Summary of Llms Will Always Hallucinate, and We Need to Live with This, by Sourav Banerjee et al.
LLMs Will Always Hallucinate, and We Need to Live With This
by Sourav Banerjee, Ayushi Agarwal, Saloni Singla
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 argues that Large Language Models (LLMs) have inherent limitations, specifically hallucinations, which are not just occasional errors but an inevitable feature of these systems. The authors demonstrate that hallucinations stem from the fundamental mathematical and logical structure of LLMs, making it impossible to eliminate them through architectural improvements, dataset enhancements, or fact-checking mechanisms. The paper draws on computational theory and Godel’s First Incompleteness Theorem to establish the mathematical certainty of hallucinations in every stage of the LLM process, from training data compilation to text generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that Large Language Models (LLMs) are not perfect and can make mistakes. The authors found that these models can make up information they didn’t learn from their training data. This is because the models’ math and logic allow them to do this. It’s like trying to solve a puzzle with missing pieces – sometimes you might fill in the wrong pieces, even if you’re really good at solving puzzles. The paper says that this problem can’t be fixed by making the model better or giving it more data. |
Keywords
» Artificial intelligence » Text generation
