An Empirical Analysis on Large Language Models in Debate Evaluation (2024)

Abstract: In this study, we investigate the capabilities and inherent biases of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 in the context of debate evaluation. We discover that LLM’s performance exceeds humans and surpasses the performance of state-of-theart methods fine-tuned on extensive datasets in debate evaluation. We additionally explore and analyze biases present in LLMs, including positional bias, lexical bias, order bias, which may affect their evaluative judgments. Our findings reveal a consistent bias in both GPT-3.5 and GPT-4 towards the second candidate response presented, attributed to prompt design. We also uncover lexical biases in both GPT-3.5 and GPT-4, especially when label sets carry connotations such as numerical or sequential, highlighting the critical need for careful label verbalizer selection in prompt design. Additionally, our analysis indicates a tendency of both models to favor the debate’s concluding side as the winner, suggesting an end-of-discussion bias.1

heading: 1 Introduction
text: Prior research in automatic debate evaluation has predominantly relied on pre-trained encoders and the modeling of argument relations and structures (Hsiao et al., 2022; Li et al., 2020; Ruiz-Dolz et al., 2022; Zhang et al., 2023). A significant drawback of these approaches is their dependency on feature engineering and extensive data training, limiting their generalizability across diverse datasets.
The advent of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 (Achiam et al., 2023) has marked the beginning of a new era in automating a wide spectrum of complex tasks (Wei et al., 2022; Thirunavukarasu et al., 2023; Lin* et al., 2023; Wang et al., 2023a; Tang et al., 2023;
∗ Equal contribution 1Our code is publicly available at https://github.com/
XinyiLiu0227/LLM_Debate_Bias/
Zhang et al., 2024; Jiang et al., 2023). These models have been increasingly utilized as automatic evaluators (Chiang and Lee, 2023a,b; Lin and Chen, 2023; Chan et al., 2023; Zeng et al., 2023; He et al., 2023). Leveraging LLMs for debate evaluation presents more challenges, including the extended duration of debates, evolving argument dynamics, and the necessity for evaluators to rely on comprehensive knowledge and reasoning that extend beyond the immediate scope of the debate. Our research delves into the utilization of LLMs for debate evaluation, uncovering their zero-shot capabilities that parallel human evaluators and surpass all existing state-of-the-art (SOTA) methods finetuned on ample data (Li et al., 2020; Hsiao et al., 2022).
We further investigate potential biases in GPT3.5 and GPT-4 within the context of debate evaluation. While previous research has identified various biases in LLMs, such as persona bias (Wan et al., 2023), political bias (Feng et al., 2023), and positional bias (Wang et al., 2023b), our investigation uniquely concentrates on biases affecting debate evaluation performance, a relatively unexplored
470
domain. Specifically, upon comparing outcomes between
scenarios where the positions of candidate responses are switched, persistent bias has been observed in both GPT-3.5 and GPT-4 toward the second candidate response presented, a positional bias induced by the prompt design. Beyond this, both models also display significant lexical biases, particularly when label sets carry connotations such as sequential or magnitude, underscoring the importance of careful selection of label verbalizers in prompt design to mitigate unintended biases (Liu et al., 2023). Moreover, our study reveals that both GPT-3.5 and GPT-4 exhibit a tendency to favor the concluding side of a debate as the winner, pointing to a potential end-of-discussion order bias. Interestingly, after all the identified biases are eliminated, GPT-3.5 still demonstrates a consistent bias, while this residual bias is less obvious for GPT-4. These insights highlight the nuanced nature of biases in LLMs and the complexity of designing fair and unbiased evaluation methodologies for debate evaluation.

