6/ But do these gains hold across cultures? 🗾

🫂 We measure cultural parity across diverse groups — and find that Multi-Agent Debate not only boosts average accuracy but also leads to more equitable cultural alignment 🌍

5/ How do model decisions evolve through debate?

We track three phases of LLM behavior:
💗 Initial decision correctness
💚 Final decision correctness
💙 Judge’s decision correctness

✨ Multi-Agent Debate is most valuable when models initially disagree!

4/ 🔥 Distinct LLMs are complementary!

We find that:
🤯 Multi-Agent Debate lets smaller LLMs (7B) match the performance of much larger ones (27B)
🏆 Best combo? Gemma-2 9B + EXAONE-3 7B 💪

3/ Before bringing in two #LLMs, we first 📈 maximize single-LLM performance through:

1️⃣ Cultural Contextualization: adding relevant rules-of-thumb for the target culture
2️⃣ Self-Reflection: evaluating and improve its own outputs

These serve as strong baselines before we introduce collaboration 🤝

1/ Are two #LLMs better than one for equitable cultural alignment? 🌍

We introduce a Multi-Agent Debate framework — where two LLM agents debate the cultural adaptability of a given scenario.

#ACL2025 🧵👇

7/ Can AskQE handle naturally occurring translation errors too? 🍃

Yes! It shows:
💁‍♀️ Stronger correlation with human judgments
✅ Better decision-making accuracy than standard QE metrics

6/ 🤖 What kinds of questions does AskQE generate?

Most commonly:
📏 Extent — How many COVID-19 cases were reported today? (24.6%)
💡 Concept — What is another name for paracetamol? (23.6%)

5/ 🔥 We test AskQE on ContraTICO and find:

📉 It effectively distinguishes minor to critical translation errors
👭 It aligns closely with established quality estimation (QE) metrics

4/ We introduce ContraTICO, a dataset of 8 contrastive MT error types in the COVID-19 domain 😷🦠

⚠️ Minor errors: spelling, word order, synonym, intensifier, expansion (no impact)
📛 Critical errors: expansion (impact), omission, alteration

3/ AskQE has two main components:

❓ Question Generation (QG): conditioned on the source + its entailed facts
❕ Question Answering (QA): based on the source and backtranslated MT

If the answers don’t match... there's likely an error ⚠️

1/ How can a monolingual English speaker 🇺🇸 decide if an automatic French translation 🇫🇷 is good enough to be shared?

Introducing ❓AskQE❓, an #LLM-based Question Generation + Answering framework that detects critical MT errors and provides actionable feedback 🗣️

#ACL2025

6/ 🧑‍⚖️ Do humans actually prefer translations of simplified inputs?

Yes! They rated these to be:
📝 More contextually appropriate
👁️ Easier to read
🤗 More comprehensible
compared to translations of original inputs!

5/ What does input rewriting actually change? 🧐

Here are 3 key findings:
1️⃣ Better translatability trades-off meaning preservation
2️⃣ Simplification boosts both input & output readability 📖
3️⃣ Input rewriting > Output post-editing 🤯

3/ 🔍 Which rewriting strategy works best?

Simpler texts are easier to translate!
But... simplification isn't always a win for MT quality 😞

🚨 New Paper 🚨

1/ We often assume that well-written text is easier to translate ✏️

But can #LLMs automatically rewrite inputs to improve machine translation? 🌍

Here’s what we found 🧵