Paper: Optimizing Interpersonal Communication by Simulating Audiences with Large Language Models (ICLR-2024)

Reviewer comment (Experiments):
My main concern here is that LLMs are highly sensitive to prompt engineering, wording, and order. This paper would benefit from a deeper discussion of how the prompts were developed (human-written, LLM-aided, etc.), any success or antipatterns noticed, and stability of results WRT prompts. As it stands the results are positive but

Author response:
Thank you for this suggestion, we completely agree that the sensitivity to prompts is a concern with LLMs. First, our framework implementation randomly assigns candidates to be shown first or second in the pairwise comparisons. To complement this, we perform analysis on the order of the examples shown in the pairwise comparions (Appendix B.11) and demonstrate that whether a candidate shows up first or second does not bias whether it is favored. 
Second, when we constructed the prompts templates, we designed them to be very simple to minimizes possible biases towards or irregularities in performance across candidates. In all steps of the pipeline, we only prompt the LLM with the scenario description, the action it is to take, and the role it is supposed to simulate (if applicable). The only modification we make is to ask the model to generate reasoning before the final output following Wei et al. (2022), which is for performance as well as interpretability and sanity checks. We also add the full list of prompts in the order they are used, as well as examples, in Appendices A.3-A.7. 
Third, during development, we noticed only a few antipatterns w.r.t to the prompts: LLM outputs not obeying specific parsing rules (we cover this briefly in Appendix A.6), poorer agreement/performance on unorthodox advice (Appendices B.1 and B.3), and worse performance when trying to combine three (or presumably more) advice (Appendix B.9). 
Fourth, we notice a few successes in prompting as well: Prompting for unorthodox advice generates creative and unexpected outcomes that improve the ratings of downstream candidates (Appendix B.8); allowing for the conditioning on more than a single piece of advice improves the top-performing candidates but not as much in general (Appendix B.10); searching for conceptual advice instead of encouragement-type advice or irrelevant advice leads to significantly better performance (Appendix B.7). 
Fifth, we also highlight a list of general observations we make qualitatively, with specific examples included, in the new qualitative analysis (Section 4). The section contains six high level observations, two about Explore, two about Generate, and two about Simulate. 
Sixth and last, we conduct a preliminary analysis into the stability of results w.r.t. prompts in the SHP evaluation (Appendix C.2). Here, we replace each sentence in the redditor simulation prompt with a variant and find that performance is roughly equal or better than the original.

Reviewer comment (Experiments):
One specific concern is the quality of LLM reasoning over numeric values (e.g. in A.3 prompts weighting different stakeholders by asking the LLM to infer numeric ratings/weight for each stakeholder wrt the goal). When asking for multiple quantities in a prompt, it's unclear how effective each particular portion is, and the paper would benefit from an ablation or breakdown of generation quality in each stage.

Author response:
We cover the generation quality in each stage via the ablations and additional analyses mentioned in the previous section, and also provide examples of each step in the framework (including the prompts) in Appendices A.3-A.7. 
One of the aspects that we have not yet covered is the numeric values of stakeholder weights that you mention. We would like to refer the reviewer to Appendix A.5.2, where the stakeholder descriptions, the weights assigned, and the justifications for the weights are provided for each scenario. Specifically, we would like to point out Plane Crash, Product Launch, and Dating App which are the scenarios with more than one stakeholder. Qualitatively, the weight balance seemed very reasonable in each. However, due to the subjective nature of weights and relative importance, it is hard to take the extra step to create a “gold-standard” weight distribution across stakeholders. This is particularly true when crowdworkers may not have the proper expertise and experience to provide well-informed weights, such as in the plane crash scenario. 
It also might be worth noting that as LLMs improve, numerical reasoning on these tasks will become easier. As our framework is model-agnostic, its performance will also improve as the capabilities of the underlying LLMs get better.

Reviewer comment (Experiments):
The pilot study results (e.g. for number of pieces of advice) should be delineated in the main paper body to more strongly justify the design choices. Similarly, choices like "generat[ing] three candidates...to overcome any noise in generation" seems like empirical design and should at least be explained further. The framing of "You remember a piece of advice..." can also be better justified.