heading: 2 Methodology

heading: LLMs’ capability for debate evaluation.
text: As illustrated in Table 1, we utilize an evaluation template T with two placeholders, T (Side1_label, Side2_label), to solicit debate assessments from LLMs. For each debate topic, two labels are assigned to represent the Pro and Con sides, respectively. These labels are then inserted into the designated slots of the evaluation template, creating a prompt T (Side1_label = P,Side2_label = C). This tailored prompt is subsequently employed to query the LLM for its judgment on the debate. Experiments with other templates are presented in the Appendix A.4.
LLMs’ biases in debate evaluation. Analyzing errors and biases in LLMs is complex due to their multifaceted decision-making processes, which are influenced by inherent orders, stances, and the nuances of prompt design. To separate LLM-induced biases from dataset biases, we use a balanced dataset, ensuring class representation equality and mitigating skewness from overrepresentation. This method focuses on LLM-specific biases within a controlled environment, minimizing dataset composition effects. We explore LLM sensitivity to label choice from alphabetic (A/B,
P/C), numerical (1/-1), to textual format (Pro/Con), providing a comprehensive bias analysis. Our study also examines the applicability of these findings in real-world, original distribution settings, extending the relevance of our insights.
3 Experiments
Dataset. We utilize DDO dataset (Durmus and Cardie, 2019), which comprises 77,655 debates from 23 topics on debate.org, structured into rounds with a single utterance from each of the Pro and Con side. We focus on debates of 3 to 5 rounds, defining winners by audience vote differences exceeding two, and exclude debates with forfeits to maintain analysis integrity, following previous works’ setting (Li et al., 2020; Hsiao et al., 2022). The length of these debates aligns well with the input length capacities of current LLMs, making it more suitable than other datasets derived from transcribed debate videos. We present experiments on an additional dataset in the Appendix A.6, which demonstrate consistent findings.
The dataset exhibits a win bias towards the Con side across 3 to 5-round debates (36.9% vs. 63.1%, 44.9% vs. 55.1%, 37.9% vs. 62.1%, respectively), likely due to a concluding side bias with Con frequently concluding debates. To evaluate LLMs in debate assessment, we propose two settings: balanced and unbalanced. The unbalanced setting
replicates the original dataset’s distribution, sampling 500 debates for each round count (totaling 1500). Conversely, the balanced setting aims to examine LLMs’ inherent bias by ensuring equal representation of four scenarios—Pro or Con initiating and winning or losing—with 125 debates each for 3 and 4 rounds, and due to data constraints, 75 debates each for 5 rounds, resulting in 500 debates for 3 and 4 rounds and 300 for 5 rounds.
Evaluation metrics. In addition to the accuracy reported by previous works, we measure weighted F-1 score to accommodate the imbalance between Pro win and Con win in the original data distribution, aiming for a more comprehensive understanding.
Models. For open-source model, we select LLaMA2-70B (Touvron et al., 2023) as it has been demonstrated as the most powerful model in the LLaMA family. For close-source models, we select the latest stable versions of OpenAI’s GPT-3.5 and GPT-4 models at the time to conduct our experiments, namely gpt-3.5-turbo-1106 and gpt-4-1106preview.
Human annotation. To assess the effectiveness of LLMs, two authors manually annotated the “win/lose” outcomes of randomly selected debates independently for 75 debates. Unlike the collective voting in multi-audience settings, this annotation was independently completed by a single annotator.

