Summary of Characterizing Multimodal Long-form Summarization: a Case Study on Financial Reports, by Tianyu Cao et al.
Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
by Tianyu Cao, Natraj Raman, Danial Dervovic, Chenhao Tan
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a systematic analysis of large language models’ (LLMs) abilities and behavior in summarization, specifically focusing on financial report summarization. The authors propose a computational framework for characterizing multimodal long-form summarization and investigate the performance of various LLMs, including Claude 2.0/2.1, GPT-4/3.5, and Cohere. The study finds that GPT-3.5 and Cohere struggle to perform this task meaningfully, while Claude 2 and GPT-4 exhibit a position bias in their summarization strategies. The authors also conduct an investigation on the use of numeric data in LLM-generated summaries, identifying a phenomenon they call “numeric hallucination.” To improve the performance of GPT-4 in handling numbers, the authors employ prompt engineering with limited success. Overall, the study highlights the capabilities of Claude 2 in handling long multimodal inputs compared to GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how large language models can summarize long texts, like financial reports. The authors test different models, including Claude and GPT, to see what they’re good at and where they struggle. They find that some models are better than others at summarizing certain types of information. They also look at how well the models do when it comes to using numbers and statistics in their summaries. Overall, the study shows that Claude is particularly good at handling long texts with lots of different types of information. |
Keywords
* Artificial intelligence * Claude * Gpt * Hallucination * Prompt * Summarization
