derived hand-crafted features to define the distance metric. Since such stylometric methods are grounded in hand-written rules, they are inherently explainable. However, these methods are known to not scale well, especially in the scenario when for a given query document the correct target author needs to be mined from a large collection of candidate documents14.

Footnote 14: See Table 1 of [76] for a comparison between scores from neural transformer/convolutional models and classical _tf-idf_ technique.

Different from classical stylometric techniques, current AV systems typically employ neural architectures which perform better at scale [76, 84]. Although significant amount of work has been devoted to advancing neural AV architectures, most of the current techniques lack interpretablity. For example, a typical neural AV architecture comprises of a Siamese network [123], where each document is separately encoded into a vector and the model is trained to ensure that the document vectors are close if the they are written by the same author and far otherwise [76, 84]. In such a network, what each dimension of the document vector _means_ is completely opaque.

To bridge this dichotomy between usability and interpretibilty between classical stylometric techniques and neural AV systems, [11] proposed to use attention heat-maps. This technique enabled them to determine which tokens in the input text are attended to (and not attended

\begin{table}
\begin{tabular}{l} \hline \hline
**Prompt-CAVE:** \\ Task: On a scale of 0 to 1, with 0 indicating low confidence and 1 indicating high confidence, please provide a general assessment of the likelihood that Text 1 and Text 2 were written by the same author. Your answer should reflect a moderate level of strictness in scoring. Here are some relevant variables to this problem. \\
1. punctuation style (e.g. hypben, brackets, colon, comma, parenthesis, quotation mark) \\
2. special characters style, capitalization style (e.g. Continuous capitalization, capitalizing certain words) \\
3. acronyms and abbreviations(e.g. Usage of acronyms such as OMG, Abbreviations without punctuation marks such as Mr Rochester vs. Mr. Rochester,Unusual abbreviations such as def vs. definitely) \\
4. writing style \\
5. expressions and Idioms \\
6. tone and mood \\
7. sentence structure \\
8. any other relevant aspect \\ Provide the answer in a Python JSON format. Use the following keys for your dictionary: ‘punctuation style’, ‘special characters style, capitalization style’, ‘acronyms and abbreviations’, ’writing style’, ‘expressions and Idioms’, ‘tone and \\ mood’, ‘sentence structure’, ‘any other relevant aspect’, ‘final score’. Apart from the ‘final score’, everything else must \\ have a text value; also, the text should include a concluding YES/NO/MAYBE about whether the two texts are similar or \\ not with respect to the key at hand. Finally, provide an ‘output’ key in your dictionary, which says YES if the two texts are \\ written by the same author, and NO otherwise. \\ Text1: excellent until a completely unsatisfying ending This movie really got me hooked. The plot about... \\ Text2: A Nutshell Review: Oliver Twist “Please sir, may I have some more?” Ah, the immortal line from... \\ \hline \hline \end{tabular}
\end{table}
Table 10: Prompt-CAVE: We extend Hung et al. (2023) to improve controllability in terms of the structure/parsing of the output and automatic verification of the consistency between the rationale and the label. The highlighted part is our extension to PromptAV.

 

to) by the neural model when performing AV. Different from attention-heat maps, model-agnostic methods, such as LIME Ribeiro et al. (2016), have also been used to probe feature importance Sari et al. (2018). Such explanations are helpful, however, they are inherently local15 and thus cannot capture higher level abstractions which can be informative of authorship, for example _fluency_, _tone and mood_, etc.

Footnote 15: For example, heat-maps can only capture features at the token/span level.

Motivated by the recent advancements made possible using LLMs, Hung et al. (2023) and Huang et al. (2024) investigated whether LLMs can perform the task of authorship analysis. They found that, when prompted correctly, the LLMs can reliably determine whether or not two documents are written by the same author. LLMs have also been used to construct interpretable authorship style vectors Patel et al. (2023) and subsequently used to rewrite text from a source author in the writing style of a target author Patel et al. (2022). Similarly, LLMs have also been used by works Hu et al. (2024) to perform authorship _attribution_, where, given a text, the task is to identify _who_ wrote the text (i.e., which human or LLM from a predetermined set of authors Huang et al. (2024)).