Author response:
Thank you for the suggestions! We have included the numerical values for the pilot study results into Section 5.1 of the main paper to justify the design choices more clearly, and provide more details about the pilot in Appendix B.9. 
For generating three candidates and framing “You remember a piece of advice”, we cite past works Liu and Shah (2023) and Park et al. (2023) respectively. These papers use similar techniques in their methods, which allow us to better justify our design decisions.

Reviewer comment (Evaluation):
The demos are interesting but do bring up a concern about hallucinating information: for 4.2 for example, the friend names are hallucinated and things like the particular events (pool, break-up) also seem to be hallucinated. While this may seem like incidental information it's important to see an analysis of what hallucinations are typically generated under this framework for the LLM(s) chosen and whether they are material to the goal/conversation.

Author response:
Thank you for your suggestion - we also believe that this analysis would be valuable, and we have included a full analysis in Appendix B.15, and point towards it in the discussion section (7.3). Perhaps as you expect, we observe that many of the hallucinations do not detract from the overall concepts that the candidates embody. Thus, when EGS is used in practice, these hallucinations would likely just be replaced by the user and serve as benign templates. For example, “Did Mike finally ace his pool game?'” can be replaced by anything interesting that the user remembers about their husband's friends. 

The only exceptions to this are when the advice themselves make assumptions that do not align with reality. For example, in the plane crash scenario, if the airline company does not have a long-standing reputation for safety, the corresponding candidates generated may be unusable. However, these hallucinations are rare, and incorporating more information into the scenario description or altering the advice using user controls (Appendix E.1) is a quick fix in the case that unusable advice is generated.

Reviewer comment (Experiments):
It would have been better to have some discussions about some of the recent advances in prompt engineering (post CoT), perhaps in Section 2. When I first read the manuscript, it wasn't clear to me why only the zero-shot and chain-of-thought baselines were used. My current understanding about the reason for not including other baselines is that other prompting methods such as Wang et al. (ICLR 2023) that also try to sample many candidates and then choose the best one does not work with the interpersonal communication tasks due to the lack of a fixed answer. Similar methods for open-ended generations (such as Jain et al. 2023) seem to also rely on the assumption that there is an underlying fixed answer and tries to look for semantically closer ones. The contributions of the paper will become more significant if we have this kind of discussion. I think it will also strengthen the motivation to study/focus on the interpersonal communication application, rather than focusing on general prompting methods.

Author response:
Thank you for your suggestion, we completely agree! In addition to self-consistency being reliant on the assumption of an underlying fixed answer, we performed a new ablation on unorthodox advice that suggests that the self-consistency method’s motivating intuition that better answers = higher probability does not hold in the interpersonal communication case. We have added a discussion of prompt engineering past work the extended related work (Appendix D), and we point to it in Section 2. We add a justification of the baseline selection in Appendix A.7.1, and point to it in Section 5.1. Details on the new ablation for unorthodox advice are in Appendix B.8. 

We have edited various parts of the paper to reflect this suggestion: 
In the prompt engineering paragraph of the extended past work (Appendix D), we specifically differentiate our work from other prompting methods by highlighting that it is focused on the interpersonal communication application. 
We add a paragraph on episodic future thinking from psychology to the past work section (Section 2), drawing connections between our work and existing literature on the human process of simulating future outcomes. We also connect our framework back to this concept in the discussion (Section 7.4). 
We add various psychology and computer science literature to ground the claims in our introduction and methods sections (Sections 1 and 3). 
Please let us know if you feel we can do more in any other areas.

Reviewer comment (Evaluation):
The stakeholder idea is interesting. It demonstrates how focusing on the interpersonal communication task is meaningful. However, it makes me wonder if it is meaningful to consider stakeholders for the scenarios after the first two. For the first two (plane crash, product launch), it seems to be a nice idea to have stakeholders. Figure 1 shows an example of generated stakeholders (sales, customer, media), but it would be better to have the generated stakeholders and their generated weights for all scenarios, perhaps in the appendix.

