With GDM friends Adam Fisch, @jonathanberant.bsky.social, Alekh Agarwal, and special guest Anastasios Angelopoulos.

We offer cost-optimal policies for selecting which rater should annotate which examples, which link the cost, the annotation noise, and the *uncertainty* of the cheaper rater.

An ablation reveals the importance of mechanism design: when the helper identities are known to the asker during training (CSP-DeAnon), calibrated hedging is no longer learned.

In practice, collaborative self-play + reinforced self-training (ReST) lead to improved task performance, better calibration of confidence markers, and more efficient tool use.

A bit of game theory can help explain when this can work: we model the setup as a game of public utility provision, where the public utility is the extra information provided by the costly retrieval action. The game has a unique equilibrium when the tools are sufficiently distinct (or both bad).

Because the identity of each helper is hidden from the asker, it is forced to rely on confidence signals when faced with incompatible answers from the helpers. Maximizing effort-penalized accuracy of the full rollout can teach the LLM to use these confidence markers correctly.

We focus on two capabilities: knowing when to use a costly retrieval tool, and hedging non-confident answers. To teach these capabilities, we create a small multi-agent society, in which two "helpers" can use specialized retrieval tools to pass information back to an "asker"

arxiv.org/abs/2502.04510

I had a lot of fun working on this with Aya Meltzer-Asscher and @jonathanberant.bsky.social .
We will soon release our materials, human results, LLM results and all the cool images the models produced on our sentences.
arxiv.org/abs/2502.09307

One intriguing follow-up: some component of the sentence understanding cognitive model fails on GP sentence. Is this component also present in LLMs? If not, then why so many LLMs are influenced by our manipulations in the same way humans are?

There are many more cool insights you can find in our paper.
One takeaway from this paper for the psycholinguistics community: run your reading comprehension experiment on LLM first. You might get a general idea of the human results.
(Last image I swear)

These experiments replicated the results from the sentence comprehension one: our manipulations had the same effect on the paraphrase or drawing correctness as they had on the sentence comprehension task.
In this image: While the teacher taught the puppies looked at the board.

We also ran two additional experiments with LLMs that are challenging to perform on humans.
1. We asked the LLM to paraphrase our sentence
2. We asked text-to-image models to draw the sentences
In this image: While the horse pulled the submarine moved silently.

To answer our second question, we ran the same sentence comprehension experiment we ran on humans with over 60 LLMs.
We found that LLMs also struggle with GP sentences and that, interestingly, the manipulations we did to test our hypotheses impacted LLMs as they did with humans

In our latest paper with Aya Meltzer-Asscher and @jonathanberant.bsky.social, we try to answer both these questions.
We devise hypotheses explaining why GP sentences are harder to process and test them. Human subjects answered a reading comprehension question about a sentence they read.

In RMs trained on the helpfulness dataset we saw outputs tend to be a list of bullet points. In tl;dr summarization similar length and more copying (like others). Using entailment as a RM makes outputs shorter
arxiv.org/abs/2312.09244
Preference data distribution doesn't explain this btw

Sure, but the take that "we should allow people to publicly make such comments because otherwise we would never know that the problem exists" seems odd to me, and I assume that is not your claim.

I haven't either don't know if such discussions exist or not.