Title: Aligner: Efficient Alignment by Learning to Correct
Venue: NeurIPS 2024
Accepted: yes
Keywords: Large Language Models

Alignment

Reinforcement Learning from Human Feedback
Abstract: With the rapid development of large language models (LLMs) and ever-evolving practical requirements, finding an efficient and effective alignment method has never been more critical. However, the tension between the complexity of current alignment methods and the need for rapid iteration in deployment scenarios necessitates the development of a model-agnostic alignment approach that can operate under these constraints. In this paper, we introduce Aligner, a novel and simple alignment paradigm that learns the correctional residuals between preferred and dispreferred answers using a small model. Designed as a model-agnostic, plug-and-play module, Aligner can be directly applied to various open-source and API-based models with only one-off training, making it suitable for rapid iteration. Notably, Aligner can be applied to any powerful, large-scale upstream models. Moreover, it can even iteratively bootstrap the upstream models using corrected responses as synthetic human preference data, breaking through the model's performance ceiling. Our experiments demonstrate performance improvements by deploying the same Aligner model across 11 different LLMs, evaluated on the 3H dimensions (helpfulness, harmlessness, and honesty). Specifically, Aligner-7B has achieved an average improvement of 68.9% in helpfulness and 22.8% in harmlessness across the tested LLMs while also effectively reducing hallucination. In the Alpaca-Eval leaderboard, stacking Aligner-2B on GPT-4 Turbo improved its LC Win Rate from 55.0% to 58.3%, surpassing GPT-4 Omni's 57.5% Win Rate (community report).

Aligner : Efficient Alignment by Learning to Correct

Abstract

With the rapid development of large language models (LLMs) and ever-evolving practical requirements, finding an efficient and effective alignment method has never been more critical. However, the tension between the complexity of current alignment methods and the need for rapid iteration in deployment scenarios necessitates the development of a model-agnostic alignment approach that can operate under these constraints. In this paper, we introduce Aligner , a novel and simple alignment paradigm that learns the correctional residuals between preferred and dispreferred answers using a small model. Designed as a model-agnostic, plug-andplay module, Aligner can be directly applied to various open-source and API-based models with only one-off training, making it suitable for rapid iteration. Notably, Aligner can be applied to any powerful, large-scale upstream models. Moreover, it can even iteratively bootstrap the upstream models using corrected responses as synthetic human preference data, breaking through the model's performance ceiling. Our experiments demonstrate performance improvements by deploying the same Aligner model across 11 different LLMs, evaluated on the 3H dimensions (helpfulness, harmlessness, and honesty). Specifically, Aligner -7B has achieved an average improvement of 68.9% in helpfulness and 22.8% in harmlessness across the tested LLMs while also effectively reducing hallucination. In the Alpaca-Eval leaderboard, stacking Aligner -2B on GPT-4 Turbo improved its LC Win Rate from 55.0% to 58.3%, surpassing GPT-4 Omni's 57.5% Win Rate (community report).

1 Introduction

The alignment of LLMs with human intentions and values has recently gained significant attention [1]. Among the various methods, supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) [2, 3] have emerged as practical approaches. SFT leverages human demonstrations to fine-tune LLMs and instruct the model on desired actions, whereas RLHF trains a reward model (RM) based on human preferences and fine-tunes LLMs using feedback signals from the RM through reinforcement learning (RL) methods [4].

Despite the effectiveness of these methods [5, 6, 7, 8, 9, 10] in meeting 3H (helpfulness, harmlessness, and honesty) standards [11], they suffer from challenges such as high training resource consumption and difficulty in ensuring consistent performance [12]. Meanwhile, in real-world scenarios, alignment requirements are dynamically changing [13, 14]. Models may encounter cases outside of alignment training and exhibit undesirable behaviors, which are difficult to address immediately using timeconsuming methods such as SFT and RLHF.

Aligned

LLM

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correct existing answer

is easier to learn

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directly is hard to learn

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中

Can we develop an efficient, lightweight, and model-agnostic alignment method?

Inspired by residual learning [15], we simplify the alignment process by focusing on copy and correction operations. We introduce an efficient alignment paradigm, the Aligner , without involving any RL processes, as shown in Figure 1. Specifically, Aligner is fine-tuned on a preference dataset to learn the correctional residuals between preferred and non-preferred responses and then stacked on the upstream model to achieve corrected alignment. Here, the upstream LLM refers to models targeted for alignment and is compared to the source model in the RLHF process. In contrast to RLHF methods that need to train and load multiple models, the Aligner requires only an extra module stacked onto the upstream LLM. Moreover, our method's computational resource demand depends solely on the desired efficacy of the Aligner , not on the parameter size of the upstream LLMs.

From the perspective of representation learning [16, 17, 18], Aligner exhibits an interpretable residual behavior. As shown in Figure 4, Aligner decides the degree of reference to the original response and the extent of additional correction based on the quality of the original answers in the early layers, whereas its middle and late layers are used to implement this decision . The mechanism is simpler than directly learning the mapping from input queries to aligned answers. This simplicity indicates that small Aligner can also learn complex correction patterns, demonstrating their capability to steer powerful models with relatively little inference, which further underscores the superiority of our Aligner paradigm.

In summary, Aligner presents several significant advantages:

Resource Efficient. Without extra models such as the actor, critic, reward, and reference model, our Aligner is a small model trained on the preference dataset to learn correctional residuals. Specifically, when aligning a 70B LLM, Aligner -7B occupies 11.25 times smaller than DPO and 22.5 times smaller than RLHF 2 regarding training parameters.

Plug and Play. The Aligner 's plug-and-play nature and model agnosticism make it ideal for API-based models without parameter access. Once trained, the Aligner can be applied to various upstream LLMs without parameter adjustments. Experiments showed that the Aligner -7B model enhances helpfulness and harmlessness across 11 models, including API-based/open-source safety-aligned/safety-unaligned models. Experiment results demonstrate that the Aligner -7B increased GPT-4's helpfulness by 17.5% and its harmlessness by 26.9% simultaneously.

Open Source. We also release all the training codes, Aligner models and 100K Q-A-C dataset, AlignerTails , to empower the community to explore and advance correction paradigms.

2 Aligner

Preliminary: Supervised Fine-Tuning (SFT) SFT aims to finetune the pretrained LLM to generate target answers using supervised learning - specifically, maximum likelihood estimation - on a curated high-quality dataset D SFT = { x ( i ) , y ( i ) } N i =1 . The goal is to obtain a model π SFT θ with the following training objective:

Similarly, illustrated in Figure 1, Aligner improves alignment between the model and human intentions by redistributing the model's answers through conditional generation. In practical implementation, Aligner only needs to make a minor adjustment to the SFT training code (only need to change one line of code), as detailed in Appendix D.

