Summary of Ali-agent: Assessing Llms’ Alignment with Human Values Via Agent-based Evaluation, by Jingnan Zheng et al.
ALI-Agent: Assessing LLMs’ Alignment with Human Values via Agent-based Evaluation
by Jingnan Zheng, Han Wang, An Zhang, Tai D. Nguyen, Jun Sun, Tat-Seng Chua
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Large Language Models (LLMs) can generate harmful content when misaligned with human values, posing significant risks. Current evaluation benchmarks use expert-designed scenarios to assess alignment, but these tests are labor-intensive and limited in scope, making it hard to generalize to real-world use cases. To address this challenge, we propose ALI-Agent, an adaptive evaluation framework that leverages LLM-powered agents to conduct in-depth assessments. ALI-Agent consists of two stages: Emulation and Refinement. During Emulation, the agent automates test scenario generation using a memory module; in Refinement, it refines scenarios to probe long-tail risks. Experiments demonstrate ALI-Agent’s effectiveness in identifying model misalignment across three human value aspects (stereotypes, morality, and legality). Systematic analysis shows that generated test scenarios represent meaningful use cases, including enhanced measures for probing long-tail risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can sometimes create harmful content. To make sure this doesn’t happen, researchers have been trying to come up with better ways to evaluate these models. The problem is that current methods are too labor-intensive and only test a limited number of scenarios. This makes it hard to predict how the models will behave in real-world situations. To fix this issue, scientists created a new framework called ALI-Agent. It uses artificial intelligence agents to generate realistic testing scenarios and refine them over time. The results show that ALI-Agent is effective in detecting when language models are not aligned with human values. |
Keywords
» Artificial intelligence » Alignment
