Anchored Alignment for Self-Explanations Enhancement

## Abstract

Abstract In this work, we introduce a methodology for alignment designed to enhance the ability of large language models (LLMs) to articulate their reasoning— self-explanation —even in the absence of annotated rationale explanations. Our alignment methodology comprises three key components: explanation quality assessment, self-instruction dataset generation, and model alignment. Additionally, we present a novel technique called Alignment with Anchor Preference Pairs , which improves the selection of preference pairs by categorizing model outputs into three groups: consistently correct, consistently incorrect, and variable. By applying tailored strategies to each category, we enhance the effectiveness of Direct Preference Optimization (DPO). Our experimental results demonstrate that this approach significantly improves explanation quality while maintaining accuracy compared to other fine-tuning strategies.

## 1 Introduction

Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, fine-tuning these models for specific applications often leads to a critical trade-off: improvements in one area may compromise the model’s generalization capabilities (Yang et al., ; Kirk et al., ). In our study, we aim to enhance a secondary task—specifically, the model’s ability to articulate reasoning processes in natural language, a skill known as self-explanation (Madsen et al., )—in parallel with the primary task, despite the constraint of not having human-annotated rationales.

The lack of annotated data of both high- and low-quality explanations can be framed in the context of model aligning without human preference data. Recent research has explored ways to align LLMs without direct human input. Some approaches generate self-instruct data to fine-tune models (Wang et al., ; Chen et al., ; Gulcehre et al., ), while others, like Bai et al. (); Yuan et al. (); Wu et al. (), use LLM-generated feedback to train reward models. Building on these advancements, we propose an end-to-end approach to align LLMs on classification tasks while also ensuring the generation of high-quality self-explanations, even without annotated data for this secondary task. Our approach integrates three core components: evaluating generated explanations, creating self-instruct datasets, and aligning the model. Additionally, we introduce Alignment with Anchor Preference Pairs, a method that improves preference pair selection by categorizing model responses into three groups: consistently correct, consistently incorrect, and variable. For each category, we apply tailored strategies to construct preference pairs, which are then used in the Direct Preference Optimization (DPO) phase (Rafailov et al., ). Our results demonstrate that this method consistently improves explanation quality, mitigating the degradation caused by SFT. Moreover, we show that using anchor preference pairs outperforms self-alignment strategies that rely solely on judge-based evaluations for preference pair selection.

Our contributions are summarized as follows:

- (i).
We introduce a framework for the qualitative assessment of self-explanations, designed to evaluate how effectively the model conveys its reasoning.
- (ii).
We analyze how supervised fine-tuning for classification tasks affects the quality of self-explanations. Our findings demonstrate that while SFT improves classification accuracy, it often reduces explanation quality, underscoring the need for improved alignment strategies.
- (iii).
We propose a novel method, Alignment with Anchor Preference Pairs, for constructing high-quality preference pairs when building self-instruct datasets. This method uses the model’s behavior on each input prompt to apply specific strategies while creating preference pairs. Our approach consistently outperforms other methods that rely solely on judge-based evaluations for selecting preference pairs.
- (iv).
We develop an end-to-end methodology for aligning LLMs to downstream classification tasks while maintaining the quality of their self-explanations, even in the absence of explanation-rich datasets.

## 2 A Framework for Qualitative Assessment of Self-Explanations

### 2.1 Quality Criteria for Effective Self-Explanations

To assess self-explanation quality, we focus on the model’s ability to effectively communicate its reasoning. This approach differs from previous work that emphasized trustworthiness metrics such as faithfulness (Madsen et al., ; ; Lanham et al., ; Lyu et al., ; Turpin et al., ; Parcalabescu & Frank, ) and truthfulness (Zhang et al., ; Sharma et al., ; Burns et al., ; Joshi et al., ). We evaluate self-explanations based on the following criteria:

- 1.
Logical coherence: The explanation should follow a clear and logical reasoning process, with all components cohesively connected to form a unified, non-contradictory narrative.
- 2.
Clarity: The explanation must present ideas clearly and precisely, using appropriate terminology to effectively communicate complex concepts without unnecessary complexity.
- 3.
Relevance: The explanation should comprehensively address the task at hand, directly answering the specific context or requirements without omitting critical information.
- 4.
Depth of argumentation: The explanation must provide strong reasoning and credible evidence to support its conclusions, reflecting a deep understanding of the task.
- 5.
Factual accuracy: This criterion assesses the correctness of individual claims within the explanation. While related to truthfulness, factual accuracy focuses on whether specific statements align with established knowledge.

### 2.2 Self-Explanations Evaluation Methodology

Let $\mathcal{M}$ represent a large language model tasked with generating responses for a classification problem. Each response consists of two components: a self-explanation, denoted as $\varepsilon_{i}$, and a predicted classification label, $\hat{y}_{i}$, corresponding to an input prompt $x_{i}$. The self-explanation $\varepsilon_{i}$ is produced by prompting the model to articulate its reasoning before providing a final prediction, following the Chain-of-Thought prompting strategy (Wei et al., ).

Our methodology is inspired by recent approaches that utilize LLMs as evaluators of other models’ outputs (Dubois et al., ; Li et al., ; Fernandes et al., ; Bai et al., ; Saha et al., ). This approach has shown versatility, extending beyond simple evaluation to various applications in model improvement and self-alignment strategies. For instance, researchers have employed this framework to generate self-instruct data for fine-tuning models (Wang et al., ; Chen et al., ; Gulcehre et al., ) and to create feedback for training reward models (Bai et al., ; Yuan et al., ; Wu et al., ).

For our evaluation, we employ a more capable model, $\mathcal{M}_{\text{Judge}}$, to assess the quality of self-explanations $\varepsilon_{i}$ based on predefined criteria (detailed in Section 2.1). The evaluation process proceeds as follows:

- 1.
For each criterion $\kappa$, $\mathcal{M}_{\text{Judge}}$ assigns a qualitative verdict $v_{i,\kappa}$ from the set {excellent, satisfactory, needs improvement, unsatisfactory}. The prompt used by $\mathcal{M}_{\text{Judge}}$ is provided in Appendix C.2.
- 2.
Each verdict $v_{i,\kappa}$ is mapped to a numerical score $s_{i,\kappa}$ (see Appendix C.1).
- 3.
The overall score for an explanation, $s_{i}$, is computed as the sum of scores across all criteria:
$s_{i}=\sum_{k=1}^{K}s_{i,k}$

### 2.3 Pairwise Model Evaluation

To assess the quality of self-explanations generated by different models, we adopt a pairwise evaluation strategy consistent with previous work (Chen et al., ; Yuan et al., ; Wu et al., ). For each input prompt $x_{i}$, we generate $N$ sample self-explanations, capturing the inherent variability in model outputs. The score for the $n$-th response is denoted as $s_{i}^{n}$, with the corresponding explanation and prediction represented by the pair $(\varepsilon_{i}^{n},\hat{y}_{i}^{n})$.

