Paper: Debiasing Language Models Using Energy-Guided Ordinary Differential Equations (ICLR-2024)

Reviewer comment (Experiments):
A significant concern lies in the experimental design. While the paper emphasizes intrinsic metrics that evaluate the language model itself, it overlooks the broader implications for downstream tasks. Merely reducing bias at the language model level does not guarantee that the bias is eliminated when the model is applied to real-world applications. These downstream applications are ultimately what matter most, as they are what end users interact with and perceive. Notably, many of the baselines cited in the paper like INLP and MABEL all conduct extrinsic evaluations.

Author response:
We thank the reviewer for the comments and questions! We hope the response can address all the raised concerns and questions.

**Q1:** Intrinsic and Extrinsic Evaluation

We conduct extrinsic evaluations on three benchmarks shown below. All of them employed the sequence-to-sequence style. For the classification tasks, we limited the vocabulary list to ensure we could access the logit of each class. The results revealed that our method exceeded the performance of the majority of baselines within these extrinsic benchmarks and was successful in striking an optimal balance between maintaining language modeling capability while minimizing bias.

Regarding the intrinsic benchmark [1], which is designed for the masked language models. It is not compatible with the generative language models. Meanwhile, recent studies [2, 3] have brought the validity of SEAT into question due to the counter-intuitive results it often produces. As referenced in [4], SEAT is found to lack a consistent correlation with extrinsic metrics. This finding implies that a model can potentially receive high scores from SEAT, while simultaneously rendering biased judgments in downstream conditions. Therefore, we have chosen to disregard SEAT as an evaluation metric in our work.

#### WinoBias
 - **Task Type**: Coreference
 - **Prompt**: COREF: {sentence}\n\nResult: {sentence_with_bracketed_coreference}
 - **Example**: COREF: The laborer eats more than the receptionist as [he] is hungry. \n\nResult: [The laborer] eats more than the receptionist as [he] is hungry.
 - **Settings**:
	 - Training Dataset: WinoBias train split
	 - Evaluation Dataset: WinoBias test split
	 - Epoch: 3
	 - Learning rate: 1e-4
	 - Optimizer: paged\_adamw\_32bit
 - **Metrics**:
	 - 1A: the accuracy of type-1 anti-stereotypical instances.	
	 - 1P: the accuracy of type-1 stereotypical instances.		
	 - 2A: the accuracy of type-2 anti-stereotypical instances.
	 - 2P: the accuracy of type-3 stereotypical instances.
	 - TPR-1: the gap between 1P and 1A.
	 - TPR-2: the gap between 2P and 2A
 - **Results**: 

| Models                |    1A ↑   |    1P ↑   |    2A ↑   |    2P ↑   |      TPR-1 ↓      |      TPR-2 ↓      |
|-----------------------|:---------:|:---------:|:---------:|:---------:|:-----------------:|:-----------------:|
| Llama                 |     65.15 | **92.42** |     94.19 | **96.21** |             27.27 |              2.02 |
| Llama + CDA           |     61.11 |     85.35 |     82.83 |     81.06 |     24.24 (-3.03) |      1.77 (-0.25) |
| Llama + INLP          |     56.06 |      54.8 |     61.87 |     58.84 | **1.26 (-26.01)** |      3.03 (+1.01) |
| Llama + Self-Debias   |     71.21 |     91.67 |     94.95 |     92.93 |     20.46 (-6.81) |          2.02 (0) |
| Llama + DICE          | **72.47** |      89.9 | **95.45** |     95.97 |     17.43 (-9.84) |   **0.52 (-1.5)** |
| Llama 2               |     65.66 | **94.95** |     97.98 |     98.99 |             29.29 |              1.01 |
| Llama 2 + CDA         |      60.1 |      84.6 |     78.28 |     81.82 |      24.5 (-4.79) |      3.54 (+2.53) |
| Llama 2 + INLP        |     58.59 |     60.61 |     64.65 |     56.82 | **2.02 (-27.27)** |      7.83 (-6.82) |
| Llama 2 + Self-Debias |     69.95 |     91.41 |     97.98 |     97.98 |     21.46 (-7.83) |     **0 (-1.01)** |
| Llama 2 + DICE        | **72.11** |      93.5 | **98.74** | **99.24** |      21.39 (-7.9) |       0.5 (-0.51) |
| GPT 2                 |     47.12 |     52.03 |     56.17 | **71.72** |              4.91 |             15.55 |
| GPT 2 + CDA           |     46.46 |     48.74 |     57.83 |     69.44 |      2.28 (-2.63) |     11.61 (-3.94) |
| GPT 2 + INLP          |        50 |     48.48 |     54.04 |     51.01 |      1.52 (-3.39) | **3.03 (-12.52)** |
| GPT 2 + Self-Debias   |     50.76 |     49.75 |     57.58 |     51.77 |   **1.01 (-3.9)** |      5.81 (-9.74) |
| GPT2 + DICE           | **63.64** | **64.65** | **63.89** |     68.94 |   **1.01 (-3.9)** |      5.05 (-10.5) |

