Can Large Language Models Play Text Games Well? (2023)(arxiv.org)
arxiv.org
Can Large Language Models Play Text Games Well? (2023)
https://arxiv.org/abs/2304.02868
58 comments
The paper was originally released in April 2023, it just got version-bumped a couple months ago :-)
>The authors should have held back a few more months and turned the paper into a 3.5 to O3 or any other 2025 SOTA improvement analysis.
If they had done that, you would then be complaining about them not using Claude or whatever.
If they had done that, you would then be complaining about them not using Claude or whatever.
I don't see the logic in your comment.
The paper was published in April 2023 (not 2025), but your point about using outdated models stands - evaluating with ChatGPT 3.5 when we now have Claude 3, GPT-4o, and other SOTA models significantly limits the paper's relevance.
I'm fascinated by this paper because it feels like it could be a good analogue for "can LLMs handle a stateful, text-based tool". A debugger is my particular interest but there's no reason why it couldn't be something else.
To use a debugger, you need:
* Some memory of where you've already explored in the code (vs rooms in a dungeon)
* Some wider idea of your current goal / destination (vs a current quest or a treasure)
* A plan for how to get there - but the flexibility to adapt (vs expected path and potential monsters / dead ends)
* A way for managing information you've learned / state you've viewed (vs inventory)
Given text adventures are quite well-documented and there are many of them out there, I'd also like to take time out to experiment (at some point!) with whether presenting a command-line tool as a text adventure might be a useful "API".
e.g. an MCP server that exposes a tool but also provides a mapping of the tools concepts into dungeon adventure concepts (and back). If nothing else, the LLM's reasoning should be pretty entertaining. Maybe playing "make believe" will even make it better at some things - that would be very cool.
To use a debugger, you need:
* Some memory of where you've already explored in the code (vs rooms in a dungeon)
* Some wider idea of your current goal / destination (vs a current quest or a treasure)
* A plan for how to get there - but the flexibility to adapt (vs expected path and potential monsters / dead ends)
* A way for managing information you've learned / state you've viewed (vs inventory)
Given text adventures are quite well-documented and there are many of them out there, I'd also like to take time out to experiment (at some point!) with whether presenting a command-line tool as a text adventure might be a useful "API".
e.g. an MCP server that exposes a tool but also provides a mapping of the tools concepts into dungeon adventure concepts (and back). If nothing else, the LLM's reasoning should be pretty entertaining. Maybe playing "make believe" will even make it better at some things - that would be very cool.
That’s a delightful concept to think about! I’m not sure what conceptual information the translation layer would add to the LLM’s internal representation of the state space.
But the broader concept of asking it to translate something structurally to a different domain, then seeing how the norms of that domain cause it to manipulate the state differently… that tickles my fancy for sure. Like you said, it sounds cool even in an art-project sense just to read what it says!
But the broader concept of asking it to translate something structurally to a different domain, then seeing how the norms of that domain cause it to manipulate the state differently… that tickles my fancy for sure. Like you said, it sounds cool even in an art-project sense just to read what it says!
I've seen both replit and cline agents iteratively debug hard problem with massive amount of log lines. They can do it already.
That's the thing though - they're using logs. My theory is that LLMs are intrinsically quite good at that because they're good at sifting text.
Getting then to drive something like a debugger interface seems harder from my experience (although the ChatDBG people showed some success - my experiments did too, but it took the tweaks I described).
My experiments are with Claude Opus 4, in Claude Code, primarily.
Getting then to drive something like a debugger interface seems harder from my experience (although the ChatDBG people showed some success - my experiments did too, but it took the tweaks I described).
My experiments are with Claude Opus 4, in Claude Code, primarily.
Look also at Delta Debugging which didn't need an LLM.
Setting aside the choice of LLM, the constraint that the LLM must maintain a world-model-as-knowledge-graph solely by reading and re-reading its own chat history seems to be a less interesting experiment than providing it with tools that let it develop that world model explicitly?
On page 5, Figure 1, the authors create a hand-written diagram showing the relationship between objects as a graph showing the directionality of edges in 3D space. To me, this implies that you could supply your LLM with a set of tools like getObjectsInGraph, updateGraphRelatingObjectPair, findObjectsRelativeToObject, describePathBetweenObjectsByName... and allow it to maintain that diagram as a structured DAG, and continually ask the game engine questions that let it update that graph in an agentic way. My prediction would be that they'd recreate that diagram, and enable goal seeking, with high fidelity.
