Venue: NAACL 2024

Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias

 Anshuman Chhabra, Hadi Askari, Prasant Mohapatra

Department of Computer Science, University of California, Davis

{chhabbra,haskari,pmohapatra}@ucdavis.edu

###### Abstract

We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring _position bias_, which we propose as a general formulation of the more restrictive _lead bias_ phenomenon studied previously in the literature. Position bias captures the tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. Through numerous experiments on four diverse real-world datasets, we study position bias in multiple LLM models such as _GPT 3.5-Turbo_, _Llama-2_, and _Dolly-v2_, as well as state-of-the-art pretrained encoder-decoder abstractive summarization models such as _Pegasus_ and _BART_. Our findings lead to novel insights and discussion on performance and position bias of models for zero-shot summarization tasks.

## 1 Introduction

Deep learning based abstractive text summarization models and Large Language Models (LLMs) have shown remarkable progress in generating concise and coherent summaries from input articles that are comparable to human-written summaries Zhang et al. (2023). Building upon this research, we aim to quantitatively measure summarization performance of LLMs (and pretrained encoder-decoder models for reference) by proposing _position bias_, which is a novel and general formulation of the _lead bias_ phenomenon Liu and Lapata (2019).

Position bias refers to the tendency of models to prioritize information from certain parts of the source text, potentially overlooking crucial details in other parts of the input article. While position bias has been studied previously in the literature as _lead bias_, we posit that lead bias is a specific case of position bias. Most prior works in this domain aim to propose methods that either _incorporate_ or _alleviate_ lead bias in models for improved performance Xing et al. (2021); Zhu et al. (2021) without a thorough analysis of the problem itself. It is also important to note that a formal definition for lead bias is still currently lacking in related work.

In contrast to lead bias, position bias seeks to decipher if models are over-utilizing sentences from any section(s) of the input articles, instead of just the leading segment. Moreover, a model's output summary can only be considered positionally biased if it overwhelmingly uses sentences from section(s) of the input that the human-written (or _gold_) summaries do not use themselves. For instance, if gold summaries for a dataset are lead biased and the model generates lead biased summaries, this is desirable behavior and cannot constitute position bias. In this scenario, if the model were to generate _tail biased_ summaries, it would be regarded as position bias. An example of a _positionally biased_ summary is shown in Figure 1.

We show how position bias can be empirically estimated by generating a distributional mapping between summary sentences and the article sentences used to generate the summary. Then, position bias can be measured using a metric such as Wasserstein distance Vaserstein (1969) between the model generated summary distribution and the gold summary distribution. Position bias measurements augmented with traditional metrics such as ROUGE scores Lin (2004) can provide a more holistic eval

Figure 1: An example of _position bias_ where gold summary is _tail biased_ and model summary is _lead biased_.

 

uation of zero-shot summarization models.

In summary, we make the following contributions in this work:

* We generalize and formalize the notion of lead bias as _position bias_ in zero-shot abstractive summarization. Lead bias can then be understood as a specific case of position bias (Section 3.2).
* We show how position bias can be empirically estimated for a given zero-shot summarization model and hence, can be employed as a metric for summarization quality alongside traditional metrics such as ROUGE scores. We conduct extensive experiments to benchmark LLMs (_GPT 3.5-Turbo1_, _Llama2-13B-chal2_, and _Dolly-v2-7B_Conover et al., 2023)) and pretrained encoder-decoder models (_Pegasus_Zhang et al., 2020) and _BART_Lewis et al. (2020)) on 4 diverse datasets: _CNN/DM_See et al. (2017), _Reddit TL;DR_Kim et al. (2018), _News Summary_Ahmed et al. (2018), and _XSum_Narayan et al. (2018) (Section 4).