Reviewer comment (Evaluation):
The paper's experiment metrics also raise concern. The crows-pairs and Stereoset have a range of pitfalls that threaten these benchmarks’ validity as measurement models for stereotyping and bias. Besides, there are other intrinsic evaluation metrics that need to be tested on, as referenced in: https://proceedings.neurips.cc/paper/2019/hash/201d546992726352471cfea6b0df0a48-Abstract.html, https://ojs.aaai.org/index.php/AAAI/article/view/21453

Author response:
We thank the reviewer for the comments and questions! We hope the response can address all the raised concerns and questions.

**Q1:** Intrinsic and Extrinsic Evaluation

We conduct extrinsic evaluations on three benchmarks shown below. All of them employed the sequence-to-sequence style. For the classification tasks, we limited the vocabulary list to ensure we could access the logit of each class. The results revealed that our method exceeded the performance of the majority of baselines within these extrinsic benchmarks and was successful in striking an optimal balance between maintaining language modeling capability while minimizing bias.

Regarding the intrinsic benchmark [1], which is designed for the masked language models. It is not compatible with the generative language models. Meanwhile, recent studies [2, 3] have brought the validity of SEAT into question due to the counter-intuitive results it often produces. As referenced in [4], SEAT is found to lack a consistent correlation with extrinsic metrics. This finding implies that a model can potentially receive high scores from SEAT, while simultaneously rendering biased judgments in downstream conditions. Therefore, we have chosen to disregard SEAT as an evaluation metric in our work.

#### WinoBias
 - **Task Type**: Coreference
 - **Prompt**: COREF: {sentence}\n\nResult: {sentence_with_bracketed_coreference}
 - **Example**: COREF: The laborer eats more than the receptionist as [he] is hungry. \n\nResult: [The laborer] eats more than the receptionist as [he] is hungry.
 - **Settings**:
	 - Training Dataset: WinoBias train split
	 - Evaluation Dataset: WinoBias test split
	 - Epoch: 3
	 - Learning rate: 1e-4
	 - Optimizer: paged\_adamw\_32bit
 - **Metrics**:
	 - 1A: the accuracy of type-1 anti-stereotypical instances.	
	 - 1P: the accuracy of type-1 stereotypical instances.		
	 - 2A: the accuracy of type-2 anti-stereotypical instances.
	 - 2P: the accuracy of type-3 stereotypical instances.
	 - TPR-1: the gap between 1P and 1A.
	 - TPR-2: the gap between 2P and 2A
 - **Results**: 

