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

arXiv: https://arxiv.org/abs/2503.03654 · HTML: https://mlsanity.com/paper/2503.03654/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [LoRA Learns Less and Forgets Less](https://mlsanity.com/paper/2405.09673/) (2024)
- [Theoretical guarantees on the best-of-n alignment policy](https://mlsanity.com/paper/2401.01879/) (2024)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [Automatically Neutralizing Subjective Bias in Text](https://mlsanity.com/paper/1911.09709/) (2019)
- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting](https://mlsanity.com/paper/2310.16523/) (2023)
- [Understanding User Experience in Large Language Model Interactions](https://mlsanity.com/paper/2401.08329/) (2024)
- [Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements](https://mlsanity.com/paper/2402.10614/) (2024)
- [Towards Agile Text Classifiers for Everyone](https://mlsanity.com/paper/2302.06541/) (2023)
- [DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues](https://mlsanity.com/paper/2310.18130/) (2023)
- [Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics](https://mlsanity.com/paper/2403.08904/) (2024)
- [Design Challenges for a Multi-Perspective Search Engine](https://mlsanity.com/paper/2112.08357/) (2021)
- [A Bias Aware News Recommendation System](https://mlsanity.com/paper/1803.03428/) (2018)
- [Controversy in Context](https://mlsanity.com/paper/1908.07491/) (2019)
- [Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit](https://mlsanity.com/paper/2212.00339/) (2022)

## Built on by (most cited first, 3 of 3)

- [AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections](https://mlsanity.com/paper/2505.12455/) (2025)
- [Think Again! The Effect of Test-Time Compute on Preferences, Opinions, and Beliefs of Large Language Models](https://mlsanity.com/paper/2505.19621/) (2025)
- [Who Defines "Best"? Towards Interactive, User-Defined Evaluation of LLM Leaderboards](https://mlsanity.com/paper/2604.21769/) (2026)

## Abstract (arXiv)

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.