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Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL

Jessica Hoffmann, Christiane Ahlheim, Zac Yu, Aria Walfrand, Jarvis Jin, Marie Tano, Ahmad Beirami, Erin van Liemt, Nithum Thain, Hakim Sidahmed, Lucas Dixon

TL;DR

The paper addresses the challenge of generating Neutral Point of View (NPOV) responses to sensitive topics by proposing Parameter-Efficient Reinforcement Learning (PE-RL) with LoRA adapters. It introduces SHQ-NPOV, a small but high-quality dataset, and demonstrates that PE-RL substantially improves NPOV quality and linguistic depth compared with baselines like LoRA SFT, SFT, RLHF, and Best-of-10, with strong generalization to out-of-distribution topics. A key finding is that data-efficient PE-RL can avoid overfitting in a low-data regime and still generalize, especially when initialized from a LoRA SFT checkpoint. The authors release SHQ-NPOV and a reproducible methodology for iterative dataset creation, positioning PE-RL as a practical path toward safer, more informative multi-perspective AI responses, while acknowledging open questions about neutrality definitions, data sourcing, and environmental costs.

Abstract

The paper shows that parameter-efficient reinforcement learning (PE-RL) is a highly effective training regime to improve large language models' (LLMs) ability to answer queries on sensitive topics with a Neutral Point of View (NPOV), i.e. to provide significantly more informative, diverse and impartial answers. This is shown by evaluating PE-RL and multiple strong baselines-including LoRA finetuning (strongest baseline), SFT and RLHF. PE-RL not only improves on overall NPOV quality compared to the strongest baseline ($97.06\%\rightarrow 99.08\%$), but also scores much higher on features linguists identify as key to separating sufficient answers from "great'' answers ($60.25\%\rightarrow 85.21\%$ for presence of supportive details, $68.74\%\rightarrow 91.43\%$ for absence of oversimplification). A qualitative analysis corroborates this. Moreover, our evaluation also finds a key property of PE-RL for this task: unlike methods that update all parameters, it generalises out of topic. Finally, to enable further studies we also release the dataset, SHQ-NPOV, and provide a methodology to create such datasets through iterative rounds of human peer-critique and annotator training.

Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL

TL;DR

The paper addresses the challenge of generating Neutral Point of View (NPOV) responses to sensitive topics by proposing Parameter-Efficient Reinforcement Learning (PE-RL) with LoRA adapters. It introduces SHQ-NPOV, a small but high-quality dataset, and demonstrates that PE-RL substantially improves NPOV quality and linguistic depth compared with baselines like LoRA SFT, SFT, RLHF, and Best-of-10, with strong generalization to out-of-distribution topics. A key finding is that data-efficient PE-RL can avoid overfitting in a low-data regime and still generalize, especially when initialized from a LoRA SFT checkpoint. The authors release SHQ-NPOV and a reproducible methodology for iterative dataset creation, positioning PE-RL as a practical path toward safer, more informative multi-perspective AI responses, while acknowledging open questions about neutrality definitions, data sourcing, and environmental costs.

Abstract

The paper shows that parameter-efficient reinforcement learning (PE-RL) is a highly effective training regime to improve large language models' (LLMs) ability to answer queries on sensitive topics with a Neutral Point of View (NPOV), i.e. to provide significantly more informative, diverse and impartial answers. This is shown by evaluating PE-RL and multiple strong baselines-including LoRA finetuning (strongest baseline), SFT and RLHF. PE-RL not only improves on overall NPOV quality compared to the strongest baseline (), but also scores much higher on features linguists identify as key to separating sufficient answers from "great'' answers ( for presence of supportive details, for absence of oversimplification). A qualitative analysis corroborates this. Moreover, our evaluation also finds a key property of PE-RL for this task: unlike methods that update all parameters, it generalises out of topic. Finally, to enable further studies we also release the dataset, SHQ-NPOV, and provide a methodology to create such datasets through iterative rounds of human peer-critique and annotator training.

Paper Structure

This paper contains 53 sections, 7 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Dataset creation and annotations
  4. Dataset overview
  5. Defining the NPOV task
  6. Methodology for writing examples
  7. Crowd-annotation procedure
  8. 1190 extra queries for evaluation
  9. Experimental setup
  10. Methods and baselines
  11. PE-RL:
  12. PE-RL + LoRA SFT:
  13. LLM $\sim$20B:
  14. LoRA SFT:
  15. SFT:
  16. ...and 38 more sections

Figures (7)

  • Figure 1: Pipeline to create Neutral Point of View (NPOV) answers to queries on sensitive topics.
  • Figure 2: Distribution of L1 distance between annotators' score and NPOV score. 75% of answers have less than 0.50 difference with the NPOV score, and 90% of answers less than 1.
  • Figure 3: Fractions of examples in the SHQ-NPOV dataset our autoraters labeled with "Supportive Details" or with "Oversimplification" by NPOV score. Only examples labeled "NPOV" (score $\geq 3$) are shown. These fractions can serve as a proxy to predict the NPOV score.
  • Figure 4: Life of an example.
  • Figure 5: Difference between results on in-distribution topics and out-of-distribution topics for the PE-RL + LoRA SFT + preamble model. All results are within the 95% confidence interval of each other.
  • ...and 2 more figures