For a given prompt $x_{i}$, we conduct $N^{2}$ pairwise comparisons between the explanations generated by two models, $\mathcal{M}_{1}$ and $\mathcal{M}_{2}$. A win for model $\mathcal{M}_{1}$ is defined when:

$$ $s_{i}^{n}(\mathcal{M}_{1})>s_{i}^{m}(\mathcal{M}_{2})$ $$

where $n,m\in\{1,\ldots,N\}$. The overall win rate $W(\mathcal{M}_{1},\mathcal{M}_{2})$ is then calculated as follows:

$$ $W(\mathcal{M}_{1},\mathcal{M}_{2})=\frac{1}{|\mathcal{X}|}\sum_{x_{i}\in \mathcal{X}}\left(\frac{1}{N^{2}}\sum_{n=1}^{N}\sum_{m=1}^{N}\mathbb{1}[s_{i}^ {n}(\mathcal{M}_{1})>s_{i}^{m}(\mathcal{M}_{2})]\right)$ $$

Here, $\mathcal{X}$ denotes the set of all prompts, while $\mathbb{1}[\cdot]$ represents the indicator function that returns 1 if the condition is true and 0 otherwise. This approach facilitates a nuanced comparison of model performance by taking into account the distribution of explanation qualities, rather than relying solely on single-point estimates. The rates for ties, defined as $s_{i}^{n}(\mathcal{M}_{1})=s_{i}^{m}(\mathcal{M}_{2})$, and losses, defined as $s_{i}^{n}(\mathcal{M}_{1})<s_{i}^{m}(\mathcal{M}_{2})$, are computed in a similar manner (see Appendix A). Throughout the evaluations presented in this work, $\mathcal{M}_{2}$ refers to the baseline model $\mathcal{M}_{\text{base}}$.

## 3 Self-Explanation Alignment with Anchor Preference Pairs

In this section, we introduce a methodology for alignment designed to enhance the ability of large language models (LLMs) to articulate their reasoning—self-explanation—even in the absence of annotated rationale explanations. However, we assume access to human-annotated data in the form of classification datasets for domain-specific adaptation, reflecting a common constraint in real-world applications, where comprehensive explanation data is often scarce or prohibitively expensive compared to classification datasets.

Building on prior work (Bai et al., ; Wang et al., ; Yuan et al., ; Wu et al., ), our alignment methodology incorporates familiar components such as self-instruction dataset generation, human-free evaluation of candidate responses using LLM-as-Judge, preference pair selection, and model alignment.

However, our approach differs from previous methods in two key ways: First, for the assessment of candidate responses, we use the evaluation explanation quality framework introduced in Sections 2.1 and 2.2. Second, we propose a novel technique, Alignment with Anchor Preference Pairs, which improves preference pair selection by categorizing model outputs into three groups: consistently correct, consistently incorrect, and variable. By applying tailored strategies to each category, we enhance the effectiveness of DPO.

The steps of the methodology are as follows:

- 1.
Supervised fine-tuning of the base model $\mathcal{M}_{\text{Base}}$ specifically on a target classification task, resulting in $\mathcal{M}_{\text{SFT}}$.
- 2.
Instruct $\mathcal{M}_{\text{SFT}}$ to generate multiple explanation-prediction pairs for each prompt, and evaluate the quality of these self-explanations using the methodology outlined in Sections 2.1 and 2.2. During alignment, the base model $\mathcal{M}_{\text{Base}}$ acts as the judge $\mathcal{M}_{\text{Judge}}$, ensuring the process remains self-contained.
- 3.
Construct an alignment dataset by selecting preference pairs using an anchor-based strategy (see Section 3.3).
- 4.
Align $\mathcal{M}_{\text{SFT}}$ via DPO with the dataset created in the third step, producing the aligned model $\mathcal{M}_{\text{Anchor}}$.

### 3.1 Supervised Fine-Tuning without Annotated Explanations

We fine-tuned the base model, $\mathcal{M}_{\text{Base}}$, on classification datasets (the primary task) to obtain $\mathcal{M}_{\text{SFT}}$, simulating scenarios where explanation annotations are unavailable. To replicate typical domain-specific adaptations and avoid potential gains from multi-task learning, we fine-tuned a separate model for each task. During fine-tuning, loss was calculated only on the target tokens corresponding to the correct choice sentence, excluding the system instruction and question. We generated the full text of the selected option to provide richer context and preserve the model’s text generation capabilities. Details on datasets and training setups are provided in Section 4.1.

### 3.2 Self-Instruction Creation

We generate self-instruct data for alignment as follows:

- 1.
Generate candidate responses: We sample $N$ diverse pairs of explanations and predictions from $\mathcal{M}_{\text{SFT}}$, denoted as $\{\varepsilon_{i}^{n},\hat{y}_{i}^{n}\}_{n=1}^{N}$, where $\varepsilon_{i}^{n}$ represents the explanation for the $n$-th prediction $\hat{y}_{i}^{n}$ corresponding to the prompt $x_{i}$.
- 2.
Evaluate candidate responses: We use the methodology described in Section 2.2 to evaluate the self-explanations generated from the candidate responses, assigning a score $s_{i}^{n}$ to each explanation $\varepsilon_{i}^{n}$. During the creation of the self-instruct dataset, we employ $\mathcal{M}_{\text{base}}$ as the judge ($\mathcal{M}_{\text{Judge}}$). This ensures that the model alignment process remains self-contained, without the need for external models, except for evaluation purposes.

### 3.3 Preference Pairs via Anchor Selection

We introduce a method to enhance the selection of preference pairs by categorizing model responses into three groups: consistently correct, consistently incorrect, and variable. For each category, we apply specific strategies to construct preference pairs, which are then used in during the DPO phase. To evaluate the model’s consistency on a given input prompt, a ground truth reference, or anchor, is required. We use a classification task as the probing mechanism.

Preference Pairs for Consistently Correct Prompts: For input prompts $x_{i}$ where $\mathcal{M}_{\text{SFT}}$ consistently produces correct answers (i.e., $\hat{y}_{i}^{n}=y_{i}$ for all $n\in\{1,\ldots,N\}$), preference pairs are constructed based on the quality of the explanations. Let $s_{i}^{n}$ denote the score assigned by the judge $\mathcal{M}_{\text{Judge}}$ to the $n$-th explanation $\varepsilon_{i}^{n}$ for prompt $x_{i}$. We define two sets: $\mathbb{A}_{i}^{w}=\{\varepsilon_{i}^{n}:s_{i}^{n}=\max_{j\in\{1,\ldots,N\}}s_
{i}^{j}\}$, which contains all explanations that achieve the highest score for prompt $x_{i}$, and $\mathbb{A}_{i}^{l}=\{\varepsilon_{i}^{n}:s_{i}^{n}<\max_{j\in\{1,\ldots,N\}}s_
{i}^{j}\}$, which includes all explanations with scores lower than the maximum for prompt $x_{i}$.

Preference Pairs for Variable Performance: For input prompts $x_{i}$ where $\mathcal{M}_{\text{SFT}}$ produces a mix of correct and incorrect predictions (i.e., $\hat{y}_{i}^{n}\neq y_{i}$ for some $n\in\{1,\ldots,N\}$), preference pairs are constructed contrastively. We define the set $\mathbb{B}_{i}^{w}=\{\varepsilon_{i}^{n}:\hat{y}_{i}^{n}=y_{i}\}$, which contains explanations associated with correct predictions. From this set, we extract $\mathbb{A}_{i}^{w}\subseteq\mathbb{B}_{i}^{w}$, the subset of explanations with the highest scores assigned by $\mathcal{M}_{\text{Judge}}$, i.e., $\mathbb{A}_{i}^{w}=\{\varepsilon_{i}^{n}\in\mathbb{B}_{i}^{w}:s_{i}^{n}=\max_{
j\in\mathbb{B}_{i}^{w}}s_{i}^{j}\}$. The set $\mathbb{A}_{i}^{l}=\{\varepsilon_{i}^{n}:\hat{y}_{i}^{n}\neq y_{i}\text{ and }
s_{i}^{n}<\max_{j\in\mathbb{A}_{i}^{w}}s_{i}^{j}\}$ contains explanations corresponding to incorrect predictions, with scores lower than the maximum score in $\mathbb{A}_{i}^{w}$.

