# On the Evaluation of Machine-Generated Reports

arXiv: https://arxiv.org/abs/2405.00982 · HTML: https://mlsanity.com/paper/2405.00982/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 99 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 30 of 34)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://mlsanity.com/paper/2102.01454/) (2021)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2311.09476/) (2023)
- [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021)
- [Internet-Augmented Dialogue Generation](https://mlsanity.com/paper/2107.07566/) (2021)
- [QuALITY: Question Answering with Long Input Texts, Yes!](https://mlsanity.com/paper/2112.08608/) (2021)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018)
- [Hurdles to Progress in Long-form Question Answering](https://mlsanity.com/paper/2103.06332/) (2021)
- [A Survey on Retrieval-Augmented Text Generation](https://mlsanity.com/paper/2202.01110/) (2022)
- [BooookScore: A systematic exploration of book-length summarization in the era of LLMs](https://mlsanity.com/paper/2310.00785/) (2023)
- [REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory](https://mlsanity.com/paper/2212.05221/) (2022)
- [A Critical Evaluation of Evaluations for Long-form Question Answering](https://mlsanity.com/paper/2305.18201/) (2023)
- [Retrieval Augmented Visual Question Answering with Outside Knowledge](https://mlsanity.com/paper/2210.03809/) (2022)
- [Successive Prompting for Decomposing Complex Questions](https://mlsanity.com/paper/2212.04092/) (2022)
- [SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore](https://mlsanity.com/paper/2308.04430/) (2023)
- [Visualize Before You Write: Imagination-Guided Open-Ended Text Generation](https://mlsanity.com/paper/2210.03765/) (2022)
- [Text Generation with Exemplar-based Adaptive Decoding](https://mlsanity.com/paper/1904.04428/) (2019)
- [Neural Machine Translation with Phrase-Level Universal Visual Representations](https://mlsanity.com/paper/2203.10299/) (2022)
- [Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval](https://mlsanity.com/paper/2212.10526/) (2022)
- [RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization](https://mlsanity.com/paper/2109.07943/) (2021)

## Built on by (most cited first, 23 of 23)

- [Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track](https://mlsanity.com/paper/2406.16828/) (2024)
- [CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning](https://mlsanity.com/paper/2503.13517/) (2025)
- [The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models](https://mlsanity.com/paper/2504.15068/) (2025)
- [FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents](https://mlsanity.com/paper/2504.13128/) (2025)
- [MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2410.13716/) (2024)
- [Auto-ARGUE: LLM-Based Report Generation Evaluation](https://mlsanity.com/paper/2509.26184/) (2025)
- [DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking](https://mlsanity.com/paper/2502.20730/) (2025)
- [Controlled Retrieval-augmented Context Evaluation for Long-form RAG](https://mlsanity.com/paper/2506.20051/) (2025)
- [Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets](https://mlsanity.com/paper/2503.09902/) (2025)
- [Principled Context Engineering for RAG: Statistical Guarantees via Conformal Prediction](https://mlsanity.com/paper/2511.17908/) (2025)
- [FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAG](https://mlsanity.com/paper/2601.05866/) (2026)
- [Overview of the TREC 2025 RAGTIME Track](https://mlsanity.com/paper/2602.10024/) (2026)
- [Incorporating Q&A Nuggets into Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.13222/) (2026)
- [LANCER: LLM Reranking for Nugget Coverage](https://mlsanity.com/paper/2601.22008/) (2026)
- [Seeing Through the MiRAGE: Evaluating Multimodal Retrieval Augmented Generation](https://mlsanity.com/paper/2510.24870/) (2025)
- [Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?](https://mlsanity.com/paper/2601.13227/) (2026)
- [Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage](https://mlsanity.com/paper/2603.08819/) (2026)
- [DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation](https://mlsanity.com/paper/2605.04458/) (2026)
- [Cross-Document Event-Keyed Summarization](https://mlsanity.com/paper/2410.14795/) (2024)
- [Supporting Humans in Evaluating AI Summaries of Legal Depositions](https://mlsanity.com/paper/2601.15182/) (2026)
- [MetaphorVU: Towards Metaphorical Video Understanding](https://mlsanity.com/paper/2605.25461/) (2026)
- [Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability](https://mlsanity.com/paper/2605.28522/) (2026)
- [Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI tools](https://mlsanity.com/paper/2607.24793/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose complete, accurate, and verifiable long-form reports. Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users. In this perspective paper, we draw together opinions from industry and academia, and from a variety of related research areas, to present our vision for automatic report generation, and -- critically -- a flexible framework by which such reports can be evaluated. In contrast with other summarization tasks, automatic report generation starts with a detailed description of an information need, stating the necessary background, requirements, and scope of the report. Further, the generated reports should be complete, accurate, and verifiable. These qualities, which are desirable -- if not required -- in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. To test completeness and accuracy, the framework uses nuggets of information, expressed as questions and answers, that need to be part of any high-quality generated report. Additionally, evaluation of citations that map claims made in the report to their source documents ensures verifiability.