Footnote 1: [https://platform.openai.com/docs/models/gpt-3-5](https://platform.openai.com/docs/models/gpt-3-5)

Footnote 2: [https://huggingface.co/meta-llans/Llama-2-1lb-chat-hf](https://huggingface.co/meta-llans/Llama-2-1lb-chat-hf)

* Using our findings, we compile novel insights to aid practitioners in selecting the right model for their zero-shot summarization tasks. (Section 5)

## 2 Related Works

Related work has studied the more specific phenomenon of lead bias in summarization. Both Grenander et al. (2019) and Xing et al. (2021) propose approaches and architectural changes to models that can reduce lead bias in _extractive summarization_, where summary sentences are selected directly from the source text. In contrast, in our work we study position bias more generally in _abstractive summarization_. Interestingly, Zhu et al. (2021) seek to leverage lead biased pre-training to improve performance on news articles, which are known to be lead biased. Prior work has also analyzed LLMs for their performance as zero-shot abstractive summarizers (Retkowski, 2023). Goyal et al. (2022) study GPT-3 specifically in the context of news summarization and Zhang et al. (2023) benchmark the summarization performance of multiple LLMs on the _CNN/DM_ and _XSum_ datasets. Tam et al. (2023) study the factuality of summaries generated by LLMs and Shen et al. (2023) use GPT 3.5-Turbo for evaluating summaries generated by other models. In vision, model bias has been investigated for video summarization Chhabra et al. (2023b). Unlike our work, none of these have studied position bias of zero-shot summarization in LLMs.

## 3 Proposed Approach

### Zero-Shot Abstractive Summarization

A zero-shot abstractive text summarization model \(\mathcal{A}\) operates on the dataset tuple \(D=(X,G)\) where \(X\) is a set of articles and \(G\) are their corresponding reference _gold_ summaries, generally written by human annotators. Moreover, each article and its corresponding gold summary consists of a variable number of sentences. The model \(\mathcal{A}\) then takes in as input the set of articles in the set \(X\) and outputs a summary, i.e., \(\mathcal{A}(X)=S\) where \(S\) is the generated summary. Traditionally, the model is evaluated by comparing the generated summaries (\(S\)) with the gold summaries (\(G\)) using the ROUGE metric Lin (2004). We use \(R^{1}/R^{2}/R^{L}\) to denote average ROUGE-1, ROUGE-2, and ROUGE-L scores.

### Formulating and Estimating Position Bias

Let an article \(x\in X\) have \(|x|=N_{x}\) number of sentences. We also obtain the set of generated summaries as \(S=\mathcal{A}(X)\) where each \(s\in S\) has \(N_{s}\) number of sentences. Since we consider abstractive summarization3, let us also assume we have a mapping function \(\phi\) that takes in a summary sentence \(s_{i}\in s\) and maps it back to a sentence \(x_{j}\in x\) in the article that it was primarily derived from. Any similarity function can be employed as a useful approximation for such a mapping function \(\phi\).4

Footnote 3: In _extractive summarization_, there is an exact one-to-one mapping between summary and article sentences.

Most works on lead bias implicitly assert that lead bias exists if for most \(s_{i}\in s\), \(\phi(s_{i})\) maps to some \(x_{j}\) that lies between the first \((0,k^{\prime}]\) sentence positions of the article. Here \(k^{\prime}\ll N_{x}\) and can be a dataset specific parameter- for example, for the Lead-3 Liu and Lapata (2019) evaluation metric, \(k^{\prime}=3\). However, this does not seem to be a reasonable definition, especially when considering general position. For example, consider a model which tends to derive information for generating summaries by using only the last few sentences of the article. This _tail bias_ might also constitute undesirable behavior if the gold summaries are not tail biased themselves, but will not be accounted for in the lead bias paradigm. Hence, it is better to reason about position more generally.

Since articles can be of differing lengths, position becomes specific to an individual article.

 

To overcome this issue, we divide each article \(x\) into \(K\) segments of approximately equal length (refer to Appendix A for how to do this) where \(K\leq\min_{x\in X}|x|\). This results in each article having at least one sentence in a segment, and we now have a uniform way of measuring sentence position across articles irrespective of their length.

To quantify position bias, we first obtain which article sentences the summary sentences are derived from using \(\phi\), for both gold \(G\) and generated \(\mathcal{A}(X)\) summaries. Then, we can map these article sentences to article segments to obtain a general sense of position in the article. We now have a distributional mapping of summary sentences to article segments. Using Wasserstein distance (Vasergetien, 1969) between the \(G\) and \(\mathcal{A}(X)\)_positional distributions_ we can then measure position bias.

