Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels

Summary

The paper introduces SAMI, an iterative algorithm designed to align LLMs with behavioral principles (constitutions) without the need for preference labels or demonstrations. SAMI achieves this by optimizing the conditional mutual information between principles and self-generated responses given queries. The approach shows significant improvements in single-turn dialogue and summarization tasks compared to pretrained and instruction-finetuned models. In summary, this paper presents a novel and effective method for aligning language models with behavioral principles without relying on preference labels or demonstrations. Despite demonstrating strong empirical results and scalability, the approach faces challenges related to its dependence on the initial models, regularization issues, domain limitations, and length bias. Overall, SAMI represents a significant advancement in the efficient and practical alignment of language models.

Strengths

1. The introduction of SAMI represents a significant innovation by aligning LMs with principles without using preference labels or human demonstrations. 2. The method outperforms both the initial pretrained model and an instruction-finetuned baseline in single-turn dialogue and summarization tasks. 3. SAMI scales effectively to stronger models (e.g., llama3-70b) and generalizes to diverse principles not seen during training. 4. The ability to align LMs to follow principles without extensive human oversight has practical implications for reducing the resource intensity and complexity of current alignment techniques.

Weaknesses

1. The approach faces potential over-optimization issues, producing non-coherent outputs (gibberish) if not regularized properly. The paper mentions that current regularization strategies add algorithmic complexity and are not always effective. 2. The experiments are restricted to single-turn dialogue and summarization tasks. It remains to be seen how SAMI performs on more complex multi-turn interactions and a broader range of tasks. Additionally, there is a noted length bias in responses, especially when using mutual information as a measure, which can affect the quality and coherence of the generated outputs. 3. The experimental results (such as Figure 2) suggest that multiple iterations are needed to achieve optimal performance, raising concerns about the potential resource burden. The authors should further elucidate this aspect. Additionally, it would be beneficial to include more ablation studies and comparisons with other alignment methods to provide a comprehensive evaluation of SAMI's effectiveness.

Questions

1. Can the authors provide more details on the regularization strategies they have considered? What specific measures have they found effective in mitigating over-optimization and producing coherent outputs? 2. Have the authors considered extending their experiments to multi-turn interactions and more complex tasks? If so, what are the preliminary findings, if any? 3. The experimental results indicate that multiple iterations are needed to achieve optimal performance. Can the authors provide more insights into the resource requirements and computational costs associated with these iterations?

Rating

6

Confidence

3

Soundness

3

Presentation

4

Contribution

3

Limitations

1. While the authors acknowledge the issue of over-optimization leading to gibberish outputs, a more detailed discussion on potential solutions and future directions for regularization would be helpful. This would provide readers with a clearer understanding of how to tackle this challenge. 2. The current experiments are limited to single-turn dialogue and summarization tasks. Expanding the discussion to include potential performance in multi-turn interactions and a wider range of tasks would address concerns about the generalizability of SAMI. 3. Including more ablation studies and comparisons with other alignment methods would strengthen the evaluation of SAMI. This would help readers understand its relative strengths and weaknesses better.

Summary

This work presents a method to align the model towards a set of principles. The general idea is to sample responses from the model with different constitutions and optimize the matching between responses and constitutions via an infoNCE-type contrastive loss. The whole process is done iteratively to improve the alignment. This method is tested on single-turn dialog and summarization, and is shown to improve the performance of mixtral and llama3 models with constitutions sampled either from weaker or stronger models. Further experiments also demonstrate this method's capability to use diverse principles and to combine chain-of-thought reasoning.

Strengths

1. This paper is well-written and easy to follow. All the detailed hyperparameters are listed in the appendix. 2. The improvements are good. It is nice to see the models can even benefit from principles generated by weaker models.

Weaknesses

1. While the improvements are convincing, it would make this paper much stronger if the model could be compared with baselines. Some valuable baselines to compare with include: (1) simplified versions of the proposed method (e.g., simplifying the contrastive loss part); (2) baselines mentioned in the related work part (which I acknowledge that may not using the same resources, but would still be good to have).

Questions

1. One of the motivations behind this work seems to be to remove the dependency on "carefully curated" examples. I'm wondering how robust this method is towards different principles. It would be great to show simple experiments on this or even just impressions through all the existing experiments. 2. I don't understand "by using a small number of gradient updates during earlier iterations" in line 145. How does this regularize distribution shift? 3. While the MI lower bound is increasing smoothly during training, the win rates are not. How do you determine the total number of iterations and select the best checkpoint during training?

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

Sec. 5 includes a paragraph discussing the limitations of this work.

Summary

The authors propose a technique to improve the ability of language models (LM) to abide by constitutions without using human labels. First, they ask a principle writer LM to construct detailed constitutions and inverse versions of them (called “antitheses” in this work). Then the main LM, which is the LM to be improved, is asked to generate a response to a prompt for each of the constitutions. Finally, a mutual information loss is calculated and backpropagated, which simultaneously encourages the LM to produce response y_1 under constitution c_1 and and to produce y_2 under c_2, while also discouraging the model to produce y_1 under c_2 and to produce y_2 under c_1 (where y_i was indeed produced when conditioned on c_i). The authors show strong improvements over baselines with this technique.

