Come check out our tutorial on multilingual and multicultural LLMs today at 2pm #EMNLP2026
…6-multilingual-cultural-llm.github.io
…6-multilingual-cultural-llm.github.io
Alan Ritter
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Computing professor at Georgia Tech - NLP, ML, AI
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En route to Budapest 🇭🇺 for #EMNLP2026! Come chat at our posters in Hall H:
Monday, Oct 26:
9:00–10:30 — Harm amplification in LLM interactions
arxiv.org/abs/2606.02423
11:00–12:30 — LaRA: detecting data contamination in RL post-training
arxiv.org/abs/2605.29888
16:45–18:15 — Who Pays More for Safety?
arxiv.org/abs/2608.22490
Tuesday, Oct 27:
9:00–10:30 — Camellia: cultural biases in LLMs for Asian languages
arxiv.org/abs/2510.05291
Looking forward to seeing everyone!
Monday, Oct 26:
9:00–10:30 — Harm amplification in LLM interactions
arxiv.org/abs/2606.02423
11:00–12:30 — LaRA: detecting data contamination in RL post-training
arxiv.org/abs/2605.29888
16:45–18:15 — Who Pays More for Safety?
arxiv.org/abs/2608.22490
Tuesday, Oct 27:
9:00–10:30 — Camellia: cultural biases in LLMs for Asian languages
arxiv.org/abs/2510.05291
Looking forward to seeing everyone!
arxiv.org Investigating and Alleviating Harm Amplification in LLM Interactions Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their capabilities... It’s great to see so much excitement around self-distillation!
However, some recent work highlights an important failure mode: when the teacher is given the solution, it can suppress verification, backtracking, and exploration, which make reasoning models effective.
We encountered a closely related issue. A teacher conditioned on an expert solution may skip crucial steps, or rationalize its way to the provided answer.
We just got back from presenting this work at ICML 🇰🇷. Our method combines mixed-policy generation with a contrastive objective to discourage shortcuts while still learning effectively from expert solutions on the most challenging instances.
However, some recent work highlights an important failure mode: when the teacher is given the solution, it can suppress verification, backtracking, and exploration, which make reasoning models effective.
We encountered a closely related issue. A teacher conditioned on an expert solution may skip crucial steps, or rationalize its way to the provided answer.
We just got back from presenting this work at ICML 🇰🇷. Our method combines mixed-policy generation with a contrastive objective to discourage shortcuts while still learning effectively from expert solutions on the most challenging instances.
Excited to be heading to ICML! I will be presenting our paper on rethinking how to train models with expert solutions using self-distillation.
🗓️ July 8, 2026, 5:00 PM – 6:45, Hall A #2502View quoted post on X
Excited to attend #ICML2026 in Seoul 🇰🇷 together with my Ph.D. students: @EthanMendes3, @hyungjoochae, @CherylolGuo!
We’ll be presenting work on:
- Multilingual reasoning
- Self-distillation from expert reasoning
- Cloning synthetic web environments
Poster info + paper links below. Looking forward to catching up!
We’ll be presenting work on:
- Multilingual reasoning
- Self-distillation from expert reasoning
- Cloning synthetic web environments
Poster info + paper links below. Looking forward to catching up!
📣 New paper by my Ph.D. student @hyungjoochae
How can we build a safe, scalable learning environment for web agents?
Paper: arxiv.org/abs/2603.10505
We introduce VERIENV, which clones real websites into executable synthetic environments using coding agents.
How can we build a safe, scalable learning environment for web agents?
Paper: arxiv.org/abs/2603.10505
We introduce VERIENV, which clones real websites into executable synthetic environments using coding agents.