## 4 Results

For experiments, we consider the _CNN/DM_, _XSum_, _News_, and _Reddit_ datasets. All datasets are different, in terms of domain, inherent position biases, or article and gold summary length. We choose only instruction-tuned LLMs as they are more performant at summarization (Retkowski, 2023) and we cover different model sizes: GPT 3.5-T is large (175B params), Llama-2 is mid-size (13B params), and Dolly-v2 is small (7B params). We also consider SOTA pretrained encoder-decoder models such as Pegasus and BART, although these models are not performant unless fine-tuned (i.e. many-shot learning). All experiments are done on the test set of datasets (more details in Appendix F).

We first visualize the positional distributions generated using our mapping procedure for the gold summaries and model generated summaries in Figure 2 with \(K=10\). As can be seen, _CNN_ and _News_ contain lead biased gold summaries and _Reddit_ and _XSum_ are positionally uniformly distributed. It can also be seen that the LLMs tend to have low position bias on _CNN, News_, and _Reddit_ datasets. However, for _XSum_, which constitutes the _extreme summarization_ setting, LLMs as well as BART/Pegasus exhibit much more lead bias, which is absent from the gold summaries. In _XSum_, the articles are up to 286 sentences long, and summary lengths are required to be between 1-2 sentences long. This is the largest jump from article \(\rightarrow\) summary and might explain models' tendency to pick a single sentence from the leading segments of the article.

Figure 2: Visualizing positional distributions of gold and model generated summaries for all datasets. The more “different” these distributions are for a given dataset/model, the more _position biased_ the model is for that dataset.

 

Comparing even with _Reddit_ where article lengths are up to 23 sentences long and summaries are 1-17 sentences long, it is evident that _XSum_ poses a unique challenge for summarization models.

Next, in Figure 3, we measure model performance using the \(R^{1}\) score between gold and generated summaries (results for \(R^{2},R^{L}\) are provided in Appendix B and follow similar trends) and position bias using Wasserstein distance between the positional distributions of gold and generated summaries of Figure 2. As is evident, LLMs attain the highest ROUGE scores on all datasets, and tend to have very low position bias, with the exception of _XSum_. However, even for _XSum_, LLMs achieve excellent performance on ROUGE. BART/Pegasus tend to have low performance and BART is also heavily position biased across all datasets.

## 5 Discussion

**Insights on Model Performance and Biases.**

* _GPT 3.5-T consistently attains low position bias and high performance._ Generally, GPT 3.5-T should be the de-facto choice for users, as can be seen in Figure 3. It consistently obtains high ROUGE scores and low position bias values. However, the paid API and closed-source access might be unfavorable to some users. For open-source models, LLama-2 is the better choice compared to Dolly-v2, and at times obtains ROUGE scores higher than even GPT 3.5-T (for e.g. on _XSum_). In comparison, Dolly-v2 at times has arbitrary and unpredictable performance, such as its low ROUGE scores and large position bias on the _Reddit_ dataset, unlike the other LLMs on the same dataset.
* _LLMs might exhibit significant lead bias for extreme summarization._ LLMs exhibit strong lead bias in the extreme zero-shot summarization case (Figure 3, _XSum_). For users who wish to undertake a similar task (pick 1-2 sentence summaries from very lengthy articles), LLMs might tend to only select sentences/information from the beginning of the article. If this is undesirable, it would be recommended to instead collect gold summaries and finetune LLMs/models to counteract this.
* _Suitability of encoder-decoder models._ As zero-shot summarizers, pretrained encoder-decoder models like BART and Pegasus have high position bias and low performance. This likely stems from their need to be finetuned on article-gold summary tuples to achieve SOTA performance. However, we would like to caution users to ensure that there is no positional mismatch between the data they finetune on and their evaluation set.5 While obvious, not ensuring this can lead to low ROUGE scores and high position bias (we demonstrate this in Appendix C).

Footnote 5: For e.g. finentuning on large # of news articles collected over the internet to then summarize in a different domain.