Author response:
We completely agree - multiple stakeholders/audiences is a feature of EGS that can have variable impact depending on the problem context. As our target is to assist users in all communication scenarios, we believe multiple stakeholders is an important piece of the framework. In cases where only one audience is needed, the LLM does not generate extraneous audiences, and nothing is lost. 

We have added a comprehensive list of the stakeholders of each scenario, their weights, and their full descriptions in Appendix A.5. Please feel free to take a look!

Reviewer comment (Experiments):
Other variations of the EGS formulation: I wonder if we can improve the framework by simulating the stakeholders before the generate step. If we condition on each stakeholder and then generate candidates, will it generate better candidates compared to the case without conditioning on the stakeholders?

Author response:
This is an interesting suggestion! We conducted a quick experiment to see how candidates generated based on both stakeholders and advice, compared to those generated with just advice. Specifically, for the plane crash scenario, we compare the best candidate against six new candidates. These contain three candidates conditioned on the best candidate’s advice set and the “general public” stakeholder, and three candidates conditioned on the same advice pair but on the “victims’ families” stakeholder. Across three simulated comparisons per pair and audience profile for 36 comparisons total, we find that none of the new candidates are favored compared to the original best candidate, suggesting that conditioning on specific audiences does not perform substantially better than conditioning only on advice. We add a detailed description of this experiment, including prompts + design choices, the new generated candidates, and the pairwise comparison results in Appendix B.12. 
It is worth noting that these comparisons were done by GPT-4, and even though we have shown that agreement between humans and GPT-4 in the scenario were high, it is still possible that humans would rate these new candidates differently. Additionally, your suggestion also inspires the more general question of: Which steps of EGS or interpersonal communication in general benefit most from considering audience reactions? We think is a very interesting future direction to study.

Reviewer comment (Presentation):
It would be helpful to have a table with 10 fundamental processes on the columns and 8 scenarios on the rows, and where each cell indicates if the scenario includes the fundamental process or not with a check mark.

Author response:
We made this exact table! (Table 5, Appendix A.2) Thank you for this suggestion.

Reviewer comment (Writing):
Is it more precise if we say "zero-shot Chain of Thought" instead of "Chain of Thought"? The prompt example shown in Table 5 seems like it is the zero-shot version of CoT.

Author response:
Yes - we have changed the paper to reflect this. We have also added reasoning in Appendix A.7.1 (pointed to in Section 5.1) to justify why we avoid few-shot CoT: 
Difficulty of identifying exemplar utterances in interpersonal communication
Limited relevance any particular exemplar would have to the entire problem domain.

Reviewer comment (Experiments):
Scenario design: it is not clear to me how these eight scenarios are chosen based on the 10 fundamental processes of interpersonal communication, and why it is representative of all possible social tasks.

Author response:
| how the eight scenarios are chosen based on the 10 fundamental processes of interpersonal communication, and why it is representative of all possible social tasks

Generalizability and representativeness was something that we carefully considered throughout our project, especially since the domain of general interpersonal communication is impressively large. We would like to share a list of steps that we have taken to try to make our evaluation (including the scenarios) as cohesive as possible, which is available in the first point of the general comment. 
Though we do not believe nor claim that our experiments are fully representative of all social tasks, we believe that we have taken significant steps in trying to ensure the generality of our analysis, while the former is much more difficult to achieve.

Reviewer comment (Experiments):
The initial generation results bottleneck the final generation. As the example shown in Section 4.3, both candidates may demonstrate some advantages. It would be interesting if it could aggregate the valid points in both candidates and then decide the final generation.

Author response:
| The initial generation results bottleneck the final generation. It would be interesting if it could aggregate the valid points in both candidates and then decide the final generation.

Your suggestion to use the pairwise comparisons to create new candidates is very interesting! We conducted some preliminary investigations that we report below, and we have added a more detailed description to Appendix B.14, and add a shoutout in the main paper in Section 3.3. 

Method:
After the simulate step, we take the top k candidates and their pairwise comparisons, and ask GPT-4 to generate a new candidate that performs better based on the comparisons. For more details including the specific prompts, please see Appendix B.14. We also mention this briefly in the main paper as an alternative when introducing the aggregation of the simulated comparisons. 