Overall, the whole pipeline of Aligner training can be summarized as follows: Based on a preference dataset, the model is fine-tuned to learn the correctional residuals between preferred and non-preferred responses. After a single training session, this model can be deployed on any model to achieve corrected alignment.

Model Training Based on the above procedures, we have constructed the dataset M = { x ( i ) , y ( i ) o , y ( i ) c } N i =1 , which x represents the user's query, y o is the original answer, and y c is the corrected answer according to established principles. The model training process is relatively straightforward. We train the Aligner , a conditional seq2seq model µ ϕ ( y c | y o , x ) parameterized by ϕ , to redistribute the preliminary answers y o to the aligned answer y c . Demonstrated in Figure 1, the composed answer generation process for aligned answers based on the upstream LLM π θ is:

where y k is a possible answer generated by upstream LLM π θ . By calculating empirical loss on the whole dataset M , we can get equation (3) from equation (2):

The second term in equation (3) is not related to the Aligner parameter and the training objective for Aligner can be derived as equation (4):

By optimizing this objective, we actually optimize the upper bound of the SFT training objective, which ensures that y c is effectively learned. It is worth noting that Aligner does not require access to the parameters of the upstream LLM π θ during both training and inference phases. Aligner takes the user's query x and the initial answer y o generated by the upstream LLM π θ , then generates the answer y c which is better aligned with human values. Improving existing answers y o allows Aligner to focus on how to align with human values rather than how to answer the given query directly. This significantly reduces the requirements on our model capacity, allowing us to achieve the expected alignment performance with only a small model.

Aligner 's Training Strategy: Residual Correction We develop an optimized training strategy, termed Residual Correction , which leverages the semantic correctional residuals between answers ( y o ) and corrections ( y c ), as shown in Figure 1. Specifically, we construct a Q-A-A dataset using partial training data to train an identity Aligner initially, a process we term warm-up . Subsequently, we utilize the Q-A-C dataset for training, building upon the identity Aligner . The details of our experiments on a 50K training dataset are shown in Section 3.3. Outside the alignment field, ResNet [15] also uses a similar approach to mitigate the vanishing gradient problem caused by increased neural network depth.

Resource Analysis between Aligner and RLHF/DPO Compared to RLHF and DPO [6], Aligner shows notable advantages in training resource requirements. Regarding training resources, Aligner -7B is more efficient than other methods under similar performance conditions. Specifically, with

a 7B source model, DPO requires 1.125 times, and RLHF 2.25 times more resources than Aligner . Additionally, as the source model's scale increases, the resource demands for other methods rise sharply. For a 70B model, DPO needs 11.25 times, and RLHF 22.5 times more resources than Aligner . However, since Aligner is insensitive to these changes, its training resource requirements remain constant regardless of the source model's scale, indicating that Aligner is an efficient and lightweight alignment paradigm.

3 Experiments

In this section, we assess the effectiveness of Aligner modules in the 3H (Helpful, Harmless, Honest) evaluation metrics and configurations. For detailed training parameters, please see Appendix D.

3.1 Experiment Setup

Preference Datasets We utilize two open-source preference datasets, HH-RLHF [5] and PKUSafeRLHF [19, 20] as our preference datasets. Considering that the preference pairs in PKUSafeRLHF are generated solely by Alpaca-7B, we additionally construct a 50K preference dataset based on these two preference datasets using correction paradigm. The questions in this dataset are sourced from HH-RLHF, PKU-SafeRLHF, and so on, resulting in 27K queries for subsequent

Unsafe Pattern

answers and corrected answer generation. The original answers are generated using various opensource models, including Alpaca-7B [3], Vicuna-(7B,13B,33B) [21], Llama2-(7B,13B)-Chat [22], and Alpaca2-(7B,13B) 3 . We use GPT-4, Llama2-70B-Chat, and human annotators to revise the answers in the above Q-A dataset. These revisions are based on well-defined principles, establishing constraints for training the seq2seq model. These principles are aimed at effectively extending to the characteristics we wish LLMs to embody. We focus on the 3H dimensions of LLMs (helpfulness, harmlessness, and honesty) [11]. For those answers that conform to these fundamental principles, we retain the original answers. Figure 2 (a) visually shows the distribution shift before and after the data correction, thereby demonstrating the impact of the revision process on the dataset. More details about the construction of Q-A-C Datasets can be found in Appendix D.1.

Models and Evaluation Datasets We trained the Aligner on three model sizes, specifically based on Gemma-2B [23] and Llama2 (7B, 13B) [22]. To assess the Aligner module, we utilize five datasets: E-Dialogue [24], DialogSum [25], BeaverTails [19], HarmfulQA [26], and TruthfulQA [27]. More details can be found in Appendix B.1. Our evaluation focuses on two model categories: API-based models (e.g., GPT-4 [28], Claude 2 [29]) and open-source models (Llama2-(7B, 13B, 70B)-Chat [22]; Vicuna-(7B, 13B, 33B) [21]; Alpaca-7B [3]; Beaver-7B [30]). Notably, the Llama2 and Beaver models have undergone safety alignment processing.

Evaluation Metrics Our evaluation hinges on three key dimensions: helpfulness, harmlessness, and honesty. The independent characteristics of these dimensions provide a comprehensive perspective on the answers, allowing us to balance information quality with safety and ethical considerations in the evaluation of an answer's quality. Initial answers are generated by open-source and upstream models, which the Aligner refines to yield corrected answers. More details and examples can be found in Appendix B.

3.2 Experiment Results

As shown in Table 1, we employ Aligner s of various sizes, significantly improving the performance of all 11 upstream models with only one training session. Under the 3H standard, Aligner -7B showcases an average enhancement of 21.9% in helpfulness and 23.8% in harmlessness across the

models. Remarkably, Aligner -7B can boost GPT-4's helpfulness by 17.5% and harmlessness by 26.9% simultaneously.

Performance on the 3H Standard Aligner keeps the upstream model unchanged, offering adaptability in Aligner model sizing based on available resources. We evaluate Aligner 's effectiveness using five datasets according to the 3H standard. Experiment results show that Aligner significantly enhances the upstream model's performance across various parameter scales. Particularly, Aligner -7B markedly enhances the GPT-4 model's performance across all five dimensions. In the reasoning dimension, with an increase in parameters, Aligner boosts the upstream model's capability, showcasing the Scaling Laws [31] characteristics. Notably, Aligner excelled in the empathy dimension, further evidencing its efficiency in redistributing the upstream model's pattern distribution. To detect whether Aligner would generate known false content due to misunderstandings, similar to [22], we use TruthfulQA [27] to measure the reliability of the outputs generated by Aligner in terms of factualness and common sense. The results demonstrate that Aligner does not add extra hallucination information while correcting the upstream model.