**Choice of Mapping Function \(\phi\).** For experiments in the paper we use TF-IDF vector similarities as \(\phi\). In our preliminary experiments, we did not observe significant differences for other choices. We provide these additional results when the \(R^{1}\) score is used instead and compare with the original results (Appendix D). Future work can analyze this choice of \(\phi\) as well as effect of other values of \(K\).

**Correlation of Position Bias and ROUGE.** An

Figure 3: Measuring _performance_ (\(R^{1}\) score) and _position bias_ (Wasserstein distance between gold and generated summaries’ positional distributions). Lower Wasserstein distance values correspond to lower position bias.

 

interesting consequence of our ROUGE and position bias results on datasets shows that their correlation is highly data dependent. For e.g., for _XSum_ Spearman's correlation shows statistically significant high positive correlation (\(\approx 0.89\)) between Wasserstein distances and \(R^{2}\) scores across all models but for _Reddit_ there is significant negative correlation (\(\approx-0.89\)). See Appendix E for detailed results. Hence, ROUGE scores are not enough to assess position bias. This also makes intuitive sense as ROUGE simply measures n-gram overlap and cannot holistically evaluate models Cohan and Goharian (2016). In future work other evaluation metrics can be studied alongside position bias.

## 6 Conclusion

We analyze zero-shot abstractive summarization by LLMs via a novel formulation of position bias. Position bias measures the tendency of models to generate summaries which overtly and unfairly utilize certain portions of input text over others. Through extensive experiments on the _CNN/DM, XSum, Reddit, News_ datasets, as well as various models (GPT 3.5-T, Llama-2, Dolly-v2, Pegasus, BART), we obtain novel insights about model performance and position bias that contribute to a deeper understanding of the challenges and opportunities in leveraging LLMs for effective abstractive summarization.

### Limitations

Our work formulates the concept of position bias in abstractive summarization and analyzes it in LLMs (and other reference models) across four diverse datasets: _CNN/DM, XSum, Reddit,_ and _News Summary_. The main limitation is that position bias of LLMs needs to be evaluated on many more datasets, and on other diverse problem settings beyond the ones considered in our paper. Moreover, the source domain itself could be challenging (legal or medical documents) or the LLM might not have been trained with data from that domain. In such cases, the LLM might default to using certain sections of the input articles over others, resulting in position bias. Another limitation of our work has been the primary use of English language datasets, but it is important to benchmark LLM position bias using summarization datasets from other languages as well. Additionally, a limitation of studies on LLMs such as GPT 3.5-Turbo is that they are constantly being updated and improved, and some behaviors might change or become non-existent in future versions Chen et al. (2023); Leiter et al. (2024). This necessitates assessing model performance/biases over time. Finally, as preceding ML/AI models are usually designed to be task/domain-specific (e.g. for clustering), issues of bias, fairness, and robustness Chhabra et al. (2024); Li et al. (2023); Li et al. (2022); Liu et al. (2023) specific to these tasks have been naturally studied in the literature. In the same manner, despite their general nature, task-specific robustness/bias needs to be further explored for LLMs.6 In future work, we seek to alleviate these limitations.

Footnote 6: Note that independent to our work, Ravaut et al. (2023) obtain similar results as ours for position bias in LLM based summarization.

### Ethics Statement

Our work on position biases in LLMs is important for understanding how these models prioritize information, and whether or not they disproportionately emphasize specific sections of the source text when generating abstractive summaries in a zero-shot setting. As LLMs are further integrated in society and utilized in various application pipelines, it is crucial to understand their behavior in a transparent manner. Through this study, we wish to shed light on this issue and allow practitioners to understand undersirable model behavior with regards to the summarization task better. This work also enables users to understand scenarios in which these models will generate more reliable outputs, leading to safer outcomes in practice.

## Acknowledgments

This research was supported in part by NSF Grant ITE 2333736 and DARPA AIE Award HR0011-24-9-0370.