Our work is the first attempt to distill free-text explanations for authorship verification from LLMs like GPT-4-Turbo into smaller LMs; we provide explicit focus to the joint explainability and security of Av systems via our model Cave.

### Self-rationalization

Explainability for neural network based models come in two formats: extractive rationales (includes pre-LLM era) and free-text rationales. Extractive rationales Li et al. (2016); Sundararajan et al. (2017); Lundberg and Lee (2017); Jin et al. (2019) involve analyzing the influence of input tokens on the predicted output via various methods such as gradient-based analysis of input tokens Sundararajan et al. (2017); Lundberg and Lee (2017), input perturbation Poerner et al. (2018); Kadar et al. (2017), attention heatmap analysis Pruthi et al. (2020); Stacey et al. (2022); Wiegreffe and Pinter (2019), and trained models for this purpose Lei et al. (2016); Chan et al. (2022); Jain et al. (2020); Situ et al. (2021); Liu et al. (2023). However, extractive rationales have limited applicability as discussed previously; hence we focus on free-text rationales.

The initial set of approaches for free-text rationales used gold standard human-written rationales to train rationale generation models Samburu et al. (2018); Narang et al. (2020); Wiegreffe et al. (2021). Following the advent of emergent self-rationalization in LLMs Wei et al. (2022), the research area moved to prompt large LMs with the help of curated templates with or without rationale demonstrations Wei et al. (2022); Kojima et al. (2023); Li et al. (2023); Jung et al. (2022); Lightman et al. (2023). Other approaches include leverage few-shot training approaches with a handful of gold rationales Marasovic et al. (2022); Chen et al. (2023), or generating silver standard training data from large LMs to distill small LMs to be better at the task or better rationalizers. Ramnath et al. (2023); Li et al. (2023); Chan et al. (2023); Wang et al. (2023); Saha et al. (2023); Hsieh et al. (2023).

## Appendix C PromptAV and CoT

Tables 11 and 12 show the prompts used to apply CoT and PromptAV respectively. The prompts are taken from Hung et al. (2023); further note that the prompt is uniform across datasets.

PromptAV (and by extension, CoT) which serve as the inspiration for Cave produce outputs which cannot be automatically checked for consistency; these prompts produces a free-text explanation followed by a final score which can be thresholded to produce a YES/NO Av label. As observed in the experiments of Hung et al. (2023), models tend to produce low confidence scores with PromptAV, which means that for better accuracy, the optimal threshold in their experiments with GPT-3.5-Turbo was found to be 0.2-0.3, which will most probably not be consistent with the free text rationale (that is, if the rationale says that it thinks it is not confident that the authors are the same, and the confidence is 0.25, the automatic label prediction will still assume that the predicted label is YES). Further, with just the score as the label, it is still possible that we misconstrue the label because the confidence score and the model's predicted label in the text are different, as shown in the examples in Tables 13 and 14.

 

GPT-4-Turbo's silver rationales. For the most part, GPT-4-Turbo generates high-quality rationales; the most frequent issue observed is _hallucination_ of details, especially punctuation marks. Out of the 60 samples analyzed (20 from each dataset), 10 samples showed hallucination in their rationale for punctuation style, 16 samples showed factual incorrectness across varying linguistic features, and 6 samples showed inconsistency of rationale with intermediate label in one linguistic feature (varying across samples) each.