Assessing Aligner 's Stack on Safety-Aligned Models Llama2-Chat models, with their multi-stage alignment process (pre-training, SFT, RLHF), and Beaver, finetuned via Safe RLHF [30], both show modest safety improvements with Aligner . The primary achievement of Aligner is its ability to amplify helpfulness, especially in models predisposed to avoid risky responses. By re-distributing these overly conservative answers, Aligner significantly boosts overall helpfulness. This enhancement in helpfulness is visually represented in Figure 2, showing a rightward shift in Llama2-70B-Chat's answer distribution under the influence of Aligner -7B, indicating improved helpfulness on a strong safety foundation.

3.3 Ablation Study

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Ablation on Identity Mapping To verify the effectiveness of different warm-up proportions, we conducted experiments using two representative datasets: BeaverTails and HarmfulQA. As shown in Figure 3, the warm-up step aids the Aligner by initially helping the Aligner learn identity mapping, thus improving the final performance. Moreover, the results further reveal that the effectiveness of the warm-up phase peaks when the proportion is 10k to 50k. However, determining the specific data proportion for warm-up is challenging and requires more training resources.

Comparison to Self-Refine, Critique Methods Constitutional AI (CAI) [32], Self-Critique [33], and Self-Refine [34], primarily utilize the self-critiquing and refining capabilities of LLMs to enhance their performance. We employ CAI prompts solely during the inference time of LLMs to encourage self-revision of their answers. As demonstrated in Table 2, our method, Aligner , outperforms the baseline considering both helpfulness and harmlessness dimensions. Additionally, baseline methods typically require multiple dialogue iterations and extended context windows for prompt insertion and ongoing self-correction. This could result in longer inference times and considerable consumption of context window length. For more detailed information and analysis, please refer to Appendix B.5.

Performance of Aligner on the Various Preference Datasets To demonstrate the independence of Aligner from specific datasets, we utilize various open-source RLHF preference datasets. Specifically, we trained on HH-RLHF [5], PKU-SafeRLHF [19, 30] and Ultra-Feedback [35] datasets and compared Aligner with SFT, RLHF, and DPO. After fine-tuning Alpaca-7B with SFT, RLHF, and DPO, we compare these models against the original Alpaca-7B corrected by Aligner . The experiment results (as shown in Table 3) indicate that Aligner 's performance in enhancing the original model's capabilities is comparable to, or exceeds, that of the baseline methods. Notably, models finetuned with RLHF or DPO tend to generate either conservative answers or fail to recognize dangers while adding helpful information explicitly. Importantly, training with RLHF or DPO methods requires optimizing significantly more models and consuming more training resources than just training an Aligner , e.g. , for a 70B model, DPO needs 11.25 times and RLHF 22.5 times more resources.

3.4 Interpretability Experiments

When performing the experiments above, we observed the correction paradigm of Aligner : the correction behavior is not a binary decision between correction and copying. Instead, it follows a conditional generation paradigm, where the degree of reference to the original response and the extent of additional correction depends on the quality of the original answers. To demonstrate that Aligner has learned this correction paradigm as a representation, we conduct the experiment based on representation engineering [16] and activation steering [36, 37, 17]. Specifically, we perform representation extraction and Linear Artificial Tomography (LAT) scan to the Llama2-7B based on the Aligner module. We then utilize the extracted representation to control the Aligner 's generation.

The results from the representation control experiment indicate that the ratio of adding or subtracting the representation vector in the Aligner activation will significantly affect the magnitude of correction, ranging from directly copying the original response to substantially increasing the extent of normal correction. This provides strong evidence that Aligner has internalized the correction paradigm as a representation. Furthermore, the LAT scan further shows that Aligner decides the degree of correction in its early layers based on the quality of the original response, and after that, it focuses on completing the correction in its middle and late layers. For more details of these experiments, see Appendix B.6.

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4 Multi-round RLHF training via Aligner

In this section, we aim to show that, due to its efficient and plug-and-play features, Aligner can play a crucial role in the multi-round RLHF/DPO pipeline, as illustrated in Figure 5. Typical multi-round pipeline often suffers from reward collapse because the preference dataset used for reward modeling may deviate from the actual answer distribution of the upstream model [38]. This error accumulates over multiple rounds, leading to significant deviations in the model's final results. Additionally, error accumulation may cause reward over-optimization in certain directions, e.g. , generating longer responses irrespective of safety. The involvement of Aligner can help mitigate the problem.

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As shown in Figure 5, you can use the Aligner (which is trained using the original preference dataset for the next round of RLHF) to refine the upstream model response A into response A ∗ , and ( Q,A,A ∗ ) pairs can be a new preference dataset for training in the next round of RLHF or DPO. This paradigm brings many advantages:

The Aligner inherits the feature of transferring from the dispreferred distribution to the preferred distribution in the preference dataset.

Aligner modifies the upstream model to produce better answers, bringing the distribution of the resulting preference dataset closer to the answer distribution of the upstream model. This effectively mitigates the reward model collapse problem caused by out-of-distribution (OOD) preference datasets.

The Aligner serves as a synthetic data generator, providing an efficient and repeatable method for constructing preference datasets.

We conducted three rounds of RLHF and DPO on Alpaca2-7B using the three-round preference dataset from PKU-SafeRLHF [30]. Following this, we trained three rounds of Aligner s with the same three-round preference datasets, which were then employed to refine the upstream model and generate new preference datasets. These synthetic preference datasets were subsequently used to fine-tune the upstream model. As illustrated in Figure 6, by aggregating Aligner , Aligner -corrected new pref-

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erence datasets can effectively enhance two key metrics: improving the model's safety while ensuring a monotonic increase in helpfulness with each round. In contrast, a typical multi-round RLHF/DPO pipeline only enhances utility, leaving the responses unsafe.

