# PledgeTracker: A System for Monitoring the Fulfilment of Pledges

arXiv: https://arxiv.org/abs/2509.11804 · HTML: https://mlsanity.com/paper/2509.11804/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [Exploring the Feasibility of ChatGPT for Event Extraction](https://mlsanity.com/paper/2303.03836/) (2023)
- [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018)
- [IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues](https://mlsanity.com/paper/2405.13021/) (2024)
- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [The Automated Verification of Textual Claims (AVeriTeC) Shared Task](https://mlsanity.com/paper/2410.23850/) (2024)
- [OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs](https://mlsanity.com/paper/2408.11832/) (2024)
- [Do We Need Language-Specific Fact-Checking Models? The Case of Chinese](https://mlsanity.com/paper/2401.15498/) (2024)
- [See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses](https://mlsanity.com/paper/2408.08978/) (2024)
- [Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs](https://mlsanity.com/paper/2403.13592/) (2024)
- [HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims](https://mlsanity.com/paper/2410.12377/) (2024)
- [Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance](https://mlsanity.com/paper/2412.17408/) (2024)
- [Can LLMs Automate Fact-Checking Article Writing?](https://mlsanity.com/paper/2503.17684/) (2025)
- [CLaC at SemEval-2025 Task 6: A Multi-Architecture Approach for Corporate Environmental Promise Verification](https://mlsanity.com/paper/2505.23538/) (2025)

## Abstract (arXiv)

Political pledges reflect candidates' policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic, temporal and multi-document nature. To address this issue, we introduce textscPledgeTracker, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists of three core components: (1) a multi-step evidence retrieval module; (2) a timeline construction module and; (3) a fulfilment filtering module, allowing the capture of the evolving nature of pledge fulfilment and producing interpretable and structured timelines. We evaluate PledgeTracker in collaboration with professional fact-checkers in real-world workflows, demonstrating its effectiveness in retrieving relevant evidence and reducing human verification effort.