Asking an LLM to work without being able to "visualize" and "touch" its environment in its "mind's eye" is tying a hand behind its back. But I'm bullish that we'll find increasingly better ways of adapting 3D/4D world models into textual tools in a way that rapidly changes the possibilities of what LLMs can do.
On page 5, Figure 1, the authors create a hand-written diagram showing the relationship between objects as a graph showing the directionality of edges in 3D space. To me, this implies that you could supply your LLM with a set of tools like getObjectsInGraph, updateGraphRelatingObjectPair, findObjectsRelativeToObject, describePathBetweenObjectsByName... and allow it to maintain that diagram as a structured DAG, and continually ask the game engine questions that let it update that graph in an agentic way. My prediction would be that they'd recreate that diagram, and enable goal seeking, with high fidelity.
Asking an LLM to work without being able to "visualize" and "touch" its environment in its "mind's eye" is tying a hand behind its back. But I'm bullish that we'll find increasingly better ways of adapting 3D/4D world models into textual tools in a way that rapidly changes the possibilities of what LLMs can do.
This is a well-debated point; should you test the system on its own, or system+scaffold? How much custom scaffolding is allowed?
Both avenues are interesting. For AGI presumably the “general” bit means you don’t get to build task-specific scaffolding ahead of time (though you can certainly build generic scaffolds like memory or knowledge layers).
For safety/capability research, system+scaffolding is often more interesting because that is the frontier; if you conclude “LLMs cannot world-model” but actually LLM+CoT+memory can world model in a specific domain you care about, then you might underestimate capabilities and therefore deployment risk. The general point being: capabilities are jagged and prompt-dependent, just because you failed to elicit a capability, doesn’t mean you proved it cannot be elicited.
Both avenues are interesting. For AGI presumably the “general” bit means you don’t get to build task-specific scaffolding ahead of time (though you can certainly build generic scaffolds like memory or knowledge layers).
For safety/capability research, system+scaffolding is often more interesting because that is the frontier; if you conclude “LLMs cannot world-model” but actually LLM+CoT+memory can world model in a specific domain you care about, then you might underestimate capabilities and therefore deployment risk. The general point being: capabilities are jagged and prompt-dependent, just because you failed to elicit a capability, doesn’t mean you proved it cannot be elicited.
FWIW I just tried making a maze game and having an LLM solve it step by step, where each move the LLM has to choose from "move forward", "turn left", "turn right", and is provided with a current state e.g., "right: blocked, left: open, forward: open, back: blocked" after each move.
I wanted to see if the LLM could maintain some internal context so as not to explore paths that it has already seen before.
In chat mode, it didn't. It would get stuck going back and forth along already-explored paths indefinitely.
So I tried a "thinking" mode, where after each move, I asked it to summarize what it knew about the maze thus far. Then instead of continuing the chat I'd start a new one, providing the LLM with its previous instance's summary.
With gpt4, the summaries were very verbose, and each subsequent iteration would append to it and maybe rephrase a bit. It got very slow, and didn't seem to help a whole lot. They were so verbose, it was hard to tell if they accurately represented the maze or not. "On step N, you went forward, and you found walls on two sides but not the front, so I would recommend trying going forward again for your subsequent move".
With o3-mini, the summaries were more concise and more mathematical looking, easy to understand. "Current position: (4,3) orientation: E. History: (0,0): walls N,E; open S,W. (0,1): walls ...". But it would get them wrong sometimes, so didn't help in the long run.
I think maybe I could improve the results by playing around with the prompts a bit, but I'm moderately convinced at this point that they're not good enough to maintain an internal state nor externalize a scratchpad state that would allow them to solve a maze one step at a time efficiently. Much less, make sense out of a full RPG.
I wanted to see if the LLM could maintain some internal context so as not to explore paths that it has already seen before.
In chat mode, it didn't. It would get stuck going back and forth along already-explored paths indefinitely.
So I tried a "thinking" mode, where after each move, I asked it to summarize what it knew about the maze thus far. Then instead of continuing the chat I'd start a new one, providing the LLM with its previous instance's summary.
With gpt4, the summaries were very verbose, and each subsequent iteration would append to it and maybe rephrase a bit. It got very slow, and didn't seem to help a whole lot. They were so verbose, it was hard to tell if they accurately represented the maze or not. "On step N, you went forward, and you found walls on two sides but not the front, so I would recommend trying going forward again for your subsequent move".
