Summary of Veritas: a Unified Approach to Reliability Evaluation, by Rajkumar Ramamurthy et al.
VERITAS: A Unified Approach to Reliability Evaluation
by Rajkumar Ramamurthy, Meghana Arakkal Rajeev, Oliver Molenschot, James Zou, Nazneen Rajani
First submitted to arxiv on: 5 Nov 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 This paper addresses a crucial limitation in Large Language Models (LLMs), which often struggle to accurately respond due to their inability to synthesize context information. To overcome this issue, the authors introduce VERITAS, a family of fact-checking models designed for flexible operation across various contexts while minimizing latency and costs. Unlike existing open-access models, which are task-specific, or closed-access models like GPT-4 and Claude, which offer flexibility but are hindered by high costs and latency, VERITAS achieves state-of-the-art results on major hallucination detection benchmarks. Specifically, it shows a 10% increase in average performance compared to similar-sized models, rivaling the performance of GPT-4 turbo with LLM-as-a-judge setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make large language models more reliable by creating a new system that can detect when they’re making things up. These models often struggle to give accurate answers because they don’t understand their context well enough. The new system, called VERITAS, can work in different situations and doesn’t cost too much or take too long. It’s better than other similar systems at detecting when a model is making something up, which is important for things like verifying the accuracy of online information. |
Keywords
» Artificial intelligence » Claude » Gpt » Hallucination
