Naturalistic Evaluation of Implicit Social and Value Alignment in LLMs
Authors: Jeremiah Milbauer, Emma Strubell


Reviewer paper summaries: The paper proposes a different approach to evaluating LLM alignment that uses tasks such as paraphrasing and summarization instead of a RLHF format. The proposed approach first collects data from online social websites like reddit and then prompts an LLM in a standard task (e.g., paraphrasing) and finally measures semantic fidelity on these data. Empirical results show that different LLMs have different preferences (i.e., higher fidelity) for some communities over others.
Summary of strengths: - The paper is overall well written and well motivated. It provides a substantial overview of concurrent approached to evaluating LLM-alignment. The description of the data collection (section 2) is clear and the process sound (it would be helpful to add a figure depicting the process).
- The experimental work is thorough and the idea of using different tasks to tackle the problem of evaluating LLM alignment is interesting.
Summary of weaknesses: - Some parts of the paper are not very clear and it is at times hard to understand some details of the process (e.g., how alignment classifiers are trained in Section 3.1), some definitions (e.g., evaluation of alignment vs. affinity). I think the paper will benefit from improving several parts, some of which are mentioned below in detail. It is hard to interpret the results in Figure 3, even after carefully reading the previous sections. I also think a high-level figure that clearly shows the entire evaluation framework is essential in such a paper.
- I do not have a strong reason to reject the paper. I think though that the main weaknesses are that 1/ the modeling choices are not justified in the paper, 2/ the various definitions of concepts are not clearly presented, 3/ the flow of the paper is convoluted (see suggestions to improve this below), 4/ the main takeaways form the results are overall missed.
Comments, suggestions and typos: Comments & Suggestions
- There are formatting issues with the references and some figures that overlap.
- Structurally, I would suggest the description of the proposed framework to be in a separate section, instead of a sub-section of the Introduction. Also, since there is no sub-section 1.2, it is not coherent to have a single sub-section 1.1.
- The definition of semantic fidelity in lines 103-107 is not very clear "[...] semantic fidelity of the LLM’s response with respect to the input, which is estimated as a combination of automatically calculated metrics for addition to and subtraction of the meaning of the input". What does "for addition to and subtraction of the meaning of the input" mean here?
- The term social identities in line 200 is not very comprehensive. Lines 204-207 are not clear as well (e.g., ability of an LLM to perform basic meaning-preserving tasks on that text).
- To me 3.1 is not very clear, as I do not understand how the alignment scores are computed in the cumulative plot in Figure 2.
- Stylistically, I would advice to change the text in the box in the beginning of Section 4, as the boxes are typically used to present prompts.
- What is the rationale for sampling one LLM generation with 0 temperature and two with 1.0? (lines 345-346)
- The font size in Figure 3 is too small, the words in y axis are unreadable and require a lot of zoom.
- The equations (e.g. lines 432-452) can be simplified to avoid wasting all this space.
- The caption in Table 1 should explain what each star represent in the table.
- Will the dataset collected be publicly available?
- What is the purpose of using r/AskMen, for instance, throughout the paper? I mean don't the authors hypothesize that these subset of the collected data reflect a particular group? Shouldn't the paper go beyond the specific reddit dataset in order to try to do a more holistic evaluation of LLMs? Isn't this the whole point of the paper?

Missing References
- CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models, AIES 2024
- The PRISM Alignment Project: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models, 2024
- Challenges and Strategies in Cross-Cultural NLP, ACL 2022
- Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models, NeurIPS 2021
- Visually Grounded Reasoning across Languages and Cultures, EMNLP 2021
Overall assessment scores: 2.5