# From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls

arXiv: https://arxiv.org/abs/2410.01039 · HTML: https://mlsanity.com/paper/2410.01039/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://mlsanity.com/paper/2210.07197/) (2022)
- [Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https://mlsanity.com/paper/2307.05300/) (2023)
- [ChatGPT as a Factual Inconsistency Evaluator for Text Summarization](https://mlsanity.com/paper/2303.15621/) (2023)
- [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://mlsanity.com/paper/1911.11408/) (2019)
- [ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts](https://mlsanity.com/paper/2210.12467/) (2022)
- [Modeling financial analysts' decision making via the pragmatics and semantics of earnings calls](https://mlsanity.com/paper/1906.02868/) (2019)
- [Embarrassingly Simple Unsupervised Aspect Extraction](https://mlsanity.com/paper/2004.13580/) (2020)
- [Enhancing Biomedical Lay Summarisation with External Knowledge Graphs](https://mlsanity.com/paper/2310.15702/) (2023)

## Built on by (most cited first, 6 of 6)

- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [Template-Based Financial Report Generation in Agentic and Decomposed Information Retrieval](https://mlsanity.com/paper/2504.14233/) (2025)
- [Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth](https://mlsanity.com/paper/2509.03867/) (2025)
- [GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification](https://mlsanity.com/paper/2410.01169/) (2024)
- [AnalysisBank: An Expert Analysis Pattern Library for Financial Report Generation](https://mlsanity.com/paper/2609.00818/) (2026)
- [A Citation-Grounded Benchmark for Trustworthy Earnings Call Transcript Analysis with Large Language Models](https://mlsanity.com/paper/2610.00969/) (2026)

## Abstract (arXiv)

This paper explores the use of Large Language Models (LLMs) in the generation and evaluation of analytical reports derived from Earnings Calls (ECs). Addressing a current gap in research, we explore the generation of analytical reports with LLMs in a multi-agent framework, designing specialized agents that introduce diverse viewpoints and desirable topics of analysis into the report generation process. Through multiple analyses, we examine the alignment between generated and human-written reports and the impact of both individual and collective agents. Our findings suggest that the introduction of additional agents results in more insightful reports, although reports generated by human experts remain preferred in the majority of cases. Finally, we address the challenging issue of report evaluation, we examine the limitations and strengths of LLMs in assessing the quality of generated reports in different settings, revealing a significant correlation with human experts across multiple dimensions.