Preference Pairs for Consistently Incorrect Prompts: For prompts where all predictions from $\mathcal{M}_{\text{SFT}}$ are incorrect (i.e., $\hat{y}_{i}^{n}\neq y_{i}$ for all $n\in\{1,\ldots,N\}$), all corresponding explanations are placed in the set $\mathbb{A}_{i}^{l}$. To generate a winning explanation, we employ the $\mathcal{M}_{\text{Base}}$ model in a consultant role, similar to the LLM-as-a-Debater approach proposed by Khan et al. (). Since the inference hyperparameters for the LLM in this consulting role might differ from those used during the generation of preference pairs, we refer to this model as $\mathcal{M}_{\text{Debater}}$ to avoid confusion. Specifically, we provide the correct answer $y_{i}$ to the LLM and request an argument supporting this answer, which is then assigned to the set $\mathbb{A}_{i}^{w}$ as the winning explanation.

Finally, preference pairs are constructed for each instruction prompt $x_{i}$ by randomly sampling $\varepsilon_{i}^{w}$ from $\mathbb{A}_{i}^{w}$ as the winning explanation and $\varepsilon_{i}^{l}$ from $\mathbb{A}_{i}^{l}$ as the losing explanation. The resulting preference pair is denoted as $(x_{i},\varepsilon_{i}^{w},\varepsilon_{i}^{l})$. The detailed algorithm is presented in Algorithm 1.

Figure: Algorithm 1 Generating Preference Pairs Via Anchor Selection

## 4 Experiments

In all experiments, we utilized Llama-3-8B-Instruct as our base model. Our study involved comparing four distinct model configurations:

- 1.
$\mathcal{M}_{\text{Base}}$: The base model, which remains unmodified.
- 2.
$\mathcal{M}_{\text{SFT}}$: This model was obtained by performing supervised fine-tuning on the $\mathcal{M}_{\text{Base}}$ model using only classification tasks, simulating scenarios where explanation annotations are not available.
- 3.
$\mathcal{M}_{\text{Rank}}$: The $\mathcal{M}_{\text{SFT}}$ model was further refined using DPO, employing a self-instruct dataset constructed from rank-ordered preference pairs derived solely from judge-based evaluations of the explanations. This approach aligns with methodologies described in Bai et al. (); Wang et al. (); Yuan et al. (); Wu et al. ().
- 4.
$\mathcal{M}_{\text{Anchor}}$ (ours): Similar to $\mathcal{M}_{\text{Rank}}$, this model was refined using DPO but utilized a self-instruct dataset created with our proposed anchored method for selecting preference pairs.

Since both $\mathcal{M}_{\text{Rank}}$ and $\mathcal{M}_{\text{Anchor}}$ undergo an additional stage of DPO alignment with the self-instruct dataset, we will collectively refer to them as self-aligned models in comparison to $\mathcal{M}_{\text{SFT}}$.

### 4.1 Experimental Setup

Datasets: We selected four datasets for our experiments: AQuA-Rat (Ling et al., ), ARC-Challenge (Clark et al., ), LogiQA (Liu et al., ), and OpenbookQA (Mihaylov et al., ). These datasets are established benchmarks for reasoning tasks, requiring a challenging reasoning process, which makes them an ideal fit for evaluating the quality of self-explanations. A key factor in their selection was the size of their training sets, which provided a sufficient number of input prompts to support the creation of the self-instruction dataset. In the case of AQuA-Rat, we sampled 5,000 examples due to computational constraints. For evaluation, we used the test split of each dataset.

SFT Training Details:
For $\mathcal{M}_{\text{SFT}}$, we used the AdamW optimizer with a learning rate of $5\times 10^{-5}$ for one epoch, following a cosine schedule with 10% warmup steps. Gradient clipping was set to 0.3, and we used an effective batch size of 12. Loss was computed only on the assistant’s completions. Instead of fine-tuning the entire model, we applied a LoRA adapter ($\alpha=128$, dropout = 0.05, rank $r=256$) to all linear layers. LoRA adapters were used to accelerate training and to act as a regularization method (Biderman et al., ), addressing the overfitting tendencies of DPO (Thakkar et al., ), which is applied during the later alignment phase.

Self-Instruct Dataset: To ensure the integrity of our evaluation process, we created separate self-instruct datasets for each benchmark. These datasets were built using input prompts specific to each task, ensuring that the DPO alignment data remained uncontaminated by exposure to multiple tasks. This approach prevents the artificial inflation of results that could occur if models were aligned across diverse tasks, unlike SFT models, which are trained on one classification task at a time.
To create the self-instruct dataset for aligning $\mathcal{M}_{\text{Rank}}$ and $\mathcal{M}_{\text{Anchor}}$, we sampled $N=4$ responses from $\mathcal{M}_{\text{SFT}}$ for each input prompt (settings: temperature $T=0.6$ and top-k value of $0.9$). This sample size provided a reasonable assessment of the model’s consistency and variability. The prompt used is presented in Appendix D. In cases of consistently incorrect responses, $\mathcal{M}_{\text{Base}}$ was employed as the debater model (Llama-3-8B-Instruct) with adjusted parameters ($T=0.5$, top-k $0.9$). The responses were scored by $\mathcal{M}_{\text{Judge}}$, which was the same base model, ensuring a self-contained alignment process. This setup differs from the evaluation phase, where a more capable model serves as the judge. The scoring of explanations followed the methodology outlined in Section 2.2, with $\mathcal{M}_{\text{Judge}}$ using fixed inference parameters ($T=0$). For $\mathcal{M}_{\text{Rank}}$, preference pairs were chosen based on the assigned scores, with the highest-scoring explanation designated as the winner, and the losing explanation randomly selected from the remaining candidates. The preference pairs for $\mathcal{M}_{\text{Anchor}}$ were selected using the methodology outlined in Section 3.3, and these pairs were then used to align the models through DPO.

DPO Training Details:
For DPO-aligned models ($\mathcal{M}_{\text{Rank}}$, $\mathcal{M}_{\text{Anchor}}$), we used similar hyperparameters as in the SFT phase but reduced the learning rate to $5\times 10^{-7}$ and trained for 2.6k steps with an effective batch size of 6. The DPO process used a $\beta$ value of 0.1 and updated the LoRA weights obtained during SFT.

Evaluation: We evaluated our models along two main dimensions: prediction accuracy and self-explanation quality. To account for variability in model outputs, we generated $N=16$ explanation-prediction pairs per input prompt. The inference settings mirrored those used to create the self-instruction dataset, with a temperature of $T=0.6$ and top-k set to $0.9$ and the same prompt used during the creation of the self-instruct dataset (see Appendix D.3). Average prediction accuracy was used to measure performance on downstream tasks. To assess self-explanation quality, we performed head-to-head comparisons between the aligned models ($\mathcal{M}_{\text{SFT}}$, $\mathcal{M}_{\text{Rank}}$, $\mathcal{M}_{\text{Anchor}}$) and the base model ($\mathcal{M}_{\text{Base}}$). These comparisons followed the methodology outlined in Section 2.2, employing Llama-3-70B-Instruct as $\mathcal{M}_{\text{Judge}}$.

### 4.2 Results

Table 1 reports the average classification accuracy for each model, along with pairwise comparisons of self-explanation quality across multiple benchmark datasets. The win, tie, and loss rates are calculated by comparing the aligned models against $\mathcal{M}_{\text{Base}}$.