heading: 4 Results and Analysis

heading: 4.1 LLMs’ Performance
text: Table 2 reveals that GPT-3.5 and GPT-4 match human evaluators in assessing debates, highlighting their effectiveness. Using 75 debates labeled by
two of the authors enables a direct comparison with GPT-3.5 and GPT-4. They achieve significant accuracy and F1 scores—82.04% and 81.85% for GPT-3.5, and 86.22% and 86.01% for GPT-4, respectively, outperforming previous SOTA models. LLaMA2-70B, on the other hand, performs significantly worse than existing methods, being only comparable to the ruble-based method. Thus, it is less likely for LLaMA2 to be adopted as the automatic debate evaluator. Our further experiments for bias analysis therefore mainly focus on GPT-3.5 and GPT-4.
Notably, the word choice in the prompt can have a profound impact on the performance of LLMs, as shown in Table 3. Within our study, employing the label set 1/-1 results in a marked decline in the performance of GPT-3.5, and using the label set Pro/Con leads to the lowest observed outcomes in GPT-4. GPT-3.5 is particularly sensitive to negative phrasing; its performance degrades below that of random selection when prompted to identify the debate’s loser rather than the winner. In contrast, GPT-4 demonstrates much less sensitivity to such changes, showing only a minor decrease in performance.
4.2 Biases Analysis
Our study explores biases present in GPT-3.5 using a balanced setting of DDO dataset. Additional analyses of the GPT-3.5 on the original unbalanced DDO data and analysis of GPT-4 are in Appendix A.3 and A.2, respectively. The experiments with an extra dataset that confirm our findings are presented in Appendix A.6.
Positional Bias. Figure 2 compares the proportion of predictions labeled as "Con" between in-
stances where the Con is positioned at the first candidate response and the instances where Con is placed as the second candidate response. It shows that GPT-3.5 systematically favors the candidate response in the second position across all tested verbalizer settings. The two-sided P-values of the two-proportion z-test consistently suggest the positional bias is significant. This finding confirms the second position preference of GPT-3.5 as reported by Wang et al. (2023b). On the unbalanced data that reflects the original distribution, we also
investigate the changes in the counts of predicted Pros and predicted Cons between the settings with shuffled candidate response positions and fixed positions. The details are shown in Appendix A.2, and A.3, suggesting a consistent trend.
Lexical Bias. GPT-3.5 is affected by the lexical choice of labels representing the two sides of a debate, as demonstrated by Figure 3. These differences highlight the inherent lexical bias of GPT-3.5 within the selected label set. GPT-3.5 prefers the label ‘B’(‘-1’) over ‘A’(‘1’), predicting Con as the
winner significantly more frequently when ‘B’(‘1’) represents Con as opposed to when ‘A’(‘1’) does, as shown in Figure 3. There is no significant lexical bias found within the P/C label set for GPT3.5. The Con/Pro label configuration, which swaps the position names of the two sides, could confuse LLMs about each label’s corresponding side, as the content of the debate usually reveals the actual position of each side. This ambiguity might contribute to the poorer performance observed in the Con/Pro label setting and raises questions about the inferred preference for the ’Con’ label. The analysis of lexical bias is further detailed in Appendix A.2 and

heading: A.3.
text: Order Bias. GPT-3.5 exhibits a significant order bias, favoring the side that concludes the debate, as shown in experiments where the Pro_label consistently ranked as the primary response (Table 4). This bias is statistically significant across all verbalizer options. The results suggest an inherent tendency in LLMs to give more weight to the final arguments.

heading: 5 Discussion
text: Our research demonstrates that LLMs outperform current SOTA models in evaluating debates but are influenced by specific word choices, affecting their efficacy. We highlight LLMs’ embedded biases—positional, lexical, and order—offering insights for future LLM training enhancements.
Despite attempts to neutralize positional bias by shuffling labels in Figs 3a and 3d, GPT-3.5 still exhibits a Pro bias, contradicting its lexical preference for ’B’(’Con’). This might suggest a con-
firmation bias-like tendency in GPT-3.5, favoring agreement with the debate topic. We further conduct experiments shuffling A/B with B/A and 1/-1 with -1/1 label sets, where each label randomly represents Pro or Con in 50% of cases, with positions also shuffled. Despite eliminating lexical and positional biases, results indicate a persistent Pro bias, detailed in Appendix Figure 6, pointing to an underlying tendency warranting further investigation.
6 Limitations
The insights from our investigation, based on the examination of GPT-3.5 and GPT-4, indicate that the discerned behavioral patterns might be unique to these specific models and not necessarily extend to other language models with divergent architectures or training approaches. With the relentless advancement in language model technology and the anticipation of updated versions, the biases detected in GPT-3.5 and GPT-4 could become obsolete in subsequent iterations. Highlighting the significance of prompt types and training techniques on the efficacy of models, our research underlines the imperative for continued research to identify the optimal prompt types for various scenarios and the optimal training methods for reducing bias.
Although various biases may interact and potentially counterbalance each other, leading to improvements, the intensity of distinct bias types can vary significantly across different contexts. Consequently, a prompt that appears to exhibit balanced bias in one scenario may manifest more pronounced bias under slightly altered conditions.
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heading: A Appendix