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## Appendix A Dividing Articles into \(K\) Segments of (Approximately) Equal Length

To overcome the issue of articles being of differing lengths, we need better mathematical structure for describing _position_ across articles in a dataset. For this, we wish to divide an article \(x\) into \(K\) segments of approximately equal length. To achieve this, the \(j\)-th segment will contain the sentences of the article that lie in the interval:

\[[(j-1)\cdot c+\min(j-1,d),j\cdot c+\min(j,d)-1],\] where \(K\leq\min_{x\in X}|x|\), \(c=\left\lfloor\frac{N_{x}}{K}\right\rfloor\), and \(d=N_{x}~{}\mod K\).

The aim is to distribute the content of the article into \(K\) segments in a way that makes the lengths of these segments as equal as possible. Here, the inequality \(K\leq\min_{x\in X}|x|\) ensures that the number of desired segments \(K\) should not exceed the length of the shortest article in the set of articles \(X\) (otherwise it will lead to empty segments for those articles). The content of each segment \(j\) (note, \(j\) represents the index of the segment from 1 to \(K\)) is determined by an interval defined by: \((j-1)\cdot c+\min(j-1,d)\) (lower bound) and \(j\cdot c+\min(j,d)-1\) (upper bound).

Intuitively, \(c=\left\lfloor\frac{N_{x}}{K}\right\rfloor\) calculates the approximate length of each segment as it divides the total number of sentences in the article (\(N_{x}\)) by the desired number of segments (\(K\)) and rounds down to the nearest whole number. However, \(N_{x}\) might not be fully divisible by \(K\) and hence, we might have remainder \(d=N_{x}~{}\bmod K\). Hence, \(d\) accounts for any additional content that cannot be evenly distributed among the segments and ensures that segments accommodate the variation in article lengths. In this manner, the terms \(\min(j-1,d)\) and \(\min(j,d)\) in the lower and upper bounds of the interval are used to account for potential variations in segment length due to the remainder \(d\).

## Appendix B Additional Results for Other ROUGE Metrics

In the main paper in Figure 3 we provided results for the ROUGE-1 (\(R^{1}\)) score. Here, we provide additional results for the ROUGE-2 (\(R^{2}\)) and ROUGE-L (\(R^{L}\)) scores measured between the gold and model generated summaries as Figure 4. It can be seen that the trends are similar to \(R^{1}\) and LLMs exhibit stellar performance for \(R^{2}\) and \(R^{L}\) across all datasets.

## Appendix C Additional Position Bias Results for Finetuning BART and Pegasus

We go beyond the zero-shot setting to provide additional results on measuring position bias when BART and Pegasus are finetuned on the datasets we consider. The training was carried out on one _NVIDIA-A100_ with 50 GB memory. We use the HuggingFace Seq2Seq Trainer Class with a batch size of 64, gradient checkpointing of 4 and gradient accumulation. We use mixed-precision training for all models. The learning rate for all models was set to _5.6e-5_. While generating summaries during finetuning we use a single beam and maximum generation length of 128.

We finetune on the training set of each of the 4 datasets and evaluate on all datasets (for reference we again provide the no-finetuning / zero-shot results of Figure 2). Results for the obtained positional distributions are shown in Figure 5. It is evident that if there is mismatch in the finetuning and evaluation datasets for pretrained encoder-decoder

Figure 4: Additional results for \(R^{2}\) and \(R^{L}\) metrics.

 

models, they exhibit high position bias, leading to biased summarization. Hence, it is important for practitioners to collect article-summary data for finetuning that exactly reflects their evaluation or production use-case.

## Appendix D Additional Results for Different \(\phi\)

For experiments in the main paper, we opt for TF-IDF vector similarities as the choice of the mapping function \(\phi\) due to computational efficiency (over computing individual ROUGE scores between summary and article sentences for e.g.). However, it is important to examine whether this choice significantly impacts results, trends, and our findings. In initial experiments with different \(\phi\) we concluded that this choice does not affect results. In Figure 6 we provide results that support this by using \(R^{1}\) (ROUGE-1) as the metric for \(\phi\) on the _Reddit_ and _News_ datasets for Llama-2 generated summaries. We compare the gold summary and generated summary positional distributions for both datasets when \(\phi\) is computed using TF-IDF vectors and \(R^{1}\). It is clear that the trends and results are the same for both \(\phi\). Even the Wasserstein distance values computed between gold and generated summaries do not change much. For e.g. on _Reddit_: for TD-IDF the distance value is 0.044 and for \(R^{1}\) it is 0.046. Despite no significant differences, we believe future work can explore the choice of \(\phi\) more deeply.

