Executive summary: Sentient Futures introduces AnimalHarmBench 2.0, a redesigned benchmark for evaluating large language models’ (LLMs) moral reasoning about animal welfare across 13 dimensions—from moral consideration and harm minimization to epistemic humility—providing a more nuanced, scalable, and insight-rich tool for assessing how models reason about nonhuman suffering and how training interventions can improve such reasoning.
Key points:
Motivation for update: The original AnimalHarmBench (1.0) measured LLM outputs’ potential to cause harm to animals but lacked insight into underlying reasoning, scalability, and nuanced evaluation—issues addressed in version 2.0.
Expanded evaluation framework: AHB 2.0 scores models across 13 moral reasoning dimensions, including moral consideration, prejudice avoidance, sentience acknowledgement, and trade-off transparency, emphasizing quality of reasoning rather than legality or refusal to answer.
Improved design and usability: The new benchmark uses curated questions, customizable run settings on Inspect AI, and visual radar plots for comparative analysis, supporting faster and more interpretable assessments.
Results: Among major models tested, Grok-4-fast was most animal-friendly (score 0.704), Claude-Haiku 4.5 the least (0.650), and Llama 3.1 8B Instruct improved from 0.555 to 0.723 after receiving 3k synthetic compassion-focused training data—showing that targeted pretraining can enhance animal welfare reasoning.
Significance: The benchmark enables researchers to evaluate and improve LLMs’ ethical reasoning toward animals—an area unlikely to self-correct through market feedback—and could inform broader AI alignment work that includes nonhuman welfare.
Next steps: Future benchmarks aim to test more complex and realistic reasoning contexts, integrating animal welfare considerations alongside other AI-related ethical tradeoffs.
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Executive summary: Sentient Futures introduces AnimalHarmBench 2.0, a redesigned benchmark for evaluating large language models’ (LLMs) moral reasoning about animal welfare across 13 dimensions—from moral consideration and harm minimization to epistemic humility—providing a more nuanced, scalable, and insight-rich tool for assessing how models reason about nonhuman suffering and how training interventions can improve such reasoning.
Key points:
Motivation for update: The original AnimalHarmBench (1.0) measured LLM outputs’ potential to cause harm to animals but lacked insight into underlying reasoning, scalability, and nuanced evaluation—issues addressed in version 2.0.
Expanded evaluation framework: AHB 2.0 scores models across 13 moral reasoning dimensions, including moral consideration, prejudice avoidance, sentience acknowledgement, and trade-off transparency, emphasizing quality of reasoning rather than legality or refusal to answer.
Improved design and usability: The new benchmark uses curated questions, customizable run settings on Inspect AI, and visual radar plots for comparative analysis, supporting faster and more interpretable assessments.
Results: Among major models tested, Grok-4-fast was most animal-friendly (score 0.704), Claude-Haiku 4.5 the least (0.650), and Llama 3.1 8B Instruct improved from 0.555 to 0.723 after receiving 3k synthetic compassion-focused training data—showing that targeted pretraining can enhance animal welfare reasoning.
Significance: The benchmark enables researchers to evaluate and improve LLMs’ ethical reasoning toward animals—an area unlikely to self-correct through market feedback—and could inform broader AI alignment work that includes nonhuman welfare.
Next steps: Future benchmarks aim to test more complex and realistic reasoning contexts, integrating animal welfare considerations alongside other AI-related ethical tradeoffs.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.