## Appendix E Hyperparameters

We download the pre-trained LLaMa-3-8B from HuggingFace ([https://huggingface.co/meta-llama/Meta-llama-3-8B](https://huggingface.co/meta-llama/Meta-llama-3-8B)) and train it for all our experiments. We train it with LowRank Adaption LoRA; Hu et al. (2021), a parameter-efficient training method which offers lightweight training no additional inference latency, unlike other existing parameter-efficient methods Houlsby et al. (2019). We train LLaMa-3-8B for \(10\) epochs. We use no validation set, and instead use the last checkpoint obtained from training; since we have no gold-standard explanations, we opt against using the GPT-4-Turbo generated data as the validation set. We experiment with different values of 'r' and 'alpha' for LoRA (refer to [https://huggingface.co/docs/pft/main/en/conceptual_guides/lora](https://huggingface.co/docs/pft/main/en/conceptual_guides/lora) for their definitions). We use r=128 and alpha=256. Note that all our training experiments for LLaMa-3-8B take ~12 hours, and all our test set inferences take ~7-8 hours on a single NVIDIA Quadro RTX 8000 GPU. We report single-run results for all our experiments.

**Why not LLaMa-3-8B-Instruct for fine-tuning?** As explained in the documentation for LLaMa-3-8B-Instruct in huggingface [https://huggingface.co/meta-llama/Meta-llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-llama-3-8B-Instruct), "Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks". Hence, we provide zero-shot and few-shot baselines with LLaMa-3-8B-Instruct whereas we provide fine-tuned results on LLaMa-3-8B.

**Document truncation.** When we truncate documents for GPU memory considerations in the training of Cave, we always ensure that equal amounts of both documents are present in the input. Further,

\begin{table}
\begin{tabular}{l} \hline \hline
**CoT:** \\ Task: On a scale of 0 to 1, with 0 indicating low confidence and 1 indicating high confidence, please provide a general assessment of the likelihood that Text 1 and Text 2 were written by the same author. Your answer should reflect a moderate level of strictness in scoring. Here are some relevant variables to this problem: \\
1. punctuation style (e.g. hyphen, brackets, colon, comma, parenthesis, quotation mark) \\
2. special characters style, capitalization style (e.g. Continuous capitalization, capitalizing certain words) \\
3. acronyms and abbreviations(e.g. Usage of acronyms such as OMG, Abbreviations without punctuation marks such as Mr Rochester vs.Mr. Rochester,Unusual abbreviations such as def vs. definitely)] \\
4. writing style] \\
5. expressions and Idioms \\
6. tone and mood \\
7. sentence structure \\
8. any other relevant aspect \\ First step: Understand the problem, extracting relevant variables and devise a plan to solve the problem. Then, carry out the plan and solve the problem step by step. Finally, show the confidence score \\ Texk1: [T1] \\ Text2: [T2] \\ \hline \hline \end{tabular}
\end{table}
Table 11: CoT prompt for the task of Av, taken from Hung et al. (2023)

\begin{table}
\begin{tabular}{l} \hline \hline
**PromptAV:** \\ Task: On a scale of 0 to 1, with 0 indicating low confidence and 1 indicating high confidence, please provide a general assessment of the likelihood that Text 1 and Text 2 were written by the same author. Your answer should reflect a moderate level of strictness in scoring. Here are some relevant variables to this problem: \\
1. punctuation style (e.g. hyphen, brackets, colon, comma, parenthesis, quotation mark) \\
2. special characters style, capitalization style (e.g. Continuous capitalization, capitalizing certain words) \\
3. acronyms and abbreviations(e.g. Usage of acronyms such as OMG, Abbreviations without punctuation marks such as Mr Rochester vs.Mr. Rochester,Unusual abbreviations such as def vs. definitely)] \\
4. writing style] \\
5. expressions and Idioms \\
6. tone and mood \\
7. sentence structure \\
8. any other relevant aspect \\ First step: Understand the problem, extracting relevant variables and devise a plan to solve the problem. Then, carry out the plan and solve the problem step by step. Finally, show the confidence score \\ Texk1: [T1] \\ Text2: [T2] \\ \hline \hline \end{tabular}
\end{table}
Table 12: PromptAV prompt for the task of Av, taken from Hung et al. (2023) 

we always use the first chunk of the document as input and not a random chunk.