## Appendix E Additional Results for Measuring Correlation Between ROUGE and Position Bias

In this section, we measure the correlation between ROUGE scores Lin (2004) and Wasserstein distance computed between the gold summary and model generated summary distributions. We conduct this experiment using Spearman's correlation coefficient statistic over all models and for each dataset. We utilize the \(R^{1},R^{2},R^{L}\) ROUGE metrics individually for this analysis, and the results are shown in Table 1. We find that correlation is highly

Figure 5: Visualizing positional distributions of gold and Pegasus/BART generated summaries for all datasets with and without finetuning on a particular dataset (training set). For the finetuned models, the diagonal subfigures are the ones that have the same finetuning and evaluation datasets and have low position bias. All other subfigures have a _mismatch_ between finetuning and evaluation datasets, and exhibit high levels of position biases. That is, the model generated summary positional distribution is very different from the gold summary positional distribution. The no-finetuning results were also shown in Figure 2 and are provided again for reference.

Figure 6: Results on _News_ and _Reddit_ for Llama-2 when \(\phi\) is either TF-IDF similarity or ROUGE-1.

 

dependent on the dataset: for _CNN_ the correlation is not strong and the results are not statistically significant, for _XSum_ ROUGE and position bias are positively correlated and statistically significant, and for _News_ and _Reddit_ results are statistically significant but highly negatively correlated. This indicates that ROUGE itself is not enough to assess position bias and hence, independent position bias measurement is important for holistic summarization evaluation.

## Appendix F Dataset, Model, and Training Details

### Detailed Dataset Information

_XSum_(Narayan et al., 2018): The _XSum_ dataset contains over 200K short, one-sentence news summaries answering the question "What is the article about?" and was collected by harvesting online articles from the British Broadcasting Corporation (BBC). The testing set consists of 11334 articles. The average number of sentences in the articles are 19.105. The total number of sentences in the summaries are 11334, leading to an average of 1 sentence per summary.

_CNN/DM_(See et al., 2017): The _CNN/DM_ dataset contains 300K unique news articles as written by journalists at CNN and the Daily Mail and is one of the most popular datasets for abstractive/extractive summarization and abstractive question answering. The testing set consists of 11490 articles. The average number of sentences in the articles was 33.37. The total number of sentences in the summaries was 43560 (an average of 3.79 sentences per summary).

_Reddit TL;DR_(Kim et al., 2018): The _Reddit_ dataset consists of 120K posts from the online discussion forum Reddit. The authors used these informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. We used an 80-20% train-test split to obtain 4214 articles in the test set. The average number of sentences per article was 22.019. The total number of sentences in the summaries was 6016 which leads to an average of 1.4276 sentences per summary.

_News Summary_(Ahmed et al., 2018): The _News_ dataset was initially created for fake news classification. We used the testing set comprising of 1000 articles. The number of sentences in the summaries are 1012 (an average of 1.012 per summary)

### Models

_Pegasus_(Zhang et al., 2020): The Pegasus model family is used mainly for text-summarization tasks. We use the _google/pegasus-large_ checkpoint7 from Huggingface as the summarization model.

Footnote 7: [https://huggingface.co/google/pegasus-large](https://huggingface.co/google/pegasus-large)

_BART_(Lewis et al., 2020): BART is a Seq2Seq encoder-decoder model for language tasks. We use the _facebook/bart-large_ checkpoint8 from Huggingface as the summarization model.

Footnote 8: [https://huggingface.co/facebook/bert-large](https://huggingface.co/facebook/bert-large)

_GPT 3.5-T_9: GPT-3.5-turbo is OpenAI's flagship LLM which has been instruction-tuned and optimized for chat purposes. We utilized the model from Microsoft Azure's OpenAI service and the version was the August 3rd version.