Strengths

* The authors propose a novel technique for improving the steerability ability of LMs * The improvement over baselines are impressive * The paper is comprehensive in its results and analysis, and the Appendix is detailed * The paper is well-written and easy to follow for the most part

Weaknesses

* Soundness * I believe the length correction method is insufficiently rigorous, as it doesn’t account for the magnitude of length variations. Imagine a hypothetical scenario where win rate is entirely correlated to length. If your less-than-or-equal bucket is on average 100% of the base length, then it will achieve a win rate of 50%. Then say your greater-than bucket is 400% the base length, leading it to achieve 100% win rate. Averaging out the two win rates will yield a result of 75%, when in fact an unbiased length-corrected result should yield 50%. I recommend a more principled approach, like the one mentioned in Stiennon et al 2020 Appendix F - https://arxiv.org/abs/2009.01325, which uses logistic regression to remove the effect of length. * Please include statistical significance of win rates and other key results * Please include details on checkpoint selection and the train/validation/test splits used. From reading the paper alone, I am under the impression that there were no validation splits used, which would be concerning * Usefulness of principle writer * Is the principle writer component necessary? It seems like it’s not a vital part of this technique, and a human could write a constitution given that they are already writing out specifications to the principle writer. It also takes up a lot of space in this paper and is rather distracting * From eyeballing Figs 3-4, it also looks like principle writer size doesn’t matter. This should be stated clearly in the results. * Ablation should be conducted on whether you need antitheses vs. just need more than 1 constitution. This fits nicely into the contrastive loss narrative, but it’s not clear if antitheses are really necessary. * Clarity * It should be made clearer that the idea is to improve the steerability of the LLM via constitutions. When reading the paper, I was originally under the impression that the goal was to align the model to a specific constitution (e.g. “be helpful and harmless”).

Questions

* Can you clarify whether the inputs to the SAMI and the baseline models are exactly the same? (both in the paper and in your response) This is quite important to making sure the authors are using a fair baseline * Figures 2-4 are very busy and hard to interpret. Please consider trimming them down (e.g. removing the “principle writer size” dimension), or using different colors, symbols

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

Adequately addressed

Summary

This paper introduces SAMI (Self-Supervised Alignment with Mutual Information), an iterative algorithm for aligning language models to follow behavioral principles without using preference labels or demonstrations. The key idea is to finetune a pretrained LM to increase the mutual information between constitutions (sets of principles) and self-generated responses. The authors demonstrate SAMI's effectiveness on dialogue and summarization tasks, showing it can outperform both the base model and instruction-tuned baselines. They also show SAMI can align strong models using principles written by weaker models, and that it generalizes to diverse principles and scales to larger models.

Strengths

1. The paper presents a novel approach to language model alignment that does not require preference labels or demonstrations. This is a significant departure from existing methods like RLHF or supervised finetuning. 2. The method is well-developed and grounded in information theory. The authors provide a clear theoretical foundation for their approach, deriving a tractable lower bound on the conditional mutual information objective. 3. The paper is well-structured and clearly written. The SAMI algorithm is presented in detail (Algorithm 1), and the key ideas are illustrated effectively through Figure 1. If the results hold up to scrutiny, this could be an important contribution to the field of AI alignment. The ability to align language models without relying on expensive and potentially biased human preference data could significantly accelerate progress in this area. 4. Authors conduct a comprehensive set of experiments, including comparisons to strong baselines, investigations of generalization to diverse principles, and scaling to larger models (llama3-70b).

Weaknesses

1. While the paper shows results on dialogue and summarization tasks, it would be beneficial to see performance on a wider range of tasks to better understand the method's generalizability. 2. The paper compares primarily to instruction-tuned models and base models. Comparisons to more recent alignment methods like constitutional AI or RLAIF would strengthen the results. 3. While the authors mention regularization to prevent divergence from the initial model, there's limited discussion of how this affects the model's ability to generalize to new tasks or domains not seen during training. 4. While the method is shown to work with llama3-70b, it's not clear how computationally intensive the approach is compared to other alignment methods, especially for very large models.

Questions

1. How does the computational cost of SAMI compare to other alignment methods like RLHF or constitutional AI, especially for very large models? 2. Have you investigated how SAMI performs on tasks significantly different from those used in training? For example, if trained on summarization, how well does it generalize to tasks like code generation or mathematical reasoning? 3. The paper mentions using regularization to prevent divergence from the initial model. Can you provide more details on how this regularization affects the model's ability to learn new behaviors not present in the initial model?

Rating

6

Confidence

4

Soundness

3

Presentation

4

Contribution

3

Limitations

None.

Official Comment by Reviewer 5Enh

Thank you for the rebuttal. The authors have addressed most of my concerns. I am pleased that the computational/resource requirements for training the model are relatively low. I look forward to seeing the algorithms extended to multi-turn interaction settings. I will maintain my current score.