**Table 1: Comparison of Aligned Models. Average accuracy is presented along with head-to-head comparisons for self-explanation quality. The results show that $\mathcal{M}_{\text{Anchor}}$ achieves comparable or superior accuracy and attains the highest win rate when compared to $\mathcal{M}_{\text{SFT}}$ and $\mathcal{M}_{\text{Rank}}$.**
| Dataset | $\mathcal{M}_{\text{Base}}$<br>Acc. (%) | $\mathcal{M}_{\text{Align}}$<br>Type | $\mathcal{M}_{\text{Align}}$<br>Acc. (%) | $\varepsilon$ Win Rate Eval. (%) | $\Delta_{W-L}$ |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Win $\uparrow$ | Tie | Loss $\downarrow$ |  |  |  |  |  |
|  |  | $\mathcal{M}_{\text{SFT}}$ | $47.7_{\pm 2.7}$ | $11.3$ | $61.9$ | $26.9$ | -15.6 |
| AQuA Rat | $47.1_{\pm 2.9}$ | $\mathcal{M}_{\text{Rank}}$ | $48.3_{\pm 2.1}$ | $11.5$ | $63.1$ | $25.4$ | -13.9 |
|  |  | $\mathcal{M}_{\text{Anchor}}$ | $51.1_{\pm 3.0}$ | $12.6$ | $70.8$ | $16.7$ | -4.1 |
|  |  | $\mathcal{M}_{\text{SFT}}$ | $81.0_{\pm 0.7}$ | $9.6$ | $54.4$ | $36.0$ | -26.4 |
| ARC-Challenge | $76.4_{\pm 0.7}$ | $\mathcal{M}_{\text{Rank}}$ | $81.9_{\pm 1.1}$ | $17.72$ | $61.4$ | $20.9$ | -3.18 |
|  |  | $\mathcal{M}_{\text{Anchor}}$ | $82.0_{\pm 0.9}$ | $21.5$ | $60.3$ | $18.2$ | 3.3 |
|  |  | $\mathcal{M}_{\text{SFT}}$ | $45.2_{\pm 0.7}$ | $14.7$ | $39.8$ | $45.6$ | -30.9 |
| LogiQA | $41.4_{\pm 1.1}$ | $\mathcal{M}_{\text{Rank}}$ | $46.0_{\pm 1.5}$ | $22.0$ | $50.1$ | $27.9$ | -5.9 |
|  |  | $\mathcal{M}_{\text{Anchor}}$ | $46.6_{\pm 2.2}$ | $26.6$ | $53.8$ | $19.7$ | 6.9 |
|  |  | $\mathcal{M}_{\text{SFT}}$ | $87.4_{\pm 1.1}$ | $11.3$ | $54.0$ | $34.6$ | -23.3 |
| OpenbookQA | $71.7_{\pm 1.3}$ | $\mathcal{M}_{\text{Rank}}$ | $87.0_{\pm 1.1}$ | $15.4$ | $60.1$ | $24.5$ | -9.1 |
|  |  | $\mathcal{M}_{\text{Anchor}}$ | $87.0_{\pm 0.9}$ | $16.7$ | $59.6$ | $23.7$ | -7 |

#### 4.2.1 Impact of Supervised Fine-Tuning on Self-Explanations

We observed a significant trade-off in evaluation results before and after applying supervised fine-tuning on a classification task (see Table 1). While SFT notably improved classification accuracy, it resulted in a substantial decline in the quality of self-explanations compared to the base model. The decline in explanation quality, as measured by the win-loss rate difference ($\Delta_{W-L}$), ranged from 15.6% to 30.9% across benchmarks.

Building on evidence that supervised fine-tuning can improve performance on specific tasks at the expense of a model’s generalization abilities (Yang et al., ; Kirk et al., ), we hypothesize that this decline occurs because the task of selecting predefined answers does not inherently encourage the model to articulate its reasoning, leading to a specialization that diminishes the quality of the generated explanations.

These findings highlight the necessity for alignment techniques that can preserve high-quality explanations in situations where datasets with annotated explanations are unavailable for fine-tuning.

#### 4.2.2 Analysis of Self-Aligned Models

Prediction Accuracy: Our experiments demonstrate that the self-aligned models, $\mathcal{M}_{\text{Rank}}$ and $\mathcal{M}_{\text{Anchor}}$, maintain the classification accuracy improvements achieved by the seed model, $\mathcal{M}_{\text{SFT}}$, over the base model, $\mathcal{M}_{\text{Base}}$ (see Table 1). Notably, $\mathcal{M}_{\text{Anchor}}$ consistently achieves the highest, or at least comparable, accuracy across all datasets. We believe that this improvement can be attributed to the fact that the model’s predictions ($\hat{y}_{i}$) are compared to the ground truth ($y_{i}$) to determine how to treat the self-explanation $(e_{i}$) during the selection of preference pairs while employing the anchor strategy, thereby providing a more informative learning signal.

Self-Explanation Quality: Pairwise evaluations of self-explanation quality (see Table 1) indicate that the initial decline in explanation performance observed in $\mathcal{M}_{\text{SFT}}$ is partially inherited by both $\mathcal{M}_{\text{Rank}}$ and $\mathcal{M}_{\text{Anchor}}$, as they use $\mathcal{M}_{\text{SFT}}$ as the seed model during the DPO alignment phase. Nevertheless, both $\mathcal{M}_{\text{Rank}}$ and $\mathcal{M}_{\text{Anchor}}$ demonstrate significant improvements in explanation quality compared to $\mathcal{M}_{\text{SFT}}$, with $\mathcal{M}_{\text{Anchor}}$ exhibiting the strongest performance. Compared to the base model, $\mathcal{M}_{\text{Anchor}}$ also shows positive $\Delta_{W-L}$ margins on ARC-Challenge (3.3%) and LogiQA (6.9%), and it significantly narrows the gap in explanation quality introduced by SFT across the remaining benchmark datasets.

Figure: Figure 1: Average Self-Explanation Scores per Evaluation Criterion. Average scores for each evaluation criterion used to assess self-explanations, as described in Section 2.1. The scores are provided for all evaluated models across the benchmark datasets.
Refer to caption: x1.png

#### 4.2.3 Analysis of Individual Evaluation Dimensions

Figure 1 presents the average scores for each evaluation criterion used to assess self-explanations, as described in Section 2.1, for all evaluated models across the benchmark datasets.

Overall, the self-aligned models outperform $\mathcal{M}_{\text{SFT}}$ across all evaluation criteria, with $\mathcal{M}_{\text{Anchor}}$ consistently achieving better results than $\mathcal{M}_{\text{Rank}}$.

Additionally, we observe that the degradation in self-explanation quality due to SFT varies significantly depending on the dataset used for fine-tuning. Two notable trends emerge from the analysis. First, for more complex tasks—where complexity is measured by lower test accuracy—such as AQuA-Rat and LogiQA, the decline in explanation quality is more pronounced across all criteria. Second, evaluation dimensions for which the base model originally received lower scores tend to experience a more significant drop in performance after SFT.

#### 4.2.4 Impact of Preference Pairs Category Distribution

We define $\lambda$ as the proportion of the self-instruct dataset used to align $\mathcal{M}_{\text{Anchor}}$, which corresponds to preference pairs selected under the consistently-incorrect or variable strategies (see Section 3.3). This metric, $\lambda$, provides insight into how the composition of the self-instruct dataset for $\mathcal{M}_{\text{Anchor}}$ differs from that of $\mathcal{M}_{\text{Rank}}$, which selects pairs based solely on scores assigned by judges, following the consistently-correct strategy. We evaluated improvements by analyzing the differences in accuracy and $\Delta_{W-L}$ between $\mathcal{M}_{\text{Anchor}}$ and $\mathcal{M}_{\text{Rank}}$ in relation to $\lambda$ (see Figure 2). In both cases, we observed a trend showing that $\mathcal{M}_{\text{Anchor}}$ demonstrates a greater relative improvement compared to $\mathcal{M}_{\text{Rank}}$ as $\lambda$ increases. This supports our design principle that tailoring strategies based on model behavior is crucial for improving the quality of self-instruct datasets and avoiding the reinforcement of problematic behavior.