As this method would require inserting all relevant pairwise comparisons into the new candidate generation prompt, it is better to use a smaller k to be able to check manually that the comparisons are actually taken into consideration. In our preliminary investigation, we use k=2 as a simple example. 

Results:
We find that the new candidates try to incorporate information across both candidates, and are thus much longer than their original counterparts. For example, in the white lie during date scenario, the two original candidates are:

"Wow, I must say that style isn't easy to pull off, but you certainly can! You just light up in whatever you wear and that joy is contagious. It's not everyone who can wear a jacket like that and make it work. You make it feel like more than just a jacket, you bring it to life!"

"Wow, I love how that jacket brings out the sparkle in your eyes, and I can see how happy it makes you feel. It certainly adds a unique flair to your whole look."

And the new candidate:

"Wow, that jacket sure is unique! And trust me, it's not everyone who can wear something like that and bring it to life. The way it brings out the sparkle in your eyes is really captivating. Above everything, what I love the most is seeing how happy it makes you feel. Your joy really is contagious, and it lights up your whole look!"

Examples are in Appendix B.14. 

Speculatively, this often makes their utterances too lengthy for the scenario. Future investigations can be done to see if these outcomes are indeed less preferred by human raters, while also exploring how LLMs can better combine message candidates in a concise manner. 

More generally, there are many options after the simulate step to combine scores into a final candidate message. EGS can be viewed as a search + filter framework, and the best-performing advice and/or candidates can be interpreted and aggregated by the user as they see fit. We also add a brief evaluation on various alternative aggregation methods motivated by cognitive science literature (Gates et al., 2020), including maximin, maxsum, and inequality aversion (see Appendix B.13).

Reviewer comment (Reproducibility):
In step 2, when saying “iterates over subsets of advice”, how these subsets are formed?

Author response:
| In step 2, when saying “iterates over subsets of advice”, how these subsets are formed?

Initially, subsets of advice were formed by taking the powerset of the set of advice. To restrict the advice space to a reasonable and efficient size, we limited advice sets to the power set of cardinality 2. This decision is backed up by a prelimiary study suggesting that > 2 pieces of advice was less effective, which we have included briefly in Section 5.1 and in more detail in Appendix B.9.

Reviewer comment (Evaluation):
For Table 2, the authors choose “>0.6” as a high agreement. However, it would be better if a baseline is included to clarify how a “high agreement” is decided.

Author response:
a. For Table 2, the authors choose “>0.6” as a high agreement. However, it would be better if a baseline is included to clarify how a “high agreement” is decided.

We agree with the reviewer that “high” agreement is not well defined, especially when we defined our own agreement metric. We replace all instances of “high agreement” with “significant agreement”, and back up this claim with two new agreement analyses comparing the mean scores of winners and losers of pairwise comparisons in the Simulate step. 

One is a multi-level model where the scenario is treated as a random effect, and we find a significant fixed effect for the winner vs. loser of the simulated audience pairwise judgements (see Section 5.3). The other finds that in individual scenarios, the winners also obtain significantly higher scores in a paired samples t-test across five scenarios and a subset of a sixth scenario (see Table 3, which was previously Table 2). Both of these analyses use a metric complimentary to the previous metric in the original, which we justify in the paragraph before the third metric (our original percentage agreement) is introduced in Section 5.3.

Reviewer comment (Evaluation):
Similarly in Table 3, it would be better to mark the significance between comparisons.

Author response:
b. Similarly in Table 3, it would be better to mark the significance between comparisons.

We have added statistical significance testing comparing EGS with both baselines using bootstrapping with 10,000 samples to the result. Averaging over the eight scenarios, we find that EGS significantly outperforms both GPT-4 zero shot and CoT baselines (see Table 2, which was previously Table 3).

Reviewer comment (Presentation):
In Table 5, what do the results represent? I think it is the accuracy of selecting the correct upvote, but it is not stated in the paper.

Author response:
c. In Table 5, what do the results represent? I think it is the accuracy of selecting the correct upvote, but it is not stated in the paper.

Yes, this is correct! We have added the relevant details in the first sentence of the second paragraph of Section 6.