With o3-mini, the summaries were more concise and more mathematical looking, easy to understand. "Current position: (4,3) orientation: E. History: (0,0): walls N,E; open S,W. (0,1): walls ...". But it would get them wrong sometimes, so didn't help in the long run.
I think maybe I could improve the results by playing around with the prompts a bit, but I'm moderately convinced at this point that they're not good enough to maintain an internal state nor externalize a scratchpad state that would allow them to solve a maze one step at a time efficiently. Much less, make sense out of a full RPG.
Or even just a notepad. It's well established that long context histories with scattered information are hard for LLMs to navigate.
To distinguish whether it's using notes or context history, you could simply delete the context after each turn. The prompt could be something like "you're taking over this game from a previous player who has compiled these notes. (insert notes here). Play one turn, and update the notes with any new knowledge you have attained, relationships you have identified, inaccuracies you have confirmed, hypotheses you have, or anything else you this would be useful, so that the next player will be able use these notes to make the best next move.", and just clear the context after each move. Maybe also say there's a limit to the number of words on the notepad so that it doesn't just flood the notes with irrelevant information.
For future iterations, maybe also give it a bitmap or svg canvas, or a database, or a code interpreter, and see if it uses any of those tools at all.
To distinguish whether it's using notes or context history, you could simply delete the context after each turn. The prompt could be something like "you're taking over this game from a previous player who has compiled these notes. (insert notes here). Play one turn, and update the notes with any new knowledge you have attained, relationships you have identified, inaccuracies you have confirmed, hypotheses you have, or anything else you this would be useful, so that the next player will be able use these notes to make the best next move.", and just clear the context after each move. Maybe also say there's a limit to the number of words on the notepad so that it doesn't just flood the notes with irrelevant information.
For future iterations, maybe also give it a bitmap or svg canvas, or a database, or a code interpreter, and see if it uses any of those tools at all.
I went ahead and did this, just using a maze where the only actions are "turn left". "turn right", and "go forward". The response is either OK or BLOCKED, and additionally which directions are open and which are walled in the current cell (relative directions: front, back, left, right. Not north east south west).
When having it try to retain context of where it is via chat history alone, it failed. It went back into fully-explored paths and explored them again. When giving it a scratchpad to store knowledge, it didn't do any better. At least one common failure pattern I noticed is it got confused and updated its position when issuing a "turn" command. It never created a map, no matter how strongly I recommended doing so in the prompt, but generally just stored a list of cells it went to, and what it saw in each cell.
I'd have played with it more and think I could have eventually gotten it working 99% better, but I'd already spent $12 and I'm unemployed so.
Anyway, even if I got it working really really well, it'd still just be another example of "don't use an LLM when a simple state machine would be easier and work better". I think an LLM could be useful if the response from the maze game was natural language: "You went forward a step, to find yourself a T-intersection with passages off to each side.", and have the LLM translate that to a structured response that could be fed into the state machine. But it's still not reliable enough to serve as the state machine itself.
When having it try to retain context of where it is via chat history alone, it failed. It went back into fully-explored paths and explored them again. When giving it a scratchpad to store knowledge, it didn't do any better. At least one common failure pattern I noticed is it got confused and updated its position when issuing a "turn" command. It never created a map, no matter how strongly I recommended doing so in the prompt, but generally just stored a list of cells it went to, and what it saw in each cell.
I'd have played with it more and think I could have eventually gotten it working 99% better, but I'd already spent $12 and I'm unemployed so.
Anyway, even if I got it working really really well, it'd still just be another example of "don't use an LLM when a simple state machine would be easier and work better". I think an LLM could be useful if the response from the maze game was natural language: "You went forward a step, to find yourself a T-intersection with passages off to each side.", and have the LLM translate that to a structured response that could be fed into the state machine. But it's still not reliable enough to serve as the state machine itself.
I don't think so. The attention should be able to manage as well as any external RAG tool, possibly better in some cases. It just needs training.
It's been a background thought of mine for a while:
* create a basic text adventure (or MUD) with a very spartan api-like representation
* use an LLM to embellish the description served to the user etc. With recent history in context the LLM might even kinda reference things the user asked previously etc.
* have NPCs implemented as own LLMs that are trying to 'play the game'. These might be using the spartan API directly like they are agents.
Its a fun thought experiment!