heading: A.1 More details of the Dataset
text: The dataset extends beyond textual debate content to audience votes across four evaluation criteria: making more convincing arguments, better conduct, use of reliable sources, and spelling and grammar proficiency. Consistent with prior research (Li et al., 2020; Hsiao et al., 2022), our analysis utilizes the criterion of "making more convincing arguments" for assessing debate outcomes. To ensure alignment with these studies and enhance comparability, we narrow our focus to debates with a definitive margin of victory—requiring a vote difference exceeding two—and limit our analysis to debates spanning three to five rounds, which represent the bulk of the dataset. Debates compromised by forfeits, identified either through explicit forfeit labels or instances of one side forfeiting a round, are omitted from consideration.
The debates within the dataset have an average length of 1574.93 words, with the majority fit within the input length constraints of contemporary LLMs. Regarding audience engagement, the average vote counts for 3-round, 4-round, and 5-round debates stand at 10.05, 7.02, and 7.03, respectively. Furthermore, the average vote differences for these debate formats are 5.52, 4.69, and 4.79, indicating a clear preference in outcomes that facilitate our focused analysis on convincing arguments. The percentages of Con conclude the debates are 77.84%, 78.24%, and 78.13% for 3-round, 4-round and 5-round debates respectively.

heading: A.2 Additional Results of DDO Dataset in the Balanced Setting
text: The detailed confusion matrices with various settings we experiment on balanced datasets can be found in Figure 7 for GPT-3.5 and in Figure 9 for GPT-4.
Performance. We also test GPT-3.5 and GPT-4 on the same subset of human-annotated data. The accuracies achieved by GPT-3.5 and GPT-4 are 79.73% and 84.00% respectively.
Positional Bias. For GPT-3.5, McNemar’s tests (McNemar, 1947) are also conducted for the settings with shuffled candidate response positions and fixed positions based on Table 5, and the results are all significant.
The direction of the positional bias presented by GPT-4 is also shown towards the second position, contradicting the finding of the first position favorite illustrated by Wang et al. (2023b). The two-sided p-value from the two-portion z-tests demonstrates that the positional bias in GPT-4 as shown in Figure 4 is also statistically significant.
Lexical Bias. The difference in the significance of lexical bias within the A/B label set and P/C label set could be due to the alphabetical distance they have or due to their common usage. To discern the underlying cause, we further experiment with the M/N label set for they are alphabetically adjacent but not typically associated with sequential interpretation. The results, detailed in Figure 8, reveal minimal lexical bias within the M/N group, suggesting that the bias originates from conventional usage rather than alphabetic proximity.
To further quantitatively assess the lexical bias in GPT-3.5, we employ McNemar’s test to analyze instances of concordances (both predict Pro or Con), instances of discordances (one predicts Pro and the other predict Con) of each flipping group as shown in Table 6. All results are statistically significant.
Similar to GPT-3.5, GPT-4 also exhibits lexical bias towards ’B’, ’-1’ and potentially ’Con’ within the A/B, 1/-1, and Pro/Con label set. However, GPT-4 favors ’C’ over ’P’ significantly. McNemar’s tests of lexical bias for GPT-4 are shown in Table 8 for GPT-4.
Order Bias. The Chi-squared test to show the association between the GPT-4’s predictions and the sides that conclude the debates are shown in Table 9. Same as GPT-3.5, across all verbalizer choices, the order biases presented by GPT-4 are also statistically significant. In addition, the magnitude of the order bias within GPT-3.5 is much stronger than GPT-4, as measured by the Phi Coefficient.
Stance Bias. Our hypothesis regarding stance bias is less evident in GPT-4, as it becomes overshadowed by lexical bias after positional bias is mitigated through shuffled positions. We conduct two experiments, employing shuffled label sets and positions under the A/B and 1/-1 configurations, as depicted in Figure 6. The findings reveal a contrasting residual bias in GPT-4 compared to GPT-3.5, after addressing positional, lexical, and order biases.

