Hacker News: Killed by LLM

Jan 6, 2025

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Source URL: https://r0bk.github.io/killedbyllm/
Source: Hacker News
Title: Killed by LLM

Feedly Summary: Comments

AI Summary and Description: Yes

Summary: The provided text discusses a methodology for documenting benchmarks related to Large Language Models (LLMs), highlighting the inconsistencies among various performance scores. This is particularly relevant for professionals in AI security and LLM security, as it touches on the credibility of benchmarks used to evaluate AI models, which is crucial for ensuring safe and reliable AI deployment.

Detailed Description: The text outlines an initiative to accurately capture significant benchmarks when evaluating Large Language Models (LLMs), stressing the importance of accurate attributions and reporting in this context. The discussion centers around the metric discrepancies observed among different sources for the model “Qwen-2.5-72B-instruct,” exemplifying the challenges in the benchmarking landscape for AI.

Key points include:

– **Benchmark Discrepancies**:
– The text lists various scores from different sources for the “Qwen-2.5-72B-instruct” model:
– Qwen’s technical report: 83.1
– Stanford’s HELM: 79.0
– Huggingface’s Open LLM Leaderboard: 38.7
– The significant deviation in these scores signals potential issues in benchmarking methodologies.

– **Addressing Inconsistencies**:
– The author encourages community engagement to report discrepancies, suggesting the need for collaboration in refining benchmark assessments.

– **Sources for Benchmarks**:
– The approach to gather benchmarks includes:
– Author’s original papers or technical reports.
– Subsequent benchmark papers that discuss performance (e.g., comparing SQuAD versions).
– Third-party sources, which provide additional data points for validation.

This focus on ensuring reliable benchmarks is crucial for security and compliance professionals working with AI, as it influences trust in AI systems and their deployment in secure environments. Reliable metrics are foundational to risk assessments and impact evaluations in AI security practices.

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