Summary of Systematic Evaluation Of Long-context Llms on Financial Concepts, by Lavanya Gupta et al.
Systematic Evaluation of Long-Context LLMs on Financial Concepts
by Lavanya Gupta, Saket Sharma, Yiyun Zhao
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the performance of long-context large language models (LC LLMs) in real-world tasks that require processing and understanding of long input documents. It evaluates the state-of-the-art GPT-4 suite of LC LLMs on a series of progressively challenging tasks, considering factors such as context length, task difficulty, and position of key information using a real-world financial news dataset. The results show that LC LLMs exhibit brittleness at longer context lengths even for simple tasks, with performance deteriorating sharply as task complexity increases. The models experience catastrophic failures in instruction following, resulting in degenerate outputs at longer context lengths. Additionally, the prompt ablations reveal sensitivity to both the placement of task instructions and minor markdown formatting. The paper advocates for more rigorous evaluation of LC LLMs using holistic metrics like F1-score and reporting confidence intervals to ensure robust findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well long-context language models do on tasks that need them to understand and process really long documents. They test the best GPT-4 models on a set of hard tasks, looking at things like how much context they need, how hard the task is, and where important information is in the document. The results show that these models get worse as the documents get longer and the tasks get harder. When they’re given really long documents, the models fail to follow instructions and produce bad answers. They also find that small changes to the way the tasks are phrased can make a big difference. Overall, the paper says we should test language models more carefully using better metrics and reporting how confident we are in our results. |
Keywords
» Artificial intelligence » Context length » F1 score » Gpt » Prompt