heading: A.3 Extension to Unbalanced Setting of DDO Dataset
text: Positional Bias. We additionally explore variations in "Pro" and "Con" predictions when alternating between shuffled and fixed candidate response placements in unbalanced data that reflects the original distribution. These observations, detailed in Table 10, highlight a consistent pattern.
Lexical Bias. The same experiments applied to the unbalanced dataset with the original distribution yield consistent results for the direction of lexical bias in GPT-3.5 (see Table 11), except for the non-significance P/C set.

heading: A.4 Enhancing Bias Reduction through Prompt Engineering
text: Winning Definition We find no significant difference in the models’ performance between giving a definition and not giving a definition in the prompt in our preliminary experiments. Therefore, we stick with the more concise version that we illustrate in the main body of the paper. We spectacle it is because our definition of ‘winning’ is consistent with the common understanding of the term.
LLM-Eval In a further step, we direct the LLMs to provide reasons for their judgments before they generate the outcomes using the prompt template shown in Table 12. Such a method is reported by Wang
et al. (2023b) to be able to reduce the positional bias. We do a pilot experiment using GPT-3.5 with a single A/B label set to see if the effect comes from ’reducing’ the bias or from providing a bias in the opposite direction and thus counteract it.
As the results shown in Figure 10, GPT-3.5 exhibits a greater bias towards Pro when generating analysis compared to when positions are shuffled to eliminate the positional bias. Therefore, it is more likely that prompting GPT-3.5 to generate the analysis first introduces a new bias towards Pro, which is in the opposite direction of the positional bias, since Con is consistently positioned as the second candidate response. However, arriving at a definitive answer necessitates further experimentation, which we defer to future research.

heading: A.5 Debate Example
text: The debate example can be found in Table 13, 14 and 15.

heading: A.6 Extension to IQ2 Dataset in the Balanced Setting with GPT-4
text: There are 108 debates in the IQ2 dataset. The average number of words contained in each debate, including all contexts, is 17579, exceeding the current maximum length constraint of GPT-3.5 (16k tokens). Only 24 debates in IQ2 have a word count below this limit, which would result in a sample size too small to derive meaningful results. While excluding the context from the host or audience involved could reduce the average length of each debate to 12801 words, it could also lead to a lack of context in some parts of the debaters’ conversation. Therefore, we only analyze IQ2 dataset on GPT-4 with a 32k token limit. We again use the balanced setting as we explained in the Methodology section. Based on the smallest
category (Con end with Con win) among the four conditions, we sampled IQ2 to be 13 for pro/con side end with pro/con win, a total of 52 samples.
Positional Bias. GPT-4 exhibits consistent positional bias on the IQ2 dataset, as shown in Table 16. The second position is preferred over the first position, proved by the higher proportion of Predicted Con when Con is positioned as the second candidate response.
Lexical Bias. We find consistent lexical biases in the IQ2 dataset with GPT-4, as shown in Table 17. ‘B’(‘-1’) is preferred over ‘A’(‘1’), indicated by the higher proportion of Predicted Con when ‘B’(‘-1’) represents Con compared to when ‘A’(‘1’) represents Con.
Order Bias. GPT-4 exhibits order bias on the IQ2 dataset, which is also consistent with our finding on DDO dataset, as demonstrated by Table 18. The ending side of a debate is more likely to be predicted as the winner.

heading: Content Prompt

heading: Future Work
text: However, arriving at a definitive answer necessitates further experimentation, which we defer to future research