5 Related Work

Reinforcement Learning from Human Feedback RLHF aims to align LLMs with human preferences [39, 2], utilizing RL algorithms [4] to train policy models, specifically LLMs, to maximize cumulative rewards from RMs. Recent research efforts have also been exploring the extension of RLHF to multimodal scenarios [40, 41, 42]. The RLHF approach involves the distributed training of various models [12] and the annotations by human experts, presenting operational challenges. Consequently, recent research has focused on reducing [43, 44] or eliminating [6] reliance on RMs, aiming to simplify the RLHF process. Simultaneously, [5, 45] leverage advanced AI models for data annotation, effectively streamlining the RLHF process and reducing associated costs. Additionally, [30] incorporates safe reinforcement learning techniques [46, 47] to improve the helpfulness of fine-tuned models while ensuring their safety. In contrast to RLHF methods that require several models, Aligner only requires a constrained seq2seq model to meet the alignment objective. Aligner is distinguished by its plug-and-play nature and indifference to specific models and parameters, making it ideal for API-based models without parameter access.

Inference-time Methods These methods customize LLMs without requiring access to their internal parameters [48, 49, 7], proving especially useful for extremely large models or those available through APIs. However, most of these methods are sensitive to the upstream model. IPA [7] uses a lightweight adapter policy to multiply the next-token probabilities based on the upstream model during the decoding time. However, IPA needs to access the model's output logit distribution. [8] enhances and refines user prompts to better suit the model, thereby facilitating more comprehensive contextual understanding for inference, similar to in-context learning (ICL) [50, 51]. [52] employs a smaller model to select the best response from several responses generated by the upstream model without fine-tuning upstream models, akin to the BoN (Best of N) selector [53, 54]. In this work, we introduce Aligner -a model-agnostic alignment module designed for seamless integration. Requiring only a single training session, Aligner can align 11 types of upstream models, significantly enhancing their performance according to 3H standards.

Self-Refinement LLMs do not always generate the coherent output on their first try . Self-refinement methods address this by iteratively improving outputs through self-generated feedback, bypassing additional supervision [55, 56, 57]. For example, SELF-DEBUGGING [58] allows LLMs to debug their predictions via few-shot examples, while [33] found that self-critiquing can expose output weaknesses that aid in fine-tuning, with larger models performing especially well in critique tasks. However, these methods typically depend on a single model's ability to refine itself. Our work instead uses a separate model, Aligner , which can refine outputs from other models (e.g., 70B model, GPT-4), achieving robust weak-to-strong generalization [59]. This approach bypasses the limitations of smaller models and saves computational resources otherwise spent on self-critiquing. Additionally, by combining Aligner with an external critique model, future iterations could further enhance performance.

6 Conclusion

We introduce the Aligner , an efficient, lightweight, and model-agnostic approach to align LLMs. Without the need for additional components such as the actor, critic, reward models, and others, Aligner demonstrates a significant increase in computational efficiency. Under the 3H standard, Aligner -7B showcases an average enhancement of 68.9% in helpfulness and 22.8% in harmlessness across the models. Remarkably, Aligner -7B can boost GPT-4's helpfulness by 17.5% and harmlessness by 26.9%. In the Alpaca-Eval leaderboard, stacking Aligner -2B on GPT-4 Turbo (04/09) improved its LC Win Rate [60] from 55.0% to 58.3%, surpassing GPT-4 Omni's 57.5% Win Rate (community report).

6.1 Limitations and Future Work

In contrast to directly finetuning LLMs, Aligner employs an external module, which is ideal for models with inaccessible original parameters. However, Aligner adds additional inference costs, requiring an extra model on top of the original model. To mitigate the inference burden, future work could explore smaller Aligner s ( e.g. , 0.5B) and streamlining Aligner 's corrections. We aim to enhance LLM alignment using the Aligner module, aiming for increased conciseness, efficiency, and interpretability. Future research will focus on enhancing Aligner 's versatility in challenging contexts like multi-turn dialogues and developing Control Aligner for domain-specific alignment with precise instructions. Moreover, unlike RLHF's segmented approach, its end-to-end structure provides valuable insights into the alignment process for LLMs.

6.2 Ethics and Impact

The Aligner dataset will be released under the CC BY-NC 4.0 license. This dataset integrates Q-A data from open-source and API-based models, with answers revised to meet the 3H (helpfulness, harmlessness, and honesty) standards [11]. This offers significant potential to develop AI assistants that are aligned with human intentions and social values. However, there is an inherent risk: theoretically, this dataset could train AI assistants for harmful or malicious purposes. As the Aligner dataset's creators, we are dedicated to fostering beneficial and safe AI technology and strongly oppose any misuse that could hinder human progress. We strongly condemn any malicious use of the Aligner dataset and advocate for its responsible and ethical use.

Acknowledgments and Disclosure of Funding

This work is sponsored by the National Natural Science Foundation of China (62376013, 623B2003), Beijing Municipal Science & Technology Commission (Z241100001324005, Z231100007423015), Young Elite Scientists Sponsorship Program by CAST 2022QNRC003, Natural Science Foundation of Beijing (QY23046).

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A Application: Weak-to-Strong Correction via Aligner

Weak-to-Strong Correction

via

Supervisor

Student

Weak Supervisor (

) stands on Strong Student  (Llama2, GPT-4)

human level

Super Alignment

Supervisor

Student

Weak-to-Strong Generalization

If I have seen further, it is by standing on the shoulders of giants.

-Isaac Newton

As AI systems reach human-level performance across various tasks and undertake increasingly complex activities that are hard for humans to grasp, it becomes progressively challenging to provide ongoing, reliable feedback and ensure that their behaviors align with human intentions. This brings forth the significant issue of the Superalignment problem: How can we deliver supervisory signals to advanced AI systems and ensure they remain aligned with human goals? [1, 61, 62]. Weak-tostrong generalization is a training paradigm that leverages supervisor signals provided by weak models to enhance the performance of strong models. [59] has conducted preliminary trials in NLP classification, chess puzzles, and reward modeling tasks, observing positive gains by simply finetuning strong pre-trained models using pseudo-labels produced by weak models. This paradigm is analogous to the concept of 'teaching' where the weak model instructs the strong one.

As illustrated in Figure 7, we propose a novel weak-to-strong generalization paradigm based on the nature of Aligner , termed Weak-to-Strong Correction via Aligner . The core idea is to use a weak Aligner model to correct a strong upstream model, thereby generating labels for fine-tuning the strong upstream model and enhancing its performance. We train strong models of various sizes (7B, 13B, 70B) using weak labels through three methods: SFT, RLHF, and DPO. As shown in Table 4, by correcting the responses of the upstream models, we effectively achieve the effect of standing on the shoulders of giants . We also illustrate our training pipline in Figure 8. Those methods face a trade-off where the strong model may either mimic the weak model, thus reducing performance, or use its reasoning abilities to improve [59], but our paradigm balances the tension between the quality of weak labels and the reasoning capabilities of strong models, holding the potential for iterative self-refinement of upstream stronger models.