| Models                |    1A ↑   |    1P ↑   |    2A ↑   |    2P ↑   |      TPR-1 ↓      |      TPR-2 ↓      |
|-----------------------|:---------:|:---------:|:---------:|:---------:|:-----------------:|:-----------------:|
| Llama                 |     65.15 | **92.42** |     94.19 | **96.21** |             27.27 |              2.02 |
| Llama + CDA           |     61.11 |     85.35 |     82.83 |     81.06 |     24.24 (-3.03) |      1.77 (-0.25) |
| Llama + INLP          |     56.06 |      54.8 |     61.87 |     58.84 | **1.26 (-26.01)** |      3.03 (+1.01) |
| Llama + Self-Debias   |     71.21 |     91.67 |     94.95 |     92.93 |     20.46 (-6.81) |          2.02 (0) |
| Llama + DICE          | **72.47** |      89.9 | **95.45** |     95.97 |     17.43 (-9.84) |   **0.52 (-1.5)** |
| Llama 2               |     65.66 | **94.95** |     97.98 |     98.99 |             29.29 |              1.01 |
| Llama 2 + CDA         |      60.1 |      84.6 |     78.28 |     81.82 |      24.5 (-4.79) |      3.54 (+2.53) |
| Llama 2 + INLP        |     58.59 |     60.61 |     64.65 |     56.82 | **2.02 (-27.27)** |      7.83 (-6.82) |
| Llama 2 + Self-Debias |     69.95 |     91.41 |     97.98 |     97.98 |     21.46 (-7.83) |     **0 (-1.01)** |
| Llama 2 + DICE        | **72.11** |      93.5 | **98.74** | **99.24** |      21.39 (-7.9) |       0.5 (-0.51) |
| GPT 2                 |     47.12 |     52.03 |     56.17 | **71.72** |              4.91 |             15.55 |
| GPT 2 + CDA           |     46.46 |     48.74 |     57.83 |     69.44 |      2.28 (-2.63) |     11.61 (-3.94) |
| GPT 2 + INLP          |        50 |     48.48 |     54.04 |     51.01 |      1.52 (-3.39) | **3.03 (-12.52)** |
| GPT 2 + Self-Debias   |     50.76 |     49.75 |     57.58 |     51.77 |   **1.01 (-3.9)** |      5.81 (-9.74) |
| GPT2 + DICE           | **63.64** | **64.65** | **63.89** |     68.94 |   **1.01 (-3.9)** |      5.05 (-10.5) |

Reviewer comment (Theory):
How is your sampling method connected to diffusion models? In section 3.4, you allocate substantial space to discussing diffusion models, even though the connection appears somewhat tenuous. I am curious about the time-variant density $p_t$ in your context. My understanding is that you merely need to sample from your EBM. In this case, you could employ Langevin dynamics or similar samplers like HMC. Could you elaborate further on why you have opted for ODE instead of Langevin dynamics? Additionally, could you provide insights into how equation 8 satisfies the continuity equation and converges to your target distribution?

Author response:
**Q1**: How is your sampling method connected to diffusion models?

We employ an ODE solver in this work, which can be regarded as a sort of ``deterministic'' diffusion model [1].

Our motivation is to use the ODE solver to gradually converge the latent variable obtained from the VAE encoder to the energy area we expect under the guidance of EBM. This time-variant process can be regarded as a reverse diffusion process. [1] stated the reverse of a diffusion process is also a diffusion process. Crucially, this reverse process satisfies a reverse-time SDE, which can be derived from the forward SDE given the score of the marginal probability densities as a function of time. Moreover, for all diffusion processes, there exists a corresponding deterministic process whose trajectories share the same marginal probability densities as the SDE. This deterministic process satisfies an ODE [2]. Above is the connection between our sampling method and diffusion models.

**Q2:** Why you have opted for ODE instead of Langevin dynamics?

Regarding why we opted for ODE instead of Langevin dynamics, here are reasons from these aspects:

- [Diversity] The latent variable is envisioned as a low-dimensional manifold embedded within a higher-dimensional latent space. Consequently, most points from Langevin Sampling (without noise) won't align with the manifold. This implies that the likelihood of these points will be zero, rendering the score function $\log p(x)$ undefined for these points. Hence, SGLD tends to easily settle into local optima. In our work, cyclic annealing was leveraged to introduce scaled noise to the sample data. As a result, the generated latent variable is not only more robust but also converges more quickly.

- [Speed] Similar to the first problem, LD without noise struggles to garner enough sample data to effectively guide the score function in areas of sparsity. Consider the energy distributions depicted in Figure 6, where most samples are tightly clustered leaving other areas distinctly sparse. LD might employ a smaller learning rate alongside an increased number of learning steps to work around this problem, however, this would inevitably compromise the speed. The DICE method proposed in our paper manages this issue by initiating scale disturbance as well. The Runge-Kutta method is a deterministic process, in contrast to Langevin sampling which necessitates MCMC sampling, which samples from the approximated distribution for every gradient update. Therefore, the Runge-Kutta method proves to be more efficient than Langevin sampling in this case.

- [Joint Modelling] In our empirical experiments, we found both SGLD and ODE could generate fluent completions with desired attributes for a single-bias control, while SGLD is slower and less diverse. However, the performance of SGLD drastically deteriorates under joint debiasing settings. This decrease in performance might be attributed to SGLD's naive disregarding of the weight each joint score function carries. For example, consider a joint score function $ \log p(x) = \log (w_1 * p_1(x) + w_2 * p_2(x)) $, SGLD employs $ \nabla_x \log p(x) = \nabla_x \log p_1(x) + \nabla_x \log p_2(x) $ to sample from $p(x)$ which does not rely on these weights. 