For our baseline SVM, we use TF-IDF feature vectors for Blog-Auth (words) and FanFiction (characters), and word-count features for IMDb62 (characters). All 3 datasets' models use the RBF kernel. IMDb62 and FanFiction use n-grams of minimum size 1 and maximum size 2, whereas Blog-Auth uses only n-grams os size 2. IMDb62 uses a C-value of 5.0. We used sklearn version 1.2.2 for this baseline.

For our baseline of LLaMa-3-8B finetuned with CoT and PromptAV, our final training dataset sizes are: for IMDb62, Blog-Auth and FanFiction, the CoT sizes - 2.7k, 2.9k, 2.4k and the PromptAV sizes - 2.6k, 3k, 2.6k.

## Appendix F Cave with different offline models, Practical advantages of Cave

We report experiments with local models of varying sizes in Table 20. After LLaMa-3-8B, Mistral-7B is the next best performing model.

Practical Advantages:Cave also demonstrates practical advantages over GPT-4-Turbo such as (1) Energy: LLaMa-3-8B is just 8B parameters, whereas GPT-4-Turbo is orders of magnitude bigger - Cave hence consumes much less energy per datapoint, and hence is more affordable and efficient, (2) Financial cost: Cave is also more financially affordable since it can be hosted on a local server; GPT-4-Turbo or any API based language model would cost much more since it charges for every input/output token, (3) Speed: On average, Cave takes 3 minutes per datapoint for inference, whereas GPT-4-Turbo takes 4.5 minutes.

### Human Pilot

Prior works in this field largely focus on task accuracy, with much lesser focus on explainability Patel et al. (2023) and by extension, human study of the explanations. In this work, we take the _first step towards human analysis of_Av _explanations_ by providing a pilot study on Cave's rationales.

## Appendix G Human Evaluation

Table 16 shows the instructions given to the annotators regarding the properties to be analyzed and Table 17 shows a sample human evaluation. We perform an in-house human evaluation, and annotators used were proficient in English and well aware of the high complexity of the task.

Table 19 shows the human pilot study numbers for individual datasets. The aggregate of these numbers were used for the overall analysis in Appendix

\begin{table}
\begin{tabular}{l} \hline \hline
**Sample CoT explanation that is inconsistent with label:** \\ \hline Based on the provided texts, I assess the likelihood that Text 1 and Text 2 were written by the same author as 0.7 (moderate confidence). \\ Here’s my reasoning: \\
1. Both texts exhibit a personal and introspective tone, which suggests that the author is comfortable sharing their thoughts and feelings. \\
2. The language and sentence structure in both texts are similar, with a mix of short and long sentences, and a conversational tone. \\
3. Both texts touch on themes of self-reflection, personal growth, and the struggle to overcome negative thoughts and emotions. \\
4. The author’s writing style is informal and conversational, with a focus on expressing their thoughts and feelings rather than presenting a polished or formal argument. \\ However, there are some differences that suggest the texts may not have been written by the same author: \\
1. The tone of Text 1 is more lighthearted and humorous, while Text 2 is more serious and introspective. \\
2. The language and vocabulary used in Text 2 are more formal and philosophical, with words like ‘suffering,’ \\ ‘unrealistic standards,’ and ’setbacks.’ \\
3. The structure and organization of the texts are different, with Text 1 consisting of short, disconnected paragraphs, and Text 2 featuring longer, more cohesive paragraphs. \\ Overall, while there are some similarities between the two texts, the differences in tone, language, and structure suggest that they may have been written by different authors. \\ \hline \hline \end{tabular}
\end{table}
Table 13: Sample output by CoT where the predicted label and the predicted confidence score are inconsistent with each other. The model predicts a high confidence score of 0.7 for same authorship (by usual standards of 0.5 as the threshold), but it predicts that the final answer is different authorship.