_Llama2-13B-chat_10: Meta developed and publicly released the Llama-2 family of LLMs, a collection of pretrained and fine-tuned generative text models ranging in scale from 7-70B parameters. The chat versions of the models are optimized for dialogue via instruction finetuning. We generated inferences by modifying the PyTorch code provided in the official Github repository: [https://github.com/facebookresearch/llama](https://github.com/facebookresearch/llama).

Footnote 10: [https://platform.openai.com/docs/models/gpt-3-5](https://platform.openai.com/docs/models/gpt-3-5)

_Dolly-v2-7B_(Conover et al., 2023): Dolly-v2-7B is a 6.9 billion parameter causal language model created by Databricks that is derived from EleutherAI's Pythia-6.9B model and finetuned on a 15K instruction corpus generated by Databricks employees. We used the _databricks/dolly-v2-7b_checkpoint11 from HuggingFace.

\begin{table}
\begin{tabular}{c c r} \hline
**Dataset** & **Metric** & **Correlation** \\ \hline  & \(R^{1}\) & 0.499 \\ _CNN/DM_ & \(R^{2}\) & 0.799\({}^{*}\) \\  & \(R^{L}\) & 0.300 \\ \hline  & \(R^{1}\) & 0.899\({}^{**}\) \\ _XSum_ & \(R^{2}\) & 0.999\({}^{**}\) \\  & \(R^{L}\) & 0.899\({}^{**}\) \\ \hline  & \(R^{1}\) & -0.999\({}^{**}\) \\ _News_ & \(R^{2}\) & -0.899\({}^{**}\) \\  & \(R^{L}\) & -0.999\({}^{**}\) \\ \hline  & \(R^{1}\) & -0.799\({}^{*}\) \\ _Reddit_ & \(R^{2}\) & -0.899\({}^{**}\) \\  & \(R^{L}\) & -0.799\({}^{*}\) \\ \hline \end{tabular}
\end{table}
Table 1: Measuring Spearman’s correlation coefficient between position bias (Wasserstein distances) and ROUGE metrics for all datasets (* denotes p-values of \(\leq 0.1\) and ** denotes p-values of \(\leq 0.05\)).

 



### Generating Summaries via LLMs

We provide the prompts used to generate summaries for each LLM and each dataset (prompts might differ between datasets for the same model due to different summary requirements, and they might differ across models as different models respond to input text differently). Note that _[Article]_ in each prompt should be replaced by the article to be summarized. It is also important to note that the prompts were adapted iteratively through multiple experiments to ensure that models followed the prompt as closely as possible. At times models did not follow the prompt specifications exactly and would generate more summary sentences than required for that dataset (for e.g. GPT 3.5-T followed exact prompt specifications 74.9% of the time). Hence, for parity between dataset and model summaries, and fair comparison between all models, we uniformly randomly sampled (so as to not add inductive bias) the number of sentences required from the generated output. Also, due to OpenAI's content moderation policy GPT 3.5-T did not give responses for a minority of inputs (6.16% of all input). We believe future LLM versions will improve along these lines to always follow prompts exactly as specified so post-hoc measures will not be required. We now provide prompts below.

#### f.3.1 Prompts for GPT 3.5-T

_Xsum_: _For the following article: [Article]. Return a summary comprising of 1 sentence. Write the sentence in a dash bulleted format._

_CNN/DM_: _For the following article: [Article]. Return a summary comprising of 3 sentences. Write each sentence in a dash bulleted format._

_Reddit_: _For the following article: [Article]. Return a summary comprising of 1 sentence. Write the sentence in a dash bulleted format._

_News_: _For the following article: [Article]. Return a summary comprising of 1 sentence. Write the sentence in a dash bulleted format._

#### f.3.2 Prompts for Llama2-13B-chat

_Xsum_: _For the following article: [Article]. Return a summary comprising of 1 sentence. Write the sentence in a numbered list format._

_For example: 1. First sentence_

_CNN/DM_: _For the following article: [Article]. Return a summary comprising of 3 sentence. Write the sentence in a numbered list format._

_For example: 1. First sentence_

_CNN/DM_: _For the following article: [Article]. Return a summary comprising of 1 sentence. Write the sentence in a numbered list format._