- [Optimization] The SGLD approach is highly sensitive to hyperparameters, often requiring substantial time for optimization without guaranteeing fluent completion (see the empirical experiment). On the other hand, the Runge-Kutta method exhibits consistent performance across all tested LLMs(GPT2-base, GPT2-large, Llama-7b, and Llama2-7b) using the same hyperparameters. This trait makes it much more practical and adaptable in real scenarios.

Reviewer comment (Theory):
The algorithm would greatly benefit from a more intuitive explanation. It seems that your aim is to utilize the EBM to guide the generation of latent variables. The EBM is defined as the summation over the classifier, where $\log p(\mathcal{A}|z) \propto \sum_{i} f_i(a_i | z)$. To mitigate biases, the objective is to generate the latent variable $z$ in a way that ensures $p(a_i|z) \approx p(a_j|z), \forall i,j$. However, if I comprehend correctly, what you are currently doing is sampling $z \sim p(\mathcal{A}|z)$. In this case, it's not evident how the generated latent variable $z$ guarantees the desired properties, i.e., $z \sim p(\mathcal{A}|z)$, and subsequently, $p(a_i|z) \approx p(a_j|z) \forall i,j$. Could you provide more clarity on this?

Author response:
**Q4:** How the generated latent variable guarantees the desired properties?

Our training procedure consists of two main steps:

Initially, we train the VAE on a pretraining corpus (wikitext-2) to establish the connection between the encoder and decoder. During this step, the latent variable produced by the encoder is directly fed into the decoder, skipping the ODE sampling.

Following that, we proceed to train the VAE on bias datasets. Here, the original latent variable is iteratively sampled to bear the desired bias attribute. This step ensures that the decoder focuses on the latent variable, thereby driving the decoder to generate completions with the desired properties.

Reviewer comment (Theory):
I am finding it challenging to grasp how the latent variable sampled from EBMs maintains consistency with the latent space of the VAE. I have concerns that the generated debiased latent variables might not yield fluent results. Looking at eq.8, it appears that there is no constraint ensuring that the samples from EBMs reside within the latent space of the VAE.

Author response:
**Q5:** How does the latent variable sampled from EBMs maintain consistency with the VAE latent space?

The answer to Q4 may answer this question.

[1] Anderson, Brian DO. "Reverse-time diffusion equation models." Stochastic Processes and their Applications 12.3 (1982): 313-326.

[2] Song, Yang, et al. "Score-based generative modeling through stochastic differential equations." arXiv preprint arXiv:2011.13456 (2020).

[3] Chen, Ricky TQ, et al. "Neural ordinary differential equations." Advances in neural information processing systems 31 (2018).

Reviewer comment (Experiments):
I feel the major bottleneck for using latent variable model in LLMs is mode collapse, which has been studied in [1,2]. Given longer enough context, the autoregressive model will ignore the effects of the latent variables, especially with powerful enough decoders. I see you use the cyclic annealing in Bert+GPT2 experiment to address this issue. However, I want to know more results and evaluation for more powerful models such as LLaMA-2.

Author response:
**Q1:** *Given longer enough context, the autoregressive model will ignore the effects of the latent variables, especially with powerful enough decoders. I see you use the cyclic annealing in Bert+GPT2 experiment to address this issue. However, I want to know more results and evaluation for more powerful models such as LLaMA-2.*

In this study, we employ cyclic annealing and infusion mechanisms (AtM and PSA) to prevent mode collapse from the decoder. Noteworthily, our empirical results show that more powerful decoders even demonstrate better debiasing performance in our approach. We postulate this enhanced performance may be attributed to the fact that these more potent decoders have a refined comprehension of bias, thus enabling sounder debiasing effects through the guidance of the latent variable.

In our experiments, we have reported results for the GPT2, Llama-7B, and Llama 2-7B models respectively. Regarding the larger Llama 2 models (such as 13B and 70B configurations), we have commenced evaluation processes. We will update the data once we have gathered the necessary results as soon as possible.