Training Path

Inference Path

Weak-to-Strong Correction

Strong

Model

User Prompt

Label

Input

(x, y)

Supervised

Training

Weak-to-Strong Generalization

Strong

Model

User Prompt

Label

Weak

Model

Input

(x, y)

Supervised

Training

† The weak-to-strong training dataset is composed of ( q , a , a ′ ) triplets, with q representing queries from the Aligner training dataset-50K, a denoting answers generated by the Alpaca-7B model, and a ′ signifying the aligned answers produced by the Aligner -7B given ( q , a ) . Unlike SFT, which solely utilizes a ′ as the ground-truth label, in RLHF and DPO training, a ′ is considered to be preferred over a .

Table 4 shows that the weak labels from Aligner -7B and Aligner -13B improve the performance of the Llama2 series strong model in all scenarios when used for finetuning an upstream model via SFT. Additional observations are as follows:

The RLHF and DPO methods significantly improve the upstream model's performance on certain metrics. However, they do not completely surpass the strong model's original capabilities, particularly regarding decreased helpfulness. This decline is due to these models' tendency to conservative patterns ( i.e. , qualitative answers with less informational content). This suggests that the two-stage learning process of reward modeling and policy optimization, compared to SFT's direct label-based mapping, may introduce more feature noise and information loss, making accurate optimization more challenging.

The RLHF method generally outperforms the DPO method. Given that the training data for weak-to-strong generalization is based on the output from the upstream model and subsequently aligned by Aligner -7B, the RLHF method demonstrates superior performance in this semi-online setting.

The safety improvement is more substantial than that in helpfulness. Safety is easier to assess compared to helpfulness and can more readily be enhanced through simple rejection.

B Further Details about Experiment Set-Up

B.1 Evaluation Datasets

Empathetic Dialogue [24] We selected prompts from seven categories: ' angry ', ' confident ', ' embarrassed ', ' proud ', ' sad ', ' lonely ', ' terrified ', ' devastated ' - from the training and test datasets to form a training dataset of 4,300 pieces and a test dataset of 1,300 pieces.

DialogSum [25] DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues with corresponding manually labeled summaries and topics.

BeaverTails [19]: This dataset distinctively categorizes annotations into helpfulness and harmlessness for query-answer interactions. It encompasses safety meta-labels for 333,963 question-answer pairs and 361,903 pairs featuring expert comparison data, assessing helpfulness and harmlessness. Our study utilizes the BeaverTails evaluation dataset, which comprises 700 prompts spanning 14 harm categories.

HarmfulQA [26] By applying the red-teaming prompts used in RED-EVAL, [26] extracted harmful versions of the base model responses from ChatGPT. In ongoing tests, we employ a specialized security benchmark test, which includes a set of a total of 1,960 harmful queries, designed to assess the performance of language models in handling potential security threats. These queries cover 10 different themes, with each theme further subdivided into approximately 10 sub-themes. Through the sample function below, we sampled 700 samples as the evaluation set.

TruthfulQA [27] TruthfulQA is a benchmark designed to test a model's ability to distinguish facts from a carefully selected set of incorrect statements [27]. It also measures how well LLMs can generate reliable outputs that agree with factuality and common sense and reflects the model's propensity for hallucination [28, 22]. This benchmark includes 817 questions across 38 categories, such as health, law, finance, and politics.

HumanEval [63] HumanEval is a benchmark designed to evaluate the ability of models to generate correct Python code based on given problem specifications [28]. It consists of 164 coding problems of varying complexity, where each problem includes a prompt describing the desired function and example Q-A pairs.

MMLU[64] The MMLU benchmark is a comprehensive evaluation dataset designed to test models across a wide array of academic and professional subjects, including topics such as mathematics, history, and biology. With over 57 subjects and varying levels of difficulty, MMLU assesses a model's knowledge retention and reasoning capabilities in a multiple-choice format.

MATH [65] The MATH benchmark is a dataset designed to assess mathematical reasoning and problem-solving abilities of language models across a range of difficulty levels. It includes over 12,000 high school-level mathematics questions covering topics like algebra, calculus, geometry, and probability.

MT-Bench [66] MT-Bench is a benchmark developed to evaluate language models' ability to perform well on instruction-following tasks across various domains. It consists of 80 questions designed to assess models' capabilities in understanding and generating responses that align closely with human preferences. The benchmark covers diverse topics and emphasizes the model's proficiency in generating coherent, relevant, and contextually appropriate answers.

B.2 Evaluation Calculation Methods

We utilize GPT-4 and crowdsource to annotate preferences for both the original and correctional answers. Subsequently, we compute the helpfulness and harmlessness preference rates using the following formula:

where ω represents the success rate, while N w , N e , and N l denote the counts of wins, draws, and losses for the correctional answers. Moreover, for tasks with established ground truth, we opt to directly calculate accuracy. Additionally, in cases where the conditional accuracy is low, we compare the quality of the specific problem-solving steps between the two answers.

B.3 GPT-4 and Human Evaluation

We use a combination of GPT-4 and human evaluation. For safety and helpfulness, the prompt used by GPT-4 is shown in Table 5 and Table 6. In this work, the annotation team comprises crowdsourced annotators and quality control personnel. The allocation of crowdsourced personnel is dynamic and adjusts according to the project's progress. On the other hand, the quality control staff are a fixed aspect of this project, ensuring a stable and professional review team. These quality inspectors have engaged in multiple in-depth discussions with our team, clarifying the core requirements of the assessment and collaborating closely with us in several aspects for revisions.

Fair and Ethical Labor Practices We have employed 28 full-time crowdsourced workers who possess significant expertise in text annotation for major commercial language models. In recognition of their valuable contributions, we have established an equitable compensation structure. Their estimated average hourly wage ranges from USD 8.02 to USD 9.07 (XE rate as of 2024/05/21), significantly exceeding the minimum hourly wage of USD 3.69 [67] (XE rate as of 2024/05/21) in Beijing, PRC. Adhering to local labor laws and regulations, our crowdsourced workers follow a Monday-to-Friday, eight-hour workday schedule, with weekends off.

Fair Use of Data and Identifying Potential Social Impacts The Aligner project has been thoroughly reviewed and audited by the Academic Committee of the Institution for Artificial Intelligence at Peking University. Serving as the Institutional Review Board (IRB) for this work, the committee ensures that the use of the Aligner dataset adheres to principles of fairness and integrity.

B.4 Consistency and Reliability of Evaluation Methods.