Figure: Figure 2: Impact of Preference Pairs Category Distribution: Presents the relative improvements in accuracy (left) and $\Delta_{W-L}$ (right) between $\mathcal{M}_{\text{Anchor}}$ and $\mathcal{M}_{\text{Rank}}$ with respect to $\lambda$.
Refer to caption: x2.png

## 5 Related Work

LLM-as-Evaluator: This concept refers to the ability of large language models (LLMs) to evaluate the outputs of other LLMs, a technique commonly referred to as LLM-as-a-Judge. This approach has gained considerable traction in recent years (Dubois et al., ; Li et al., ; Fernandes et al., ; Bai et al., ) and is frequently used to assess LLM performance across various downstream tasks (Zheng et al., ). It has proven particularly effective in automating evaluations, as demonstrated on platforms like LMSys Chatbot Arena. Key implementations include direct scoring based on specific criteria, pairwise comparisons (Liu et al., ), reference-based evaluations, and ensemble methods (Verga et al., ). While LLM-as-a-Judge offers scalability and consistency, it can also inherit biases from the evaluation model, potentially amplifying problematic outputs (Huang et al., ). Despite these challenges, it remains a valuable tool due to its efficiency and cost-effectiveness in evaluating LLM systems. In our work, we introduce a framework for the qualitative assessment of self-explanations using the LLM-as-a-Judge technique, designed to evaluate how effectively a model conveys its reasoning.

Self-Alignment: Several approaches have been developed to improve LLMs without requiring human-annotated feedback. One method involves fine-tuning models using high-quality, self-generated input-output pairs (Wang et al., ; Chen et al., ; Gulcehre et al., ), though this can perpetuate biases in example selection without a clear mechanism for improving selection quality. Another influential approach is Constitutional AI (Bai et al., ), where an LLM provides feedback and refines responses, which are then used to train a separate, static reward model. Building on this concept, Yuan et al. () and Wu et al. () proposed using the LLM itself as a dynamic reward model, eliminating the need for a static one. This allows for continuous improvement in both generation and evaluation capabilities through iterative training processes. In our work, we introduce a novel method for creating a self-instruct dataset. Our approach, called Alignment with Anchor Preference Pairs, enhances preference pair selection by categorizing model behavior in response to each input prompt and applying tailored strategies for each category. To evaluate a model’s consistency for a given input prompt, an anchor—i.e., a ground-truth reference—is required, which we derive from a classification task used as a probing mechanism.

LLM-as-a-Debater: This adversarial approach aims to improve model performance through argumentation. In Perez et al. (), debaters are limited to extracting relevant statements from a source text, rather than generating original arguments. Du et al. () extended this concept by involving multiple LLM instances to debate their individual responses over several rounds, eventually converging on a shared final answer. Khan et al. () further developed this approach by using debate-like scenarios to challenge and refine model outputs through simulated arguments. In our work, we adopt the LLM-as-a-Debater approach in the role of a consultant, specifically following Khan et al. (), for cases where the model’s response to certain input prompts is consistently incorrect. This strategy enables the creation of self-instruct examples that avoid reinforcing problematic behavior.

## 6 Limitations

We acknowledge some limitations in our approach. First, evaluating the model’s consistency on a given input prompt requires a anchor—ground truth reference. Consequently, the selection of preference pairs via the anchor strategy relies on a classification task as the probing mechanism, which restricts its applicability. Second, when ranking the quality of self-explanations, we assign equal weights across all evaluation dimensions. This uniform weighting may not accurately reflect the varying significance of different aspects of explanation quality, which can differ depending on the user or specific application. Moreover, this approach may overlook instances where individual explanations degrade in separate criteria, potentially leading to preference pairs where score differences arise from unrelated factors.

Finally, using the base model as the judge during the creation of the self-instruct dataset eliminates the need for a more capable model but introduces a static evaluation process. As the model improves, the judge may fail to capture important evaluation nuances. Iteratively enhancing the judge’s capabilities, similar to the approaches in Yuan et al. () and Wu et al. (), could help mitigate this issue.

## 7 Conclusion

In this work, we introduced a methodology for alignment that enhances LLMs’ ability to generate high-quality self-explanations, even in the absence of annotated rationale explanations. Our approach provides an end-to-end solution for aligning LLMs on classification tasks, ensuring that they not only produce accurate predictions but also articulate coherent explanations for their decisions. This is achieved through three core components: evaluating the quality of generated explanations, creating self-instruct datasets, and aligning the model. Central to our approach is Alignment with Anchor Preference Pairs, a novel method that refines preference pair selection by categorizing model outputs into three groups: consistently correct, consistently incorrect, and variable. For each category, we apply tailored strategies to construct preference pairs, which are then used in DPO. Our empirical results demonstrate that this method consistently improves explanation quality, reducing the degradation caused by task specialization. Furthermore, we show that using anchor preference pairs outperforms self-alignment methods that rely solely on judge-based evaluations for preference pair selection.

## Appendix A Pairwise Model Evaluation

To compare the performance of two models, denoted as $\mathcal{M}_{1}$ and $\mathcal{M}_{2}$, we perform a pairwise evaluation of the self-explanations generated for a given prompt $x_{i}$. Each model produces $N$ explanations, and we compare each explanation from $\mathcal{M}_{1}$ with every explanation from $\mathcal{M}_{2}$, resulting in $N^{2}$ pairwise comparisons.

For a given comparison between the $n$-th explanation from model $\mathcal{M}_{1}$ and the $m$-th explanation from model $\mathcal{M}_{2}$, where $n,m\in\{1,\ldots,N\}$, we compare the corresponding scores, $s_{i}^{n}(\mathcal{M}_{1})$ and $s_{i}^{m}(\mathcal{M}_{2})$. A win for $\mathcal{M}_{1}$ is recorded if the score from $\mathcal{M}_{1}$ is strictly greater than that from $\mathcal{M}_{2}$:

$$ $s_{i}^{n}(\mathcal{M}_{1})>s_{i}^{m}(\mathcal{M}_{2})$ $$

Conversely, a loss for $\mathcal{M}_{1}$ occurs if the score from $\mathcal{M}_{1}$ is strictly less than the score from $\mathcal{M}_{2}$:

$$ $s_{i}^{n}(\mathcal{M}_{1})<s_{i}^{m}(\mathcal{M}_{2})$ $$

A tie is defined when both scores are equal:

$$ $s_{i}^{n}(\mathcal{M}_{1})=s_{i}^{m}(\mathcal{M}_{2})$ $$

For each prompt $x_{i}$, we count the total number of wins, losses, and ties across all $N^{2}$ comparisons between the explanations from both models. To summarize the performance of the models across the entire dataset, we compute the win rate, tie rate, and loss rate.

