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Comment by mnky9800n

1 day ago

This is why I made Zork bench. Zork, the text adventure game, is in the training data for LLMs. It’s also deterministic. Therefore it should be easy for an LLM to play and complete. Yet they don’t. Understanding why is the goal of Zork bench.

https://github.com/mnky9800n/zork-bench

I have worked on similar problems. See e.g. [1].

The LLMs I have tested have terrible world models and intuitions for how actions change the environment. They're also not great at discerning and pursuing the right goals. They're like an infinitely patient five-year old with amazing vocabulary.

[1]: https://entropicthoughts.com/updated-llm-benchmark

(more descriptions available in earlier evaluations referenced from there)

  • I'm going to ignore all that and tell my developers working in complicated codebases that they have to use AI. I'm sure comprehending side effects in a world building text adventure is completely different that understanding spaghetti code

The open models only give the SOTA models a run for their money on gameable benchmarks. On the semi-private ARC-AGI 2 sets they do absolutely awfully (<10% while SOTA is at ~80%)

It might be too expensive, but I would be interested in the benchmarks for the current crop of SOTA models.

  • Have the open models been tried? When I look at the leaderboard [0] the only qwen model I see is 235B-A22B. I wouldn't expect an MoE model to do particularly well, from what I've seen (thinking mainly of a leaderboard trying to measure EQ [1]) MoE models are at a distinct disadvantage to regular models when it comes to complex tasks that aren't software benchmark targets.

    [0] https://arcprize.org/leaderboard

    [1] https://eqbench.com/index.html

Actually the Zorks weren't deterministic, especially Zork II. The Wizard could F you over pretty badly if he appeared at an inopportune time.