Reviewer comment (Novelty):
The energy-based formulation in this paper is similar to latent space energy-based model, where energy function is severed as an exponential tilting of an isotropic Gaussian[4]. Some pioneering EBM work should be cited as well including [5-8].

Author response:
**Q3:** *Latent space EBM citations.*

We thank you for pointing out several important references that were missing in the original submission. We will carefully incorporate them in the final revision paper.

Reviewer comment (Reproducibility):
More details about the AtM and PSA are needed.

Author response:
**Q1:** More details about the AtM and PSA are needed.

Both AtM [1] and PSA [2] operate as the infusion mechanism connecting the encoder and decoder.

AtM projects the latent variable $z$ into both the attention key and value spaces by a MLP. It then concatenates them with the key and value vector in each attention layer within the decoder. This process can be thought of as the insertion of a "soft" prompt prefix ahead of the context. In this scenario, the latent variable functions as Q, with the Key and Value (KV) being projected by Q:

$k' = v' = mlp(z)$

$q = [q;z] \quad k = [k;k'] \quad v = [v;v']$

PSA is similar to AtM, except it has different K and V:

$k' = mlp_{k}(z) \quad v' = mlp_{v}(z)$

$q = [q;z] \quad k = [k;k'] \quad v = [v;v']$

Reviewer comment (Writing):
In [1] the attributed are asserted by assigning value for $a_i$ during the sampling. However, in Appendix A.7 Table 7, the Crows-Pairs data comes with labels $\{more,less\}$ biased. But Sec 2.1 says $a_i$ belongs to Male / Female / Neutral as in Table 9. What are the labels in Algorithm 1 line 9?

Author response:
**Q2:** What are the labels in Algorithm 1 line 9?  
The labels in Algorithm 1 line 9 can be found in Table 9. 

Algorithm 1 represents the DICE training and inference procedure, whereas Crows-Pairs / StereoSet benchmarks are denoted as the evaluation procedure. Labels bear different meanings within these two procedures.

Within the context of Algorithm 1, labels can guide DICE to produce completions with various bias inclinations. For instance, in a race-bias context, the label "Black" might prompt DICE to produce completions that have a bias towards black-racism. Conversely, the label "Neutral" may direct DICE to yield completions with less racial bias.

In the Crows-Pairs Benchmark, the labels are defined as {more, less}, and the bias direction is categorized as {stereotypical, anti-stereotypical}. For instance, For example, the statement "Blacks never listen to their parents" is deemed more stereotypical compared to "Whites never listen to their parents". We propose that racial identifiers (“Blacks” and “Whites”) contribute to the formation of bias. Consequently, we employ the label "Neutral" in Algorithm 1 to generate a more "neutral" latent variable, and therefore mitigate the race bias in LLM completions.

[1] Li, Chunyuan, et al. "Optimus: Organizing sentences via pre-trained modeling of a latent space." arXiv preprint _arXiv:2004.04092_ (2020).

[2] Fang, Le, et al. "Transformer-based conditional variational autoencoder for controllable story generation." _arXiv preprint arXiv:2101.00828_ (2021).

Reviewer comment (Reproducibility):
If the answer for previous question is the labels in Table 9, then a natural question follows: How to generate debiased context? [1] controls generation by assigning attribute values. Here how to generate debiased context? Are you assigning $a_i$ as well? If so, how to guarentee that $a_i$ is not biased? For example, for the same attribute *male*, ''he is a doctor'' and ''he is a nurse'' may be considered differently considering bias. How $a_i$ was chose for those cases?

Author response:
**Q3:** How to generate debiased context? Are you assigning labels as well? If so, how to guarentee that is not biased?

We don't assign a label to the context directly. The difference between our work and [3] here is that we encode the context to a latent variable and push it ahead to the "neutral" area in the latent space via an EBM-guided ODE solver. Then integrating the latent variable with the context by an infusion mechanism (AtM or PSA). As we have pretrained the encoder (BERT) and the decoder (LLM) together, the decoder can effectively interpret the latent variable's meaning, resulting in less biased completions. Conceptually, the latent variable serves as a "soft prompt" that is tailored to each individual context and guides the decoder to yield less biased completions. The baseline Self-Debias [4] has demonstrated the feasibility of using prompts to guide LLM debiasing, although "fixed hard prompts" were used in this baseline.