The win rate $W(\mathcal{M}_{1},\mathcal{M}_{2})$ is the average proportion of pairwise comparisons in which model $\mathcal{M}_{1}$ outperforms model $\mathcal{M}_{2}$ across all prompts in the set $\mathcal{X}$. It is computed as:

$$ $W(\mathcal{M}_{1},\mathcal{M}_{2})=\frac{1}{|\mathcal{X}|}\sum_{x_{i}\in \mathcal{X}}\left(\frac{1}{N^{2}}\sum_{n=1}^{N}\sum_{m=1}^{N}\mathbb{1}[s_{i}^ {n}(\mathcal{M}_{1})>s_{i}^{m}(\mathcal{M}_{2})]\right)$ $$

Here, $\mathcal{X}$ is the set of all prompts, and $\mathbb{1}[\cdot]$ is the indicator function, which returns 1 if the condition inside the brackets is true (i.e., if $\mathcal{M}_{1}$ wins) and 0 otherwise.

Similarly, we define the tie rate $T(\mathcal{M}_{1},\mathcal{M}_{2})$ as the proportion of pairwise comparisons where the models perform equally:

$$ $T(\mathcal{M}_{1},\mathcal{M}_{2})=\frac{1}{|\mathcal{X}|}\sum_{x_{i}\in \mathcal{X}}\left(\frac{1}{N^{2}}\sum_{n=1}^{N}\sum_{m=1}^{N}\mathbb{1}[s_{i}^ {n}(\mathcal{M}_{1})=s_{i}^{m}(\mathcal{M}_{2})]\right)$ $$

The loss rate $L(\mathcal{M}_{1},\mathcal{M}_{2})$ captures the proportion of comparisons where $\mathcal{M}_{1}$ performs worse than $\mathcal{M}_{2}$:

$$ $L(\mathcal{M}_{1},\mathcal{M}_{2})=\frac{1}{|\mathcal{X}|}\sum_{x_{i}\in \mathcal{X}}\left(\frac{1}{N^{2}}\sum_{n=1}^{N}\sum_{m=1}^{N}\mathbb{1}[s_{i}^ {n}(\mathcal{M}_{1})<s_{i}^{m}(\mathcal{M}_{2})]\right)$ $$

By evaluating the win, tie, and loss rates, we obtain a comprehensive picture of how the two models compare in terms of generating higher-quality self-explanations. This approach accounts for variability in the quality of explanations generated by the models, providing a more nuanced and detailed evaluation than methods relying solely on single-point estimates.

## Appendix B Inference Parameters

Table 2 summarizes the inference parameters, including temperature and top-k, used for each component, such as the judge, consultant, and sampler.

**Table 2: Inference parameters per component**
| Component | Temperature | Top-k |
| --- | --- | --- |
| Judge | $0.0$ |  |
| Consultant | $0.5$ | $0.9$ |
| Sampler | $0.6$ | $0.9$ |

## Appendix C Judge Component

The judge model $\mathcal{M}_{\text{Judge}}$ evaluates the quality of self-explanation, denoted as $\varepsilon_{i}$, associated with an input prompt $x_{i}$. based on predefined criteria, which are elaborated in Section 2.1. The evaluation process proceed as follows:

- 1.
For each criterion $\kappa$, $\mathcal{M}_{\text{Judge}}$ assigns a qualitative verdict $v_{i,\kappa}$ from the set {excellent, satisfactory, needs improvement, unsatisfactory}. The prompt used by $\mathcal{M}_{\text{Judge}}$ is provided in Appendix C.2.
$\mathcal{M}_{\text{Judge}}(x_{i},\varepsilon_{i})\rightarrow\{v_{i,\kappa}\}
\text{ for }\kappa\in\{1,\dots,K\}$
- 2.
Each verdict $v_{i,\kappa}$ is mapped to a numerical score $s_{i,\kappa}$ (see Appendix C.1).
- 3.
The overall score for an explanation, $s_{i}$, is computed as the sum of scores across all criteria:
$s_{i}=\sum_{k=1}^{K}s_{i,k}$

### C.1 Judge Score Mapping

Each verdict $v_{i,\kappa}$, assigned by $\mathcal{M}_{\text{Judge}}$ for criterion $\kappa$ on self-explanation $\varepsilon_{i}$ corresponding to prompt $x_{i}$, is mapped to a numerical score $s_{i,\kappa}$ as follows:

$$ $s_{i,\kappa}=\begin{cases}1.0&\text{if }v_{i,\kappa}=\textbf{{Excellent}},\\ 0.8&\text{if }v_{i,\kappa}=\textbf{{Good}},\\ 0.6&\text{if }v_{i,\kappa}=\textbf{{Satisfactory}},\\ 0.2&\text{if }v_{i,\kappa}=\textbf{{Needs Improvement}},\\ 0.0&\text{if }v_{i,\kappa}=\textbf{{Unsatisfactory}}.\end{cases}$ $$

In this mapping, scores $s_{i,\kappa}\in\{1.0,0.8,0.6,0.2,0.0\}$ represent the numerical evaluation of the verdict, with higher values indicating better performance.

### C.2 Judge prompt

Table 3 presents the complete prompt that guides the judge model in evaluating the self-explanations.

**Table 3: Judge prompt.**
| Prompt Judge |
| --- |
| System: You are an expert judge tasked with evaluating the quality and correctness of a STATEMENT in response to a given QUESTION.<br>Your goal is to assess whether the STATEMENT accurately and comprehensively answers the QUESTION while providing sound reasoning and clear explanations. Be vigilant for any errors, misleading information, or gaps in the reasoning.<br>Evaluate the STATEMENT based on the following criteria:<br>•<br>Factual Accuracy: Are all specific claims, data points, and facts in the STATEMENT verifiably correct?<br>•<br>Logical Coherence: Does the STATEMENT follow a clear, logical sequence of reasoning without contradictions, and do all parts logically connect to form a cohesive and unified explanation?<br>•<br>Clarity and Comprehensibility: Is the STATEMENT expressed clearly and precisely, using appropriate terminology and effectively explaining complex ideas?<br>•<br>Relevance and Completeness: Does the STATEMENT fully address all relevant aspects of the QUESTION, ensuring that it directly responds to the specific context or requirements, leaving no key details omitted?<br>•<br>Depth of Argumentation: Does the STATEMENT provide strong reasoning and credible evidence to support its conclusions?<br>For each criterion, select one of the following assessments:<br>•<br>EXCELLENT: The STATEMENT exemplifies this criterion, setting a high standard.<br>•<br>GOOD: The STATEMENT meets this criterion well, with only minor room for improvement.<br>•<br>SATISFACTORY: The STATEMENT adequately meets this criterion but has noticeable areas for enhancement.<br>•<br>NEEDS IMPROVEMENT: The STATEMENT partially meets this criterion but requires significant revisions.<br>•<br>UNSATISFACTORY: The STATEMENT fails to meet this criterion.<br>Provide the assessments (EXCELLENT/GOOD/SATISFACTORY/NEEDS IMPROVEMENT/UNSATISFACTORY) for each criterion without additional justification. Respond with the following structure:<br>•<br>Factual Accuracy: [Assessment]<br>•<br>Logical Coherence: [Assessment]<br>•<br>Clarity: [Assessment]<br>•<br>Relevance: [Assessment]<br>•<br>Depth of Argumentation: [Assessment] |
| User: Here is the question:<br>[QUESTION]<br>Here is the statement you need to evaluate:<br>[STATEMENT] |

### C.3 Consultant Component

In cases where the model $\mathcal{M}_{\text{SFT}}$ behaves consistently incorrectly for the input prompt $x_{i}$, we employ the model $\mathcal{M}_{\text{Base}}$ in a consultant role. Specifically, we provide the correct answer $y_{i}$ to the LLM and request an explanation $\varepsilon_{i}$ supporting this answer.