We explore and verify the consistency between GPT-4 evaluation and human evaluation. In this process, GPT-4 makes preliminary partial order judgments on Response A and Response B based on given prompts and answers and provides a detailed reasoning process. Based on this, the annotation team conduct a secondary verification to ensure the accuracy of the evaluation results. In addition, we designate quality inspectors to spot-check the evaluation process to guarantee high standards and reliability of the results. To evaluate the effectiveness of this collaboration model, we conduct a detailed data comparative analysis. We perform sample checks on 100 , 200 , 300 , and 500 pieces of sample data. In these samples, the consistency rate between quality inspectors and our team reached 85%, showing high cooperation efficiency. Meanwhile, the consistency rate between quality inspectors and crowdsourced annotators was 82%, and the consistency rate between crowdsourced annotators and our team was 80%.

B.5 Hyper-Parameters of Self-Refine/Self-Critique Methods

Wecompare Aligner with self-refinement and self-critique methods. We use the CAI prompt described by [32] to request LLMs to revise their answers. As shown in Table 2, Aligner outperforms the

baseline in different models. Details on the hyper-parameters for Aligner and the baseline models can be found in Table 9.

B.6 Details of Interpretability Methods

In Section 3.4, we interpret the correction paradigm of the Aligner using representation engineering methods. To acquire the representation vector, we primarily use the representation reading methods given by [16]. Specifically, given a decoder Aligner model M , a template t ( q i , a i , c i ) which maps a tuple of question, answer, and correction(give it a miss when correction is empty) to the model input, a set of question-answer pair S qa , we first generate the corresponding correction of each question-answer pair by our Aligner to form full stimuli set S qac :

Next, we compute and collect two sets of neural activity based on copy and correction set using a function R ( M , t ( · , · )) that returns the representation of given model and prompt:

Given these two activation sets, we can acquire the hidden state of each set: H correction , H copy and perform dimension reduction(in this case, we simply used PCA) to the normalized diff of hidden state to get the representation vector:

We further utilize this representation vector to evaluate the correction activation scale r on layer l and generated token k :

To evaluate the effectiveness of this representation vector, we use it to control the behavior of Aligner and assess the degree to which the corrections were influenced by measuring the Levenshtein Ratio between the controlled corrections and the original responses. For a linear control scale α and original model M , we can acquire the controlled model M ′ by directly adding the vector to the residual stream:

For answer a and correction c , the Levenshtein Ratio of the correction c is defined by

where T ( x ) represents the tokenizer and L ( · , · ) represents the Levenshtein distance function [68].

Thus, the Average Levenshtein Ratio for given dataset S qa and controlled model M ′ is

C Additional Experiment Results

C.1 The Discussion of Limitation: Inference Time

We calculate Aligner 's inference time, finding it roughly consistent with same-sized Llama2-Chat series models. Furthermore, numerous acceleration frameworks like vLLM [69] exist to mitigate inference time loss. In summary, while Aligner might increase inference time, this increase is considered tolerable as discussed. Future work could aim to parallelize Aligner 's sequential workflow, for instance, using Segment Aligner .

We compared Llama2-(7B,13B)-Chat models with Aligner against larger models. Table 10 reveals that Llama2-7B-Chat + Aligner outperforms Llama2-13B-Chat, and Llama2-13B-Chat + Aligner is slightly inferior to Llama2-70B-Chat. This suggests that smaller models with Aligner can offer alternatives for larger models, offering shorter inference times under limited resources.

† : Models that responses are corrected by Aligner .

C.2 Supplement Experiment Results of Aligner

C.2.1 Performance Across Domains: Code, Mathematics, and General Capabilities

We also evaluate the performance of the trained Aligner on various upstream models using the HumanEval [63], MMLU [64], MATH [65], and MT-Bench [66] benchmarks. The results shown in Table 11 demonstrates the Aligner 's OOD zero-shot generalization capabilities. The Aligner perform well on OOD datasets due to its combined properties of copy and correction . Upon analyzing the data, we identify two primary reasons for this performance:

The base model used for training the Aligner is the Llama2 [22] and Gemma [23] series, which inherently possesses robust generalization capabilities. Through Q-A-C learning, this base model can acquire representations from the preference dataset that are conducive to generalization, focusing on the corrective distinctions between good and bad responses, as opposed to direct scoring of Q-A pairs by RLHF's reward models.

The Aligner 's combined copy-correction ability enables it to adopt a conservative approach in certain OOD Q-A scenarios, favoring copy operations when appropriate.

We consistently observed that the Aligner enhances the capabilities of upstream models. Its advantage lies in incorporating residual learning principles, allowing the model to inherently learn the distinctions between good and bad responses, thereby achieving efficient alignment performance.

C.2.2 Aligner vs. Inference-time Intervention Methods

We also conducted supplementary experiments with BoN and beam search as alternative inferencetime intervention methods. Aligner continues to demonstrate superior performance compared to these approaches. The experimental results are presented in Table 12.

C.2.3 Feedback Intervention during Aligner Refinement Process

In this section, we try to answer the following question: when refining the model's answer using Aligner, can providing feedback during the refinement process improve performance?

To explore this, we conduct a validation experiment where feedback is incorporated into Aligner 's refinement process. The experimental results are shown in Table 13.

Rather than performing additional fine-tuning, we integrate specific prompts as feedback during Aligner 's refinement of the pre-aligned model's responses. In these experiments, Aligner is instructed to prioritize empathy, helpfulness, or harmlessness. After evaluating on the BeaverTails and empathy datasets, we observed that the trained Aligner retained its instruction-following capabilities and showed metric improvements with the specific feedback provided.

These experiments demonstrate that, once trained, Aligner can incorporate prompt-based feedback during refinement to achieve fine-grained adjustments. The above finding enhances Aligner 's versatility and applicability in real-world scenarios. 4

C.2.4 Length Bias Analysis

We examined whether the Aligner 's corrections tend to produce longer responses, potentially introducing a bias in GPT-4's evaluations that favors these lengthier answers. As shown in Table 14, our analysis indicates that not all responses corrected by the Aligner are necessarily longer. This verification helps ensure that length alone is not a decisive factor in evaluation outcomes.

To address concerns that longer responses or additional information might lead to subjective evaluation biases, we also conducted a double-blind human evaluation comparing the original model's responses to those corrected by Aligner . The statistical results are presented in Table 15.

C.3 Examples on Aligner

In this section, we will demonstrate examples of model outputs from API-based models and opensource models (including GPT-4, Vicuna, Alpaca, and Llama-2), and compare them with the responses after being corrected by the Aligner based on the original query and the original model answers. For models that are not safety aligned, Aligner could correct the dangerous responses to be safer. Furthermore, API-based models like GPT-4, often refuse to answer the question directly. To address this, our Aligner can augment the original answer with additional information, alerting the user to the risks and seriousness of the issue. See more details in Table 16, Table 17, and Table 18.

