Summary of Generating Tables From the Parametric Knowledge Of Language Models, by Yevgeni Berkovitch et al.
Generating Tables from the Parametric Knowledge of Language Models
by Yevgeni Berkovitch, Oren Glickman, Amit Somech, Tomer Wolfson
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 This paper explores the ability of large language models (LLMs) to generate accurate and factual tables from parametric knowledge. While LLMs excel in recreating knowledge bases and generating free-form text, table generation remains a challenge. The study evaluates four state-of-the-art LLMs – GPT-3.5, GPT-4, Llama2-13B, and Llama2-70B – using three prompting methods: full-table, row-by-row, and cell-by-cell. A novel benchmark, WikiTabGen, containing 100 curated Wikipedia tables is introduced for evaluation. Tables are manually annotated with short natural language descriptions to ensure factual correctness. The findings reveal that table generation remains a challenge, with GPT-4 achieving the highest accuracy at 19.6%. The study highlights how various table properties influence generation performance and provides an evaluation framework for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models can create tables from information they know. While these models are great at writing and understanding text, creating tables is still a challenge. Researchers tested four of the best LLMs using different ways to ask them to make tables. They also created a new way to test table-making called WikiTabGen, which has 100 Wikipedia tables with descriptions. The study found that making tables is still hard, even for the best models. It also shows how things like table size and content affect how well the models do. |
Keywords
» Artificial intelligence » Gpt » Prompting