$$ $\mathcal{M}_{\text{Debater}}(x_{i},y_{i})\rightarrow\varepsilon_{i}$ $$

### C.4 Consultant prompt

**Table 4: Consultant prompt.**
| Consultant prompt |
| --- |
| System: You will act as an expert consultant. You will be shown a QUESTION and one OPTION to argue for. Your goal is to construct convincing arguments supporting your option as correct.<br>Maintain a scholarly yet assertive tone, focusing on evidence-based analysis while crafting your arguments and answering questions. Avoid phrases like "In summary", "In conclusion", or similar at the end of your argument. Be as concise as possible.<br>Your argument must fulfill the following criteria:<br>•<br>Truthfulness: Any factual information mentioned in your argument must be true and accurate.<br>•<br>Logical Consistency: Your argument must be logically sound, free of logical fallacies or contradictions.<br>•<br>Clarity: Your argument directly addresses and answers the question. It is clear, concrete, concise, and well-structured. |
| User: Here is the question:<br>[QUESTION]<br>Here is option you need to back up:<br>[OPTION] |

## Appendix D Prompts inference per task

**Table 5: Prompt for LogiQA task.**
| Prompt for LogiQA task |
| --- |
| System: You will be presented with a CONTEXT passage and a corresponding QUESTION with four answer CHOICES.<br>Carefully read the passage to understand its content. Then, read the QUESTION and CHOICES thoroughly. Choose the correct CHOICE and explain your reasoning.<br>Your response will consist of two parts: an EXPLANATION followed by your selected CHOICE.<br>Enclose your explanation within tags as follows:<br><explanation>[Your EXPLANATION here]</explanation><br>Enclose your chosen choice (e.g., if the question has only 4 choices, then A, B, C, or D) within tags as follows:<br><choice>[Your CHOICE here]</choice> |
| User:<br>Context:<br>[CONTEXT]<br>Question:<br>[QUESTION]<br>Choices:<br>[CHOICES] |

### D.1 Prompt for AQuA-Rat task

**Table 6: Prompt for AQuA-Rat task.**
| Prompt for AQuA-Rat task |
| --- |
| System: You will be given a QUESTION along with multiple answer CHOICES, involving a math problem that requires step-by-step reasoning to determine the correct answer.<br>Carefully read the QUESTION and CHOICES. Choose the correct CHOICE and explain your reasoning.<br>Your response will consist of two parts: an EXPLANATION followed by your selected CHOICE.<br>Enclose your explanation within tags as follows:<br><explanation>[Your EXPLANATION here]</explanation><br>Enclose your chosen choice (e.g., if the question has only 4 choices, then A, B, C, or D) within tags as follows:<br><choice>[Your CHOICE here]</choice> |
| User:<br>Context:<br>[CONTEXT]<br>Question:<br>[QUESTION]<br>Choices:<br>[CHOICES] |

### D.2 Prompt for ARC-Challenge task

**Table 7: Prompt for ARC-Challenge task.**
| Prompt for ARC-Challenge task |
| --- |
| System: You will be presented a QUESTION with multiple answer CHOICES.<br>Carefully read the QUESTION and CHOICES. Choose the correct CHOICE and explain your reasoning.<br>Your response will consist of two parts: an EXPLANATION followed by your selected CHOICE.<br>Enclose your explanation within tags as follows:<br><explanation>[Your EXPLANATION here]</explanation><br>Enclose your chosen choice (e.g., if the question has only 4 choices, then A, B, C, or D) within tags as follows:<br><choice>[Your CHOICE here]</choice> |
| User:<br>Context:<br>[CONTEXT]<br>Question:<br>[QUESTION]<br>Choices:<br>[CHOICES] |

### D.3 Prompt for OpenbookQA task

**Table 8: Prompt for OpenbookQA task.**
| Prompt for OpenbookQA task |
| --- |
| System: You will be presented a QUESTION with multiple answer CHOICES.<br>Carefully read the QUESTION and CHOICES. Choose the correct CHOICE and explain your reasoning.<br>Your response will consist of two parts: an EXPLANATION followed by your selected CHOICE.<br>Enclose your explanation within tags as follows:<br><explanation>[Your EXPLANATION here]</explanation><br>Enclose your chosen choice (e.g., if the question has only 4 choices, then A, B, C, or D) within tags as follows:<br><choice>[Your CHOICE here]</choice> |
| User:<br>Context:<br>[CONTEXT]<br>Question:<br>[QUESTION]<br>Choices:<br>[CHOICES] |

## Appendix E Generated Instructions

**Table 9: Distribution of anchor categories: This table presents the distribution of the categories—Consistently Correct (CC), Consistently Incorrect (CI), and Variable (V)—across datasets used during the DPO alignment phase of $\mathcal{M}_{\text{Anchor}}$ .**
| Dataset | Category | Samples | Ratio (%) |
| --- | --- | --- | --- |
| AQuA-Rat | V | $1196$ | $41.17$ |
| CC | $1010$ | $34.77$ |  |
| CI | $699$ | $24.06$ |  |
| ARC-Challenge | V | $62$ | $8.09$ |
| CC | $645$ | $84.20$ |  |
| CI | $59$ | $7.70$ |  |
| LogiQA | V | $1251$ | $26.86$ |
| CC | $2487$ | $53.39$ |  |
| CI | $920$ | $19.75$ |  |
| OpenbookQA | V | $176$ | $5.13$ |
| CC | $3178$ | $92.60$ |  |
| CI | $78$ | $2.27$ |  |

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 5
presentation: 3
questions: - It would be helpful to test the proposed method with one or two more LLMs to demonstrate its robustness and applicability.
- Can the lambda in line 423 be optimized as a hyperparameter? In the current setup, lambda is chosen from the dataset, but controlling lambda as a hyperparameter (by adding a condition after line 18 in Algorithm 1) might improve model performance, especially given Figure 2’s positive correlation between lambda and model performance.
- Why does the performance of M_{Anchor} vary across the four datasets? While lambda partially explains these variations, understanding other influencing factors could reveal ways to further improve performance.
- Are the symbols “s” in lines 113 and 193 referring to the same concept?
soundness: 3
strengths: The main strength of this paper lies in its introduction of a novel and well-executed modeling of self-explanation alignment. The concept is both sound and straightforward, making it applicable to other QA tasks and LLM models. In terms of clarity, the paper is generally well-written, though certain aspects could benefit from further elaboration.
summary: This paper introduces a new self-explanation alignment method, called Alignment with Anchor Preference Pairs, to improve both exact answer prediction and explanation quality in response to given questions. The main idea is to categorize preference pairs into three types: consistently correct prompts, consistently incorrect prompts, and a mixed category. The authors also propose a new evaluation framework for explanations, measuring five aspects - logical coherence, clarity, relevance, depth of argumentation, and factual accuracy - using other large language models (LLMs). Experiments on four different datasets show that the proposed method outperforms existing self-alignment techniques in generating better explanations, as assessed by the new evaluation framework. Although answer accuracy was somewhat higher than baseline methods, the difference was not statistically significant. The authors further analyze the effect of preference pair category distribution and find that selecting preference pairs from consistently incorrect prompts and the mixed set improves performance over baselines.
weaknesses: One main concern is the effectiveness of the proposed method in answer accuracy and explanation quality. While M_{Anchor} outperforms M_{Rank}, the differences shown in Table 1 are not statistically significant. Are the reported ± values standard deviations or standard errors? Regarding explanation quality, it would strengthen the paper to validate the new evaluation framework by measuring its correlation with human judgments. Even if LLMs perform similarly to human raters, the reliability of a new evaluation metric needs to be confirmed.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: **Q1**. Why does SFT result in a degradation in explanation quality?