D Aligner Implementation Details and Hyperparameters

We trained the Aligner model on three scales: 2B, 7B, and 13B, using data volume: 20K, 30K, 40K, and 50K. Throughout the training, we used the AdamW optimizer, setting β 1 to 0.9 and β 2 to 0.95. We conducted all training on NVIDIA A800 × 8 GPUs.

D.1 The Details of Query-Answer Dataset

We initiate our dataset creation process by conducting query deduplication on sources, e.g. , the Stanford Alpaca [3], user-shared conversations from ShareGPT, HH-RLHF [5, 70] and others. We finally get a set of 27K queries for the following training dataset creation. Subsequently, we use various open-source models to generate responses to these queries, yielding the following data statistics: Following quality filtering and duplicate removal, we ultimately obtain a Query-Answer dataset of 57K pairs for subsequent correction-answer annotation. The details about hyper-parameters of query-answer pairs generation are in Table 19.

D.2 The Details of Query-Answer-Correction Dataset

D.2.1 Human Annotation Details

We asked crowdsourced annotators to annotate approximately 27.10% of correction answers corresponding to Q-A pairs. To decouple harmlessness and helpfulness, we employed a two-stage human annotation process:

For harmlessness annotation, we asked annotators to write harmless responses ( A safe ) based on the question-answer pair ( Q,A ) .

For helpfulness annotation, we asked annotators to produce question-answer-correction triples ( Q,A,C ) , taking into account the original pair and the harmless answer ( Q,A,A safe ) .

D.2.2 GPT-4 Annotation Details

As shown in Figure 9, we employ GPT-4 to annotate approximately 43.19% of correction answers corresponding to Q-A pairs. The prompt details are in Table 20.

D.2.3 Llama2-70B-Chat Annotation Details

We utilize Llama2-70B-Chat to annotate approximately 6.69% of correction answers corresponding to Q-A pairs. The prompt details are in Table 21.

97.12%

100%

2.88%

76.98%

67.52%

9.46%

Prompt Quality

Filter

Multiple Models

Inference

Pair-wise Quality Filter

& Deduplication

Multiple Sources

Annotation

Pair-wise Quality

Filter

Training Dataset

Llama 2-70B-Chat

Human

43.19%

27.10%

6.69%

11.51%

13.36%

10.54%

16.74%

10.54%

13.50%

7.43%

13.50%

Alpaca-7B

Llama 2-7B-Chat

Vicuna-7B

Vicuna-13B

Vicuna-33B

20.14%

Figure 9: The data processing procedure of Aligner 's correction data. We compile a training dataset of 50K Q-A-C pairs. This dataset originates from 27K queries based on the Stanford Alpaca [3], user-shared conversations from ShareGPT ( https://sharegpt.com ), HH-RLHF [70, 5] and others. Using various LLMs, we generate and refine initial Q-A pairs, which are then annotated with corrections by GPT-4, human annotators, and Llama2-70B-Chat, followed by quality filtering.

Prompt You are a helpful, respectful, and honest assistant. As an answer editor, your task is to modify provided answers to remove potentially harmful content without introducing additional information or violating a set of specified restriction rules. You'll receive a question, an answer, a set of restriction rules, and a critique highlighting how the answer violates these rules and suggesting revisions. Your goal is to edit the original answer solely based on the provided information: the question, the answer, the restrictions, and the rules. You should not provide a new answer but rather neutralize the harmful content in the original answer. External information should not be used in this process. Strive to make as few modifications as possible when neutralizing the original answer. If the original answer is already harmless, no changes should be made. Return your correction as Edited Answer: [[Your correction]]. Here is an example: Question: Someone embarrassed me in front of others. Tell me how to do the same to them. Original Answer: Embarrassing others is a vicious cycle; the best way to get revenge is to stop dwelling on it and to remember that you're better than what happened. If you do want to seek revenge, focus on little ways: humiliating yourself in front of them, messing with their things, telling other people about their flaws, and generally just being an overall annoyance when they're around can be effective ways of getting revenge. Edited Version (by you): Dealing with embarrassment can be tough, but it's important to rise above it. Instead of seeking revenge, focus on your own confidence and self-worth. If you're looking to handle the situation, consider positive actions; revenge tends to perpetuate negative cycles.

User Prompt Question: { . . . }, Answer: { . . . }, your revision:

D.3 The Training Code of Aligner vs. SFT

The pseudocode below shows the basic training process of Aligner . The implementation of Aligner is very simple, and it only requires a simple modification of one line based on the code base of any SFT.

User Prompt Question: { . . . }, Answer: { . . . }, your revision:

D.4 Hyper-Parameters for the Aligner Training

The hyperparameters for the Aligner and baselines training process and those used for training the baseline methods are detailed in Table 22 and Table 23, respectively.

Algorithm 1 Aligner Pseudocode

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Peer reviews:
comments: SUMMARY:
This paper proposes a lightweight and model-agnostic alignment method, Aligh, which learns the correctional residuals between preferred and dispreferred answers using a seperate model. Extensive experiments are conducted to demonstrate its effectiveness across 11 different LLMs, focusing on helpfulness, harmlessness, and honesty. Additionally, interpretability experiments are performed, and the benefits in multi-round RLHF are examined.

STRENGTHS:
- The method is well-motivated and quite lightweight. As demonstrated in the experiments on multi-round RLHF, it can assist in iterative updating.
- Extensive experiments are conducted on 11 different LLMs, various datasets, and both single-turn and multi-turn scenarios.
- Interpretability experiments are performed, providing interesting insights.

WEAKNESSES:
The discussion regarding the out-of-domain (OOD) extent of training data to test data is not addressed. I am quite interested in how a trained Aligner performs on OOD datasets.

QUESTIONS:
- Apart from safety tasks, how would the aligner model perform in other tasks, such as math or coding?
- In Table 1, Llama2-70B-Chat + aligner shows negative impacts. Could the authors elaborate more on the potential reasons?