 



### Random Errors due to Computational Limitations

We acknowledge random errors (i.e., not systematic) that could have occurred in our results owing to limitations of language models and computational resources. Our method uses a language model to read two input documents and provide an output; however, in practice, language models have a fixed maximum length of input due to model limitations as well as GPU memory limitations. This means that if the two input documents together (along with the instructions) do not fit into the input, it becomes necessary to truncate them, which leads to loss of information of their latter parts. This issue is exacerbated when we move towards using in-house models on user servers (which typically have a shorter context window than API-based models like GPT-4-Turbo). In our experiments with LLaMa-3-8B, we used just 2 GPUs (to have a realistic user environment), which means that while training we had to restrict the input to be of maximum length \(1280\lx@math@degree\)6, which could have affected the training for the longer documents. Fur

\begin{table}
\begin{tabular}{l} \hline \hline
**Sample PromptAV explanation that is inconsistent with label:** \\ \hline To assess the likelihood that Text 1 and Text 2 were written by the same author, we will analyze the texts based on the provided variables: \\
1. **Punctuation Style**: \\ - Text 1 uses a variety of punctuation, including colons, commas, and quotation marks, with a distinctive style of spacing before and after punctuation marks (e.g., ‘this : THIS’). It also uses apostrophes in plural forms, which is unconventional (e.g., ’one’s’). \\ - Text 2 uses standard punctuation without the unconventional spacing or misuse of apostrophes seen in Text 1. \\
2. **Special Characters and Capitalization Style**: - Text 1 features continuous capitalization for emphasis (e.g., THIS IS BETTER THAN THE ORIGINAL’) and capitalizes certain words for emphasis. \\ - Text 2 does not exhibit continuous capitalization or the same pattern of capitalizing certain words for emphasis. \\
3. **Acronyms and Abbreviations**: \\ - Text 1 does not prominently feature acronyms or abbreviations. \\ - Text 2 also does not prominently feature acronyms or abbreviations, making this variable less useful for differentiation. \\
4. **Writing Style**: \\ - Text 1 has a detailed, narrative style, sharing personal experiences and opinions in depth. \\ - Text 2 has a more straightforward, opinion-based style without the narrative depth or personal anecdotes found in Text 1. \\
5. **Expressions and Idioms**: \\ - Text 1 uses expressions and a more vivid language (e.g., ‘take 15 showers afterwards’). \\ - Text 2 uses simpler language and fewer idiomatic expressions. \\
6. **Tone and Mood**: \\ - Text 1 has a passionate, intense tone, especially when discussing the movie’s impact and the author’s feelings. \\ - Text 2 has a lighter, more casual tone, even when expressing disappointment. \\
7. **Sentence Structure**: \\ - Text 1 features complex sentences with multiple clauses and detailed descriptions. \\ - Text 2 uses simpler, more straightforward sentence structures. \\
8. **Other Aspects**: \\ - The subject matter and interest areas differ significantly between the two texts, with Text 1 focusing on horror films and Text 2 on a Disney film. This could indicate different interests or purposes for writing, though it’s not definitive evidence of different authorship. \\ Given these observations, the differences in punctuation style, capitalization, writing style, tone, and sentence structure suggest that Text 1 and Text 2 were likely written by different authors. However, without more direct evidence (e.g., consistent unique spelling errors or very distinctive stylistic guirks that appear in both texts), absolute certainty is not possible. \\ Confidence Score: 0.8 \\ This score reflects a high level of confidence that the texts were written by different authors, based on the analysis of the provided variables, but it leaves room for the possibility of the same author adapting their style to different subjects or formats.” \\ \hline \hline \end{tabular}
\end{table}
Table 14: Sample output by PromptAV where the predicted label in the text and the predicted confidence score are inconsistent with each other. The model predicts a high confidence score of 0.8 (by usual standards of 0.5 as the threshold), but it predicts that the final answer is different authorship. Unless a human (or a trained model) can manually analyze the text, it is impossible to determine that the predicted label via the confidence score and the actual predicted label are different.



ther, even for generating the training data from GPT-4-Turbo for FanFiction, the excessive size of the documents made the GPT-4-Turbo generations economically infeasible; we cut our the input to to a maximum of 300 words per document, which could have created errors/incomplete rationales that eventually propagated to our downstream training of LLaMa-3-8B.