_For example: 1. First sentence_

_1. First sentence_

_1. First sentence_

_2. Second sentence_

_3. Third sentence_

_Reddit_: _For the following article: [Article]. Return a summary comprising of 1 sentence. Write the sentence in a numbered list format._

_For example: 1. First sentence_

_News_: _For the following article: [Article]. Return a summary comprising of 1 sentence. Write the sentence in abered list format._

_For example: 1. First sentence_

#### f.3.3 Prompts for Dolly-v2-7b

_Xsum_: _Generate a 1 sentence summary for the given article. Article: [Article]._

_CNN/DM_: _Generate a 3 sentence summary for the given article. Article: [Article]._

_Reddit_: _Generate a 1 sentence summary for the given article. Article: [Article]._

_News_: _Generate a 1 sentence summary for the given article. Article: [Article]._

## Appendix G Analyzing the Effect of Prompt Engineering Methods

To motivate future work and showcase the generalizability of our framework, we include results for position bias when a zero-shot prompt engineering approach is used: _role-playing_(Kong et al., 2023). Role-playing has been shown to effectively increase LLM performance and reasoning abilities. We randomly sampled 2275 articles from the _XSum_ dataset and utilize the _Llama2-13B-chant_ LLM. Then, we use each of the 2275 articles to plot position bias distributions for summaries generated using role-playing and our original prompt generated summaries (as well as gold summaries) for comparison. This result is shown as Figure 7.

Figure 7: Using role-playing on Llama-2 and _XSum_.



It can be seen that the curves change slightly- and the role-play summary distribution becomes closer to the gold summary distribution, as expected. However, the overall trends are similar as lead bias is still prominent. Clearly, role-playing on this subset of data is not an exhaustive study, but future work can expand on uncovering how prompt engineering methods (e.g. role-playing, among others) affect summarization position bias.

## Appendix H Additional Results on Flan-T5

Since we have primarily considered specialized encoder-decoder models such as BART and Pegasus in this work, we also provide additional results for position bias when a generalized encoder-decoder model such as Flat-T5 Chung et al. (2022) is used instead. These results can be observed in Figure 8 for all 4 of our datasets. As can be seen in the figure, position bias is low for all datasets, and especially _XSum_ (which contrasts with LLMs). This is also observable in the Wasserstein distance values (\(\approx\)0.050 for _Reddit_ and _CNN/DM_, 0.024 for _News_, and only 0.015 for _XSum_).

## Appendix I Mapping Summary Sentences to Multiple Article Sentences

Currently, \(\phi\) maps back from one summary sentence to one article sentence that contributes the most to that summary sentence. To do this, as \(\phi\) measures similarity between sentences, we currently only pick the article sentence with the maximum similarity to the summary sentence. However, since \(\phi\) is basically measuring similarity, we can return the top-2 or top-3 matches and undertake the same position bias analysis as in the main results. No specific change is necessary, since our position bias estimation is done in aggregate, via binning. It is beneficial to assess the impact of utilizing multiple article sentences, especially for datasets like _XSum_ where the summary is usually just one sentence and discusses facts from multiple article sentences.

We undertake this analysis for each of our 5 models in the paper on the _XSum_ dataset and the results are shown in Figure 9. Here, we have provided position bias distributional results for only using the top-1 match (our original results), top-2 matches, and top-3 matches. It can be seen that the distributions do change slightly, but overall the trends remain the same. More specifically, lead bias for each of the LLMs on _XSum_ is further exacerbated, indicating that even the top-1 match provides good estimates for position bias.

## Appendix J Code and Reproducibility

We open-source our code and provide it as a Github repository: [https://github.com/anshuman23/LLM_Position_Bias](https://github.com/anshuman23/LLM_Position_Bias). The repository contains explicit instructions for how to reproduce our results and analyze the findings for each model. We used Python 3.8.10 for all experiments. The experiments were conducted on Ubuntu 20.04 using NVIDIA GeForce RTX A6000 GPUs running with CUDA version 12.0.

Figure 8: Position bias results for Flan-T5.

Figure 9: Mapping summary sentences to multiple article sentences for measuring position bias on _XSum_.