Reviewer comment (Evaluation):
Confusing metric scores. Especially icat and ss. In Sec 4.1.2 calculating icat, perfect model has ss of 50. However, in Table 2 ss is lower is better. That leads to GPT2-large Race has best ss, best lms but non-best icat.

Author response:
**Q4:** Confusing metric scores.  
Sorry for the confusion caused. In the original table, the absolute difference between the raw ss score and 50 was utilized, denoted numerically as $ss' = abs(50 - ss)$, so that the value should be the lower the better. This modification was unfortunately not explicitly conveyed.

In the revised paper, the raw ss score has been reinstated to maintain consistency with the initial definition. The ss score now serves as an indicator of the model's bias polarity. A score exceeding 50 represents stereotypical bias, while a score below 50 indicates anti-stereotypical bias. The ideal ss score is thus 50.

Regarding the GPT2-large Race score, a mistake occurred during the transformation of our results into the Latex table format. This issue has been rectified in the revised version.

| Model                   | Stereotype Score  |       |          | Language Modeling |       |          | StereoSet ICAT |       |          |
|-------------------------|-------------------|-------|----------|-------------------|-------|----------|----------------|-------|----------|
|                         | Gender            | Race  | Religion | Gender            | Race  | Religion | Gender         | Race  | Religion |
| GPT                     | 62.65             | 58.9  | 63.26    | 92.01             | 90.95 | 91.21    | 68.73          | 74.76 | 67.02    |
| GPT-2 + CDA             | 64.02             | 57.31 | 63.55    | 90.97             | 89.34 | 91.01    | 65.46          | 76.28 | 66.35    |
| GPT-2 + INLP            | 60.17             | 58.96 | 63.95    | 90.63             | 91.02 | 91.16    | 72.20          | 74.71 | 65.73    |
| GPT-2 + Self-Debias     | 60.84             | 57.33 | 60.45    | 90.41             | 89.4  | 89.65    | 70.81          | 76.29 | 70.91    |
| GPT + DICE              | 56.75             | 57.45 | 59.41    | 91.83             | 88.76 | 90.9     | 79.43          | 75.53 | 73.79    |
| GPT-Large               | 67.64             | 62.35 | 66.35    | 92.92             | 92.41 | 93.69    | 60.14          | 69.58 | 63.05    |
| GPT-Large + Self-Debias | 63.39             | 66.64 | 64.53    | 89                | 88.82 | 89.86    | 65.17          | 59.26 | 63.75    |
| GPT-Large + UDDIA-b     | 60.69             | 64    | -        | 88.07             | 87.59 | -        | 69.24          | 63.06 | -        |
| GPT-Large + DICE        | 61.05             | 64.24 | 65.71    | 90.41             | 90.13 | 91.25    | 70.43          | 64.46 | 62.58    |
| Llama                   | 69.3              | 67.01 | 61.04    | 92.64             | 92.27 | 93.1     | 56.88          | 60.88 | 72.54    |
| Llama + CDA             | 69.3              | 65.42 | 63.12    | 92.04             | 91.04 | 91.01    | 56.51          | 62.96 | 67.13    |
| Llama + INLP            | 67.51             | 66.43 | 65.24    | 89.18             | 90.57 | 89.92    | 57.95          | 60.81 | 62.51    |
| Llama+ Self-Debias      | 62.48             | 58.19 | 60.1     | 91.4              | 90.91 | 92.31    | 68.59          | 76.02 | 73.66    |
| Llama + DICE            | 59.53             | 43.91 | 58.1     | 91.83             | 90.77 | 91.43    | 74.33          | 79.71 | 76.62    |
| Llama 2                 | 66.27             | 64.06 | 60.41    | 88.83             | 88.83 | 92.27    | 59.92          | 63.85 | 73.06    |
| Llama 2 + CDA           | 64.03             | 67.24 | 60.19    | 86.42             | 89.02 | 90.41    | 62.17          | 58.33 | 71.98    |
| Llama 2 + INLP          | 63.97             | 62.5  | 60.33    | 85.41             | 90.04 | 88.98    | 61.55          | 67.53 | 70.60    |
| Llama 2 + Self-Debias   | 60.04             | 63.49 | 59.1     | 89.3              | 91.3  | 90.17    | 71.37          | 66.67 | 73.76    |
| Llama 2 + DICE          | 58.83             | 60.42 | 59.98    | 90.44             | 89.2  | 91.47    | 74.47          | 70.61 | 73.21    |