### Out-of-domain inference

Finally, we evaluate Cave models on the other datasets' test sets (refer Table 21); we notice robust out-of-domain performance especially by the IMDb62 and FanFiction models. This is a significant result, as it shows the generalizability of this approach to unseen Av datasets: although we use fine-tuning to improve performance over a zero-shot approach, it is not fine-tuning specifically to the domain that is most critical but rather fine-tuning to the structured format of Cave's output.

## Appendix J Post-hoc Interpretability

We finally clarify that Cave was designed to serve _post-hoc rationalization_, and not mechanistic (i.e., low-level mathematical) understanding of the language model's workings. We quote from Lipton (2018) to emphasize that Cave's improved post-hoc explainability leads to improved utility to (1) downstream users, and (2) researchers, who can leverage Cave's accessible outputs for detailed analyses, to address errors and develop improved models: "_while post-hoc explanations often do not elucidate precisely how a model works, they may nonetheless confer useful information for practitioners and end users of machine learning_".

## Appendix K More rationale properties

In addition to consistency, there are several properties necessary for high quality rationales Joshi et al. (2023); Ramnath et al. (2023). Some of these are easily verifiable by humans, even if there exist no prevalent automatic methods to measure them: for eg:- factual correctness, hallucination in the rationale, etc. that we measured in our human evaluation (SSF.1). There are also properties that are hard to verify even by human evaluators: for eg:-_completeness_ of the rationale, i.e., has the rationale missed any significant (dis)similarity between the documents?. In fact, completeness does not have a comprehensive definition even for simpler tasks like multi-choice QA Ramnath et al. (2023). In the future, we would like to work on automatic metrics and model training for these properties specifically for AV.

\begin{table}
\begin{tabular}{|p{59.7pt}|p{113.8pt}|p{113.8pt}|} \hline
**Human Annotation Instructions:** & **Options** \\ \hline Given two documents, the task is to say whether they are written by the same author (label: YES) or not (label: NO). The model produces a set of explanations (corresponding to different aspects like punctuation, writing style, etc.) for the same each with an intermediate YES/NO/MAYBE label, and finally a YES/NO output label for the task. \\ The human evaluation looks at the three properties described below, and gives a score of 1, 0.5, 0 or -1 for each of the individual explanations. The ”Sample human eval” section gives an example evaluation. If you give a 0.5 or a 0, please write a short explanation what was wrong with the rationale! \\ \hline \hline
* all details are consistent, some are hallucinated, 0
- all details are hallucinated,
- l don’t know \\ \hline factual correctness & Are the details actually correct? (for example, it mentions serious writing style when writing style is actually humorous) & 1
- all details are factual, 0.5
- some details are factual, 0
- no details are factual,
- I don’t know \\ \hline consistency with pre-dicted label & Is the statement faithful with the YES/NO/MAYBE at the end? & 1 – yes it is faithful, 0.5
- some details are faithful, 0
- no it is not faithful,
- Idon’t know \\ \hline \end{tabular}
\end{table}
Table 16: Instructions given to annotators for the human pilot study. Sample human evaluations are in Table 17.

\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline Dataset & Initial size & Filter for Structure & Filter for float score & Filter for Cons-R-L \\ \hline IMDb62 & 3400 & 3396 & 3255 & 2354 \\ Blog-Auth & 3600 & 3600 & 3146 & 2724 \\ FanFiction & 3600 & 3383 & 2887 & 2159 \\ \hline \end{tabular}
\end{table}
Table 15: Train dataset sizes at every stage of data generation and preprocessing. The ‘Initial size’ column shows the number of datapoints generated from GPT-4-Turbo, and each succeeding column shows the number of datapoints remaining after the different filtering methods (in order of application).

