Papers by Jessica Hoffmann
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics (2024.lrec-main)
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Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain, Erin MacMurray van Liemt, Kathleen Meier-Hellstern, Lucas Dixon
| Challenge: | a growing audience of users is engaging with LLM-driven chatbots. |
| Approach: | They propose a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia’s Neutral Point of View principle. |
| Outcome: | The proposed methods detect errors in the tuned LLM responses even when no training data is available. |
Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL (2025.emnlp-main)
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Jessica Hoffmann, Christiane Ahlheim, Zac Yu, Aria Walfrand, Jarvis Jin, Marie Tano, Ahmad Beirami, Erin MacMurray van Liemt, Nithum Thain, Hakim Sidahmed, Lucas Dixon
| Challenge: | Parameter-efficient reinforcement learning (PE-RL) is a highly effective training regime to improve large language models’ ability to answer queries on sensitive topics with a Neutral Point of View (NPOV). |
| Approach: | They propose to use parameter-efficient reinforcement learning to train large language models to answer queries with a Neutral Point of View (NPOV) This is compared to the strongest baseline, LoRA finetuning, SFT and RLHF. |
| Outcome: | The proposed training regime improves on NPOV quality and scores higher on features identified by linguists as key to separating good answers from the best answers. |
Towards Agile Text Classifiers for Everyone (2023.findings-emnlp)
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Maximilian Mozes, Jessica Hoffmann, Katrin Tomanek, Muhamed Kouate, Nithum Thain, Ann Yuan, Tolga Bolukbasi, Lucas Dixon
| Challenge: | Text-based safety classifiers are widely used for content moderation and increasingly to tune generative language model behavior. |
| Approach: | They propose to use small, targeted datasets to train safety classifiers using small, iterative datasets that can be quickly developed for a particular policy. |
| Outcome: | The proposed method can be quickly developed for a specific policy with a labeled dataset of as few as 80 examples. |