4arXiv cs.CL (Computation and Language)·
AutoJourn: LLM pipeline for multi-perspective news generation with bias detection and neutralisation
AutoJourn is a demonstration system that uses LLMs to generate balanced news summaries from social media discussions, incorporating perspective extraction, multi-viewpoint summarisation, and sentence-level bias detection and neutralisation. The pipeline combines prompt engineering with optional retrieval augmentation and is evaluated on semantic diversity, summary quality, and bias reduction metrics. The work targets responsible automated journalism and ships with a publicly accessible live demo.
Related guides (1)
Related events (8)
Defining and Evaluating Political Bias in LLMs
OpenAI has published a post describing their methodology for evaluating political bias in ChatGPT, introducing new real-world testing approaches aimed at improving objectivity and reducing bias. The piece outlines how OpenAI defines political bias in the context of large language models and the evaluation frameworks they are developing to measure it. This represents OpenAI's public commitment to systematic bias measurement as a component of responsible deployment.
Data2Story: Multi-agent framework for end-to-end data journalism with verifiable claims
Researchers introduce Data2Story (Data Journalist Agent), a multi-agent framework that orchestrates specialized roles to transform raw data into multimodal news articles. A key innovation is an Inspector module that grounds every claim back to data, code, or external references, enabling verifiability. The system also generates interactive multimodal outputs (maps, audio) rather than static text and charts. Evaluation across 18 articles with 53 human participants shows competitive quality versus expert-written pieces, with particular strength in transparency and auditability, though human journalists retain an edge in editorial angle and creative design.
AI-Mediated Communication Can Steer Collective Opinion via LLM Editing Biases
This paper demonstrates empirically that LLMs from multiple model families introduce directional biases when editing human-written texts on contested topics (e.g., nudging toward gun control, against atheism). The authors develop a mathematical opinion-dynamics model showing these biases are amplified through social networks, shifting collective opinion at scale. An audit of X's 'Explain this post' feature finds evidence of pro-life bias in Grok's outputs on abortion content, traced to specific design choices. The paper concludes with implications for EU legislative efforts on AI-mediated communication.
Training-free mixture-of-agents framework combines LLMs and knowledge graphs for multi-document summarization
A new arXiv preprint proposes a training-free multi-agent framework for multi-document summarization (MDS) that decomposes the task into specialized agents for extractive selection, knowledge-aware abstraction, and iterative refinement, unified via a multi-perspective consistency mechanism. The system integrates LLMs with knowledge graphs without task-specific fine-tuning. Experiments across four datasets in English and Vietnamese show state-of-the-art or competitive performance, with the authors emphasizing cross-domain and cross-lingual generalization.
Multi-agent system using open-source LLMs outperforms GPT-4 on disinformation detection
A new arXiv preprint proposes a multi-agent system for automated disinformation detection that emulates human annotator decision-making through consensus mechanisms, cognitive diversity, and hierarchical structure. The system uses open-source models (LLaMA, Kimi, Qwen, DeepSeek, LLaMA-Nemotron) and is evaluated on English, Polish, Slovak, and Bulgarian datasets across three fact-checking tasks. Results claim superior performance over individual LLMs including GPT-4 and GPT-3.5, with transparency benefits from using open weights models.
Tree-of-Thoughts hybrid approach for legal case judgement summarization using LLMs
A new arXiv preprint proposes a tree-of-thoughts-inspired extractive-abstractive summarization method for legal case judgements. The authors evaluate DeepSeek and LLaMA models across extractive, abstractive, and hybrid summarization strategies, finding the hybrid prompt approach yields better summaries. The work addresses a narrow but practically relevant domain application of LLMs in legal NLP.
LLMs fail to consistently simulate demographic perspective-taking in hate speech annotation
A new arXiv paper evaluates whether persona-conditioned LLMs can replicate how different demographic groups perceive hate speech, testing three dimensions: inter-group disagreement, in-group sensitivity, and vicarious prediction. No model consistently captures all three dimensions, and performance is highly model-dependent rather than emerging reliably from identity prompts alone. Vicarious prompting with Llama 3.1 provides the closest approximation to human disagreement patterns across demographic axes. The findings have implications for using LLMs as proxies for diverse human annotators in content moderation tasks.
LLUMI: Fine-Tuning Open-Source LLMs for Mental Health Writing Assistance Using Reddit Community Feedback
LLUMI is a two-component system (a generation model and an improvement model) designed to provide mental health writing assistance using smaller open-source LLMs hosted in privacy-preserving, on-premise environments. The system leverages Reddit community endorsement signals (upvotes/downvotes) to construct preference pairs for SFT and DPO training, then further aligns outputs via human evaluation across readability, empathy, connection, actionability, and safety dimensions. Results show LLUMI achieves performance comparable to proprietary GPT-based models on linguistic and human evaluations, suggesting community-derived preference signals can substitute for expensive expert labeling in sensitive domains.