 



**Sample evaluation:**

Text1: One last update. I've decided to wear my khaki pants all day. I get off work at 4:30. I see no need to rush to the 5:05 train. So I'll take my time and go on the 5:35. I just blowdried may hair and I think it's going to be okay. I need to eat something. I've eaten my two favorite foods, of late, for breakfast: pop tarts and pears. I think it's going to be rice and peas for lunch.

Text2: Hey guys! I'd like to tell you all that RICE is organising an event, 'Where is the love'. Go to urlLink my site for more info, or go to the urLink RICE site ?It's going to be great! there will be around 800 people there, and this event will be about how the world's gone wrong with terrorism, war... etc. Date: 10 July 2004 Place: Willoughby Civic Centre Time: 7pm Ask Ben, Adro or me for more info. We'd like All of you to come! urlr_link

GOLD LABEL: NO

\begin{tabular}{|p{96.7pt}|p{96.7pt}|p{96.7pt}|p{96.7pt}|} \hline
**Rationale** & **P1** & **P2** & **P3** \\ \hline punctuation style: Both texts use a variety of punctuation marks such as periods, commas, and ellipses. However, Text 1 uses more complex structures like colons and parentheses, which are less prevalent in Text 2. & 0.5 (ellipses/parentheses/parallelness) & 0.5 (colons in both) & 1 \\ \cline{2-5} structures like colons and parentheses, which are less prevalent in Text 2. & MAYBE & & \\ \cline{2-5} special characters style, capitalization style: Text 1 shows a more natural capitalization style without unusual emphasis on continuous capitalization or certain words. Text 2 capitalizes ’RICE’ and uses all caps for ‘URL’, indicating a different approach. & 0.5 (url not capitalized in Text2) & 0.5 (RICE is an organization, it it not a reflection of the capitalization approach) & 1 \\ \cline{2-5} acronyms and abbreviations: Text 1 does not prominently feature acronyms or abbreviations, while Text 2 includes ’URL’ as an abbreviation. This suggests a difference in the use of abbreviations. Conclusion: & 1 & 1 & 1 \\ \cline{2-5}
**NO** & & & \\ \hline writing style: Text 1 has a more personal, reflective style, discussing personal routines and thoughts. Text 2 adopts a more informative and promotional tone, inviting people to an event. The styles are quite distinct. & 1 & 1 & 1 \\ \cline{2-5} expressions and idioms: Neither text heavily relies on idiomatic expressions, but the context in which they write differs significantly, with Text 1 focusing on personal narrative and Text 2 on event promotion. & 1 & 1 & 1 \\ \cline{2-5}
**NO** & & & \\ \cline{2-5} tone and mood: The tone of Text 1 is casual and somewhat contemplative, while Text 2 is more enthusiastic and outward-facing, aiming to promote an event. These differences suggest different authors. & 1 & 1 & 1 \\ \cline{2-5} sentence structure: Text 1 uses a mix of short and long sentences, with a more conversational flow. Text 2 also uses a mix but is more structured, likely due to its promotional nature. The sentence structures show some similarities but also differences in context and purpose. & 1 & 1 & 1 \\ \cline{2-5}
**MAYBE** & & & \\ \cline{2-5} any other relevant aspect: TThe subject matter of the two texts is vastly different, with Text 1 focusing on personal habits and reflections, and Text 2 on organizing an event and expressing concerns about global issues. This difference in content and focus further supports the likelihood of different authors. & 1 & 0.5 (differentence in content doesn’t mean differment authors) & 0 (should be MAYBE. In content is not difference in authorship) \\ \hline \end{tabular} Table 17: Sample human evaluation shown to human annotators



## Appendix L Analyzing cases where PromptAV succeeds but Cave fails

We consider the \(88\) samples of the Blog-Auth test set where PromptrAV succeeds but Cave fails. From qualitative analysis of a few samples, we find out the following (non-exhaustive) patterns:

* Cases where the gold label is 'YES', and both methods fail: Cave wrongly predicts a label of 'NO' with a final score of < \(0.5\). PromptrAV gives the wrong reasoning, but concludes by predicting a confidence score > \(0.5\) that the authors are _different_ which leads to a misinterpretation that PromptAV was right. We provide an example of this error by PromptrAV in Appendix C.
* Differences in perceiving patterns such as idioms, acronyms: Both methods generate similar explanations for abstract features such as writing style, tone & mood. But when it comes to token-level features such as acronyms, idioms, etc., the methods sometimes predict different subsets, which leads to differing final predictions. This comes under the issue of _completeness_ of rationale, which we discuss in the Limitations section and in Appendix K. In this particular situation, both models have provided different incomplete rationales, which eventually leads to different labels.

\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline Linguistic Features & \multicolumn{3}{c}{IMDb62} & \multicolumn{3}{c}{Blog-AutoH} & \multicolumn{3}{c}{FanFiction} \\ \cline{2-10}  & P1 & P2 & P3 & P1 & P2 & P3 & P1 & P2 & P3 \\ \hline punctuation style & 15 & 31 & 43 & 25 & 38 & 45 & 23 & 34 & 37 \\ \hline special characters/ capitalization style & 31 & 40 & 47 & 23 & 37 & 46 & 33 & 39 & 43 \\ \hline actronyms/abbr. & 38 & 41 & 47 & 25 & 37 & 45 & 47 & 46 & 43 \\ \hline writing style & 47 & 45 & 46 & 44 & 48 & 49 & 45 & 44 & 50 \\ \hline expressions/idioms & 44 & 48 & 49 & 40 & 43 & 45 & 46 & 46 & 44 \\ \hline tone and mood & 47 & 48 & 48 & 46 & 47 & 48 & 47 & 43 & 49 \\ \hline sentence structure & 45 & 48 & 46 & 42 & 45 & 48 & 39 & 40 & 47 \\ \hline any other & 48 & 43 & 38 & 46 & 38 & 37 & 48 & 46 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 19: **NEW Human Pilot for individual datasets:** This table shows the number of examples (out of 20) where all 3 annotators agree that the rationale (correcting to the linguistic feature) conforms to the property being analyzed. P1 is ‘Detail-Consistency’, P2 is ’Actual-Correctness’ and P3 is ‘Label-Consistency’.





\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{Model} & Test \(\rightarrow\) & \multicolumn{3}{c}{IMDb62} & \multicolumn{3}{c}{Blog-Auth} & \multicolumn{1}{c}{FanFiction} \\ \cline{2-7}  & Train \(\downarrow\) & Acc. & Cons. & Acc. & Cons. & Acc. & Cons. \\ \hline LLaMa-3-8B & Fine-tune & **74.1** & **0.99*** & **60.6** & **1.00*** & **62.6*** & **0.99*** \\ \hline Mistral-7B & Fine-tune & 73.7 & 0.99 & 59.2 & 0.99 & 57.7 & 0.98 \\ \hline Stable-LM-Zephyr-3B & Fine-tune & 50.8 & 0.71 & 47.7 & 0.81 & 42.0 & 0.68 \\ \hline \end{tabular}
\end{table}
Table 20: Empirical Results comparing Cave trained with local models of different sizes. Mistral-7B is the next best performing model after LLaMa-3-8B. Stable-LM-Zephyr-3B however has a lower performance.

\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Test \(\rightarrow\) & \multicolumn{3}{c}{IMDb62} & \multicolumn{3}{c}{Blog-Auth} & \multicolumn{1}{c}{FanFiction} \\ \hline Train \(\downarrow\) & Acc. & Cons. & Acc. & Cons. & Acc. & Cons. \\ \hline IMDb62 & — & — & 59.9 & 1.0 & 61.2 & 0.99 \\ Blog-Authi & 71.3 & 0.96 & — & — & 45.3 & 0.77 \\ FanFiction & 68.8 & 1.0 & 59.3 & 1.0 & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 21: Out of domain performance by Cave