📢 Life update:
I’m now a visiting researcher at @mila-quebec.bsky.social and @etsmtl.bsky.social in Montréal for the next year and a half! 🍁✨

Prompting helps: moral + linguistic prompts increase inclusive outputs and reduce the male-female gap. But it's no silver bullet: quality can drop, and even the best setup yields proper 🇫🇷 markers (like iel or un.e) in only ≤11% of inclusive cases. We need more than vibes.

9/11

🔍 Singular “they” is hardest. In 246 test cases, models output plural “ils/elles” 50–90 % of the time or gendered “il/elle”. Even with inclusive prompts, “iel” appears only sporadically. Diagnostics matter!

8/11

📊 Results: male translations lead, female trail, inclusive dead last—in every configuration. Bias is systemic, not model‑specific.

7/11

🛠️ Benchmarked Gemma‑2B, Mistral‑7B, Llama 3‑8B & 70B under 4 prompts (baseline, moral, linguistic, moral+ling). Metrics: BLEU, COMET and a custom pronoun/agreement checker → 16 slices x 2 scores = deep bias audit!

6/11

📦 FairTranslate contains 2 418 EN‑FR sentence pairs covering 62 occupations. Every example appears in three gender variants (male, female, inclusive) and carries metadata on stereotype, ambiguity type, and more. All human‑annotated for reliability.

3/11

🤔 Most benchmarks for gender bias in machine translation focus on binary gender (male/female). FairTranslate introduces a new resource for evaluating how LLMs handle non-binary gender in English→French, a language where gender is deeply grammaticalized.

2/11

So proud to have given a talk on Explainability for Fairness at the prestigious Henri Poincaré Institute in Paris during the Mathematical Statistics Day. Thank you @statfr.bsky.social for the invitation! 🙋‍♀️

For those submitting a paper to FAccT or for organizers: the papers are in single-column format again? I thought that had changed since last year?