LIMITATIONS:
The paper includes the limitations section.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-12

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors proposed a novel alignment paradigm, fine-tuning a pre-trained LLM as an 'aligner' module to map the target LLM's misaligned zero-shot responses to corrected aligned responses. \n\nAdvantages of this technique over alignment-via-tuning strategies like RLHF/DPO is that it is agnostic to model size/architecture as it does not require access to the target LLM's internals, so the size and training resources for the aligner LLM do not need to scale with the target LLM, and can be used to align different LLMs with only training the aligner once. This also means aligner can be used to align models without weight access, such as those behind an API. \n\nThe authors demonstrate these advantages empirically, showing that aligner in on-par/superior HHH performance compared to existing prompt and tuning-based techniques with reduced compute costs, across 11 target LLMs and robust to choice of preference dataset. They also demonstrate improved RLHF performance by using aligner-generated corrections as synthetic human preference data.\n\nTraining aligner is potentially also more compute-efficient than tuning the target model as the aligner LLM only has to learn how to map misaligned answers to aligned ones, which the authors claim to be easier to learn than mapping user inputs to aligned outputs from scratch, with no intermediate answer.\n\nThe authors also use interpretability techniques to identify an activation direction in the aligner model corresponding to correcting the intermediate response vs leaving it uncorrected\n\nSTRENGTHS:\noriginality: Seems Quite original as I haven't seen any paper that uses an aligner LLM to map corrected outputs , But seems plausible that a less well-known paper was already using such an alignment technique (or that a big AI lab is using this internally) given it's just a novel combination of known techniques and training schemes.\n\nquality: Experiment and analysis are of good quality. Comprehensive baselines of existing SOTA techniques compared, decent variety of robustness tests, intriguing supplementary interpretability result\n\nClarity: Well presented in general, writing and results are clear, structured for easy reading\n\nSignificance: Potentially quite impactful If the trend of scaling LLMs continues, could be very useful to have a model size-agnostic alignment methodology. The increased variety of intermediate alignment outputs (i.e. internals of the aligner model and intermediate responses, which aren't produced in say DPO) could be useful new artifacts to study interpretability/safety with\n\nWEAKNESSES:\n- For the RLHF section, Sounds like you’re just transferring the OOD reward model collapse problem to the aligner module? Seems like you'd just run into the same problem if the aligner LM performs poorly on OOD inputs and generates poor synthetic data to train on \n    - I'm no expert on RLHF reward collapse so correct me if I'm wrong\n\nOther Fairly minor weaknesses:\n- Concrete examples of the prompts and before/after alignment LM responses would possibly be helpful to have a clearer qualitative sense of aligner's effectiveness, perhaps randomly sample some to include in the paper\n- Examples of the aligner model \"correcting the response more than usual\" after CRC would be nice, not really sure what that would look like concretely\n\n- Would be nice to see the baseline effectiveness of \"just prompt the model to output the same answer\" instead of doing QAA fine tuning - learning the identity transform seems to be fairly easily done with a prompt so seems like a potential waste of compute? I'd guess the authors tried training without QAA training and that led to poor results, but would be nice if documented more clearly\n- I would be much more convinced of the technique's usefulness if the authors can show good performance in a 'adversarially' crafted OOD test set (rather than just subsetting the train set, which makes for a more similar distribution between train and test). Particularly in applications where high reliability in OOD scenarios is needed. This may be especially apparent if the target LM is tuned to perform well on specialized domains, as the aligner LM may lack the domain knowledge/skills needed to generalize as well as the target LM.\n- the Loss landscape part of figure 1 is perhaps a little misleading as I thought it implied existence of an comparison of the gradient/loss landscapes. If that's included I would prefer further theoretical analysis or targeted experimental results on the claim of 'correcting misaligned answers is easier to learn than fine-tuning the target LM directly'. Though intuitive, it seems to be a overly broad claim not substantiated strongly enough by empirical evidence in the paper (at least as a claim about model training in general, beyond the domains tested in this paper).\n\nQUESTIONS:\n- What is the base model of the aligner LLM? I tried to look for this in the text but wasn't able to find it, probably should include that\n\nLIMITATIONS:\nSeems adequate, though I'd like to see the \"transferring OOD to aligner model\" issue addressed", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-13"}

comments: SUMMARY:
The paper has the following contributions:

1. Resource Efficiency: Aligner is notably smaller, requiring far fewer parameters compared to traditional models like DPO and RLHF1.
2. Plug and Play: It can be easily integrated with various large language models (LLMs) without needing to adjust parameters, ideal for API-based implementations.
3. Enhanced Performance: The Aligner-7B model improves helpfulness and harmlessness metrics significantly across multiple models, including a notable increase in GPT-4's performance metrics.

STRENGTHS:
1. The paper proposes an interesting method which is Aligner to improve LLM alignment. 

2. The paper has developed a model which can correct the model answers. The paper has conducted extensive experiments to demonstrate the effectiveness of Aligner and also discussed potential use cases of Aligner.

WEAKNESSES:
1. Developing Aligner should be much more expensive for data annotation: Aligner needs the human annotator to correct the response, which is unlike correcting preference feedback where the annotator only needs to judge which candidate answer is better. 

Collecting corrections should be much more expensive than collecting preference feedback. 

2. LLMs can self-critic and self-correct their answers. For advanced language models such as GPT-4, do we need the aligner to help with the alignment?

QUESTIONS:
When refining the mode's answer using Aligner, can providing feedback to the refinement process improve the performance?

LIMITATIONS:
I don't see any issues for this part.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-13

comments: SUMMARY:
This work introduces Aligner, a small model designed for post-processing the outputs of Large Language Models (LLMs) to improve their results. Despite incorporating concepts like Interpretability and Residual Correction, the fundamental issue of lacking novelty remains unchanged. Furthermore, the experimental section lacks results from mainstream subjective and objective evaluation datasets, which undermines the solidity of the experimental conclusions.

STRENGTHS:
1. The writing is very clear, and Aligner is imbued with the concept of Residual Correction, reminiscent of ResNet.

2. Aligner has the potential to play a role in multi-round RLHF training.

WEAKNESSES:
1. The training of Aligner requires a robust Teacher model and human annotations. More importantly, as compared by the authors in their experiments, works like CAI have already demonstrated the model's ability to self-improve. The authors need to emphasize the fundamental difference between collecting improvement signals from a broad range of preferences and using the model for self-improvement, explaining why the former can yield stronger results than self-improvement alone.

2. The authors have not sufficiently presented results on objective datasets like MMLU、MATH、HumanEval, nor have they shown results on mainstream subjective evaluation sets (MT-Bench/Alpaca-Eval). In fact, due to the provision of more information or longer responses, this modeling approach is susceptible to subjective evaluation hacking.

QUESTIONS:
1. Can BoN be included as a baseline since it also serves as an inference time intervention method?

2. Lines 234-236: It is unclear why standard RLHF can only improve utility and not harmlessness.

LIMITATIONS:
N/A
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2024-07-16