Newish work (arXived in December): Prompts can be ambig., but handling ambiguity is context/user dependent. Sometimes the right thing is to ask a clarifying question, sometimes to give multi. answers, and sometimes to just guess. Can we train steerable models that change their strategy per context?

For more discussion, please see the paper! arxiv.org/abs/2602.24188 While AI models may struggle to collaborate, at Google DeepMind my collaborators are proactive and fully coherent. Thanks to @fantinehuot.bsky.social , Adam Fisch, @jonathanberant.bsky.social, and Mirella Lapata!

MT-PingEval: Evaluating Multi-Turn Collaboration with Private Information Games

We present a scalable methodology for evaluating language models in multi-turn interactions, using a suite of collaborative games that require effective communication about private information. This e...

arxiv.org

AI systems are also overconfident, terminating dialogues long before exhausting their turn budget - even after explicit reminders.

So how well do today's models do? To answer this, we design a new multi-turn scaling analysis, called *isotoken evaluation*: fix a total token budget, and partition it into variable numbers of turns. Performance should be non-decreasing in the number of turns... and yet!

We believe these games are more naturalistic and proactive than most existing multi-turn evaluations, which often employ user simulators to create multi-turn user-assistant scenarios. Here's another game, which requires answering a question about two privately-held images.

This task is part of 🏓MT-PingEval, a new benchmark of verifiable collaborative private information games that involve multi-turn dialogue. In this game, the "describer" sees only a single image, and the "guesser" has to identify which one it is.

Are AI models effective collaborators, or mere assistants awaiting your next command? (Preprint: arxiv.org/abs/2602.24188) To find out, we make AI collaborate with itself, in private information games: tasks that require sharing private information, like this chess board ordering task.

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.

Cheap but noisy? Or accurate but expensive? How to split a limited annotation budget between different types of judges?👩‍⚖️🤖🦧 www.arxiv.org/abs/2506.07949

Cost-Optimal Active AI Model Evaluation

The development lifecycle of generative AI systems requires continual evaluation, data acquisition, and annotation, which is costly in both resources and time. In practice, rapid iteration often makes...

arxiv.org

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"

We all want LLMs to collaborate with humans to help them achieve their goals. But LLMs are not trained to collaborate, they are trained to imitate. Can we teach LM agents to help humans by first making them help each other? arxiv.org/abs/2503.14481

Don't lie to your friends: Learning what you know from collaborative self-play

To be helpful assistants, AI agents must be aware of their own capabilities and limitations. This includes knowing when to answer from parametric knowledge versus using tools, when to trust tool outpu...

arxiv.org

A way to help models "be aware of their own capabilities and limitations" from @jacobeisenstein.bsky.social et al: arxiv.org/abs/2503.14481 #MLSky

Fun work led by @amouyalsamuel.bsky.social and with Aya. Coming in I didn't think LLMs should have difficulties with answering questions on some of the GP sentences we used, but turns out they had! See Samuel's thread for more info...

@amouyalsamuel.bsky.social · 2y ago

The old man the boat. You probably had to read that sentence twice. It's because it's a garden path (GP) sentence. GP sentences are read slower and often misunderstood. This begs the questions: 1. Why are these sentences harder to process? 2. How do LLMs deal with them?

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

When the LM misunderstood the human chuckled: Analyzing garden path effects in humans and language models

Modern Large Language Models (LLMs) have shown human-like abilities in many language tasks, sparking interest in comparing LLMs' and humans' language processing. In this paper, we conduct a detailed c...

arxiv.org

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.

The old man the boat. You probably had to read that sentence twice. It's because it's a garden path (GP) sentence. GP sentences are read slower and often misunderstood. This begs the questions: 1. Why are these sentences harder to process? 2. How do LLMs deal with them?

Inference-time procedures (e.g. Best-of-N, CoT) have been instrumental to recent development of LLMs. Standard RLHF focuses only on improving the trained model. This creates a train/inference mismatch. 𝘊𝘢𝘯 𝘸𝘦 𝘢𝘭𝘪𝘨𝘯 𝘰𝘶𝘳 𝘮𝘰𝘥𝘦𝘭 𝘵𝘰 𝘣𝘦𝘵𝘵𝘦𝘳 𝘴𝘶𝘪𝘵 𝘢 𝘨𝘪𝘷𝘦𝘯 𝘪𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦-𝘵𝘪𝘮𝘦 𝘱𝘳𝘰𝘤𝘦𝘥𝘶𝘳𝘦? Check out below.

Excited to share 𝐈𝐧𝐟𝐀𝐥𝐢𝐠𝐧! Alignment optimization objective implicitly assumes 𝘴𝘢𝘮𝘱𝘭𝘪𝘯𝘨 from the resulting aligned model. But we are increasingly using different and sometimes sophisticated inference-time compute algorithms. How to resolve this discrepancy?🧵

We’re really excited to release this large collaborative work for unifying web agent benchmarks under the same roof. In this TMLR paper, we dive in-depth into #BrowserGym and #AgentLab. We also present some unexpected performances from Claude 3.5-Sonnet

I will also be at NeurIPS! Happy to chat about post-training, reasoning, and interesting ways you use multiple agents for things.