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AI Research
arXiv CS.AI·Jun 10·5 min read
Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs
TL;DR
Existing benchmarks for evaluating large language models (LLMs) often overlook subtle forms of misleading communication that do not involve outright falsehoods. Janus is a new benchmark designed to assess goal-conditioned information distortion in LLM outputs by comparing neutral and goal-directed scenarios using a fixed set of facts.
✦ Why It Matters
Engineers can use Janus to evaluate and improve the transparency and reliability of LLM outputs in real-world applications.
Key Takeaways
- Use the JANUS benchmark to assess LLM performance on information distortion.
- Focus on subtle biases in LLM outputs, not just blatant inaccuracies.
- Explore the impact of goal framing on model responses.
- Incorporate findings to improve transparency and reliability in AI communications.
How It Works
JANUS operates by providing a fixed pool of facts for each scenario, allowing researchers to compare outputs from neutral and goal-directed prompts. This design isolates the effects of selective emphasis and omission, enabling a clearer understanding of how LLMs can distort information based on specific objectives.