**Q2**. In Figure 2, Section 4.2.4, how is $\lambda$ calculated? Is it  the raw number of preference pairs selected under the consistently-incorrect or variable strategies? How do you scale $\lambda$? Does the total dataset size increase as $\lambda$ increases, or is it kept constant? Could the relative improvement be solely due to a decrease in by decrease in M_{Rank} performance?
soundness: 3
strengths: **S1**. The paper presents a framework for assessing the quality of self-explanations based on various criteria, including logical coherence, clarity, depth of argumentation, and factual accuracy, using an LLM as the evaluator.

**S2**. The authors provide an analysis of the relationship between alignment methods and self-explanation quality, based on their evaluation framework.

**S3**. They introduce the "Alignment with Anchor Points" method to create high-quality preference pairs by grouping them according to the consistency of their accuracy, along with an end-to-end alignment strategy that enhances downstream performance while preserving self-explanation quality, without requiring human rationale annotations.

**S4**. The paper is well-written and easy to read.
summary: The paper presents a framework for evaluating the quality of self-explanations from language models using criteria like coherence, clarity, and accuracy, with an LLM acting as the judge. It introduces the "Alignment with Anchor Points" method to create reliable preference pairs and uses an alignment strategy that boosts performance while maintaining explanation quality, without needing human annotations.
weaknesses: **W1**. Although the authors state in Line 69 that their approach differs from faithfulness, they should be more precise when articulating the aim of their approach. The phrase "effectively conveying the model's reasoning" is broad and can be interpreted as faithfulness. A more suitable term might be "improving the plausibility of explanations," as this study aims to replace human-annotated preference pairs with LLM annotations, and the criteria selected resemble those that make explanations plausible to humans. An explicit discussion of where these criteria fit within the distinction between faithfulness and plausibility would be beneficial [1]. Additionally, while this alignment scheme might incidentally improve the faithfulness of explanations, it is not necessarily required to do so. It would be interesting to examine the correlation between each criterion dimension and faithfulness. References [2, 3] could be used for evaluating the faithfulness of explanations.

**W2**. The paper lacks details about the dataset used. A section, either in the main text or the appendix, should briefly summarize the tasks involved.

**W3**. Since the study relies solely on one base model, LLama-3-8B-Instruct, it is unclear if the proposed method's improvements are consistent across different model families or sizes. For example, the marginal improvement may be reduced in larger models.

**W4**. As mentioned in the limitations, using the base model as the judge during the creation of the self-instruct dataset may fail to capture certain nuances as the model evolves. However, the authors did not provide evidence to show whether this is a practical concern. An analysis comparing how often the M_{base}, M_{Anchor} and the evaluation judge agree on evaluation scores would be helpful.

**W5**. The paper lacks an ablation study that uses a different LLM as the judge for evaluation.

**W6**. The method is limited to classification tasks, as mentioned in the limitations section. However, it could potentially be extended to generation tasks using a similar approach, where outputs are grouped based on their similarity to ground-truth answers. Although this may be more complex than for classification tasks, discussing such possibilities could strengthen the paper.

**W7**. Figure 1 should not use radar charts, as they are difficult to interpret and can be misleading. Replacing each radar chart with a bar plot would make the data much easier to interpret.

**References**

1.  Jacovi, Alon and Yoav Goldberg. “Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?”

2. Lanham, Tamera et al. "Measuring Faithfulness in Chain-of-Thought Reasoning"

3. Parcalabescu, Letitia and Anette Frank. “On Measuring Faithfulness or Self-consistency of Natural Language Explanations.”

---

confidence: 5
contribution: 2
initial_rating: 3
presentation: 2
questions: * In Section 3.2, why did you use the name "Self-Instruction Creation"? Aren't the inputs fixed? Perhaps it should be called "Self-Preference Data Creation" instead?

* I'm not sure how the method differs a lot from the "Self-rewarding LM" paper. Could you elaborate on this?

* Please see the questions in weaknesses
soundness: 2
strengths: * For employing LLM-as-a-Judge, the authors use 5 evaluation criteria and include an ablation experiment in Figure 1 to compare which one was effective. This is very insightful.

* The methodology is clear and very intuitive.
summary: This paper introduces Alignment with Anchor Preference Pairs, a method to improve an LLM's reasoning capability when there is no CoT rationale available. The key idea is to employ the LM as both a judge and response generator. By dividing into three cases, the authors empirically show that their method is effective compared to naively constructing the preference dataset based on the judge model's prediction
weaknesses: * If the main point is exploring how to utilize CoT rationales when they are not available, I think there should also be a upper-bound score, which is curating CoT rationales from a stronger teacher model (for instance, Llama-3.1-70B-Instruct, which was used as the evaluator). Without that, it is hard to tell how effective the method is compared to a strong baseline which is distillation.

* There isn't enough ablation experiments to support the design choices in Section 3.3. For example: 
   * When M_sft consistently produces correct answers, preference pairs are constructed based on the quality of the explanations. What if we remove these instances?
   * When there are a mix of correct and incorrect predictions, the explanations with the highest scores assigned by the judge model is employed. What are the trends like if counterfactually choosing instances where the judge model provides slightly lower scores?

* The evaluation is done with pairwise ranking (O(N^2)), making it computationally expensive to employ in diverse settings.

* The explanation in line 355 - 358 regarding how M_anchor could outperform M_rank lacks insight or details. Could you further elaborate on this?

* The algorithm still requires ground-truth annotations of the datasets (even if it doesn't require a CoT rationale).

---

confidence: 4
contribution: 3
initial_rating: 8
presentation: 4
questions: - How do you choose between two or more explanations with the same score when calling min/max to generate Preference Pairs? My question is regarding line 225.
soundness: 4
strengths: - The paper targets a common yet important problem in the field of explanations.
- A comprehensive evaluation of the explanations through the introduced metrics.
- Using LLM as a judge to evaluate self-explanations w.r.t. the given metrics.
summary: The authors propose a pipeline to generate self-explanation without gold explanations. The process is divided into three main steps: i) explanation evaluation, ii) creating a self-instruct dataset, and iii) aligning the model with Anchor Preference Pairs which are "better" pairs that are refined by categorizing the model's output in three different categories and are then utilized in DPO for model alignment. The main achievement of this research is mitigating the effect of fine-tuning which causes degradation in explanation quality without harming the task accuracy.
weaknesses: N/A

Meta-review: This paper presents a method for improving methods' self-explanation abilities. It produces sets of preference pairs for alignment to find data that will improve these abilities. This is done by separating the examples into several cases (consistently correct, consistently incorrect, and variable) and using a different strategy for each. The strategy for incorrect prompts resembles how STaR (Zelikman et al.) handles incorrect predictions. The paper evaluates explanations with LLM-as-a-judge across several factors. Improvements are shown on mathematical, scientific, and logical reasoning datasets.

This paper explores an interesting and timely problem. The proposed division of examples into the different cases is a nice way to handle it. However, I think there is some circularity in the work: an LLM judge is used in the process of constructing the preference pairs, and then again for evaluation. Although it is the M_Base model, I think this is still circular. Because of shared training data, even different LLMs (especially Llama 3 8B and Llama 3 70B) will be correlated, and I don't have high confidence that the reported gains represent robust improvements in explanation quality. I think more qualitative and human analysis is needed to show that the improvements in explanations represent something real as opposed to surface-level stylistic factors, which LLM judges have been shown to be biased by (e.g., https://lmsys.org/blog/2024-08-28-style-control/ ).

Appendix B makes an argument about correlation with human judgments. The correlations are present, but weak. It is possible that improvements in the judge scores may not be reflected by human evaluation, particularly since the judges are used in the process.

Decision: Reject