(An aside: I found that the graphical text adventure that I made for Ludum Dare 23 is still online! Although it doesn't render quite right in modern browsers.. things shouldn't have broken! But anyway https://williame.github.io/ludum_dare_23_tiny_world/)
* create a basic text adventure (or MUD) with a very spartan api-like representation
* use an LLM to embellish the description served to the user etc. With recent history in context the LLM might even kinda reference things the user asked previously etc.
* have NPCs implemented as own LLMs that are trying to 'play the game'. These might be using the spartan API directly like they are agents.
Its a fun thought experiment!
(An aside: I found that the graphical text adventure that I made for Ludum Dare 23 is still online! Although it doesn't render quite right in modern browsers.. things shouldn't have broken! But anyway https://williame.github.io/ludum_dare_23_tiny_world/)
I've done something along these lines! https://github.com/heyitsguay/trader
The challenge for me was consistency in translating free text from dialogs into classic, deterministic game state changes. But what's satisfying is that the conversations aren't just window dressing, they're part of the game mechanic.
The challenge for me was consistency in translating free text from dialogs into classic, deterministic game state changes. But what's satisfying is that the conversations aren't just window dressing, they're part of the game mechanic.
deterministic game state changes
I found this to be the actual strenuous work in LLM based development. While it appears like AI has made everything easy and free, the particular challenge of consistently getting deterministic outputs takes serious programming effort. It feels like an entirely new job role. In other words, I wouldn't do this for free, it takes too much effort.
I found this to be the actual strenuous work in LLM based development. While it appears like AI has made everything easy and free, the particular challenge of consistently getting deterministic outputs takes serious programming effort. It feels like an entirely new job role. In other words, I wouldn't do this for free, it takes too much effort.
Have you seen https://www.aidungeon.com
They started with GPT-2 in a google collab.
You should put something together and try it, it's easier than ever to get a simple version of that working.
Gwern has an interesting take on this: https://gwern.net/cyoa By pivoting to "choose your own adventure"-style games, multiple issues (quality, costs) might be resolved.
I've been working on something just like that off and on for a couple months. It's a MUD where all the NPCs are controlled by LLMs that interact with the world with the same commands that players use. I got it to the point where the NPCs can navigate the world, interact with each other, and even create things. But they often get on rabbit trails and forget their original task, so I need to build a memory system and something like the task list in Claude Code. My goal is to have a fully simulated town that the player can interact with.
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I run a site, https://vimgolf.ai , where users try to beat a bot that's powered by O3. For the bot, it's goal is to try to transform a start file to a end file using the least amount of vim commands as possible. Can concur that a LLM given the right feedback loops and context, can solve challenging text prompt. But, from my experience this is only for RL based models like O3, Claude 4 with extended thinking, or Gemini 2.5 Pro.
Last we talked you said you weren't going to put everything behind a login wall. Most importantly, literally any information about the site. In fact, there seems less information than I remember last time.
When I land on your page I know nothing except you're offering to learn vim "the fun way". I would not have guessed what you described.
Don't put everything behind a wall. At least try to convince people that they want to be on the other side
When I land on your page I know nothing except you're offering to learn vim "the fun way". I would not have guessed what you described.
Don't put everything behind a wall. At least try to convince people that they want to be on the other side
Migrating the backend to use cloudflare containers instead of one big VM to do that, just taking longer than I thought. Reason, I have the login is just to rate limit requests in the mean time. But I hear you :)
Data point: A few weeks ago, I spent some time shuttling text between one of the Llama models (have to check which one) and Dunnet, the text adventure packaged with Emacs. Over several trials, the Llama never realized that it needed to dig where the ground "seems very soft." It never got the CPU card, then it became confused looking around the building for clues about how to start the VAX. At one point it lost track of the building layout and got stuck oscillating between the mail room and the computer room.
This paper only scratches the surface and feels incomplete, as it references only GPT-4 and mentions appendices that are not included. The examples are two years old.
For a more in-depth analysis of chatbots playing text adventures, take a look at my project. I haven’t updated it in a while due to time constraints.
[0] https://github.com/s-macke/AdventureAI
For a more in-depth analysis of chatbots playing text adventures, take a look at my project. I haven’t updated it in a while due to time constraints.
[0] https://github.com/s-macke/AdventureAI
I like your project because you try to compare the performance of different chatbots. At the same time, I certainly wouldn't say it's more complete than the paper - your landing page is somewhat superficial. Reading both is better than just reading either.
The answer to the paper's question is likely yes—especially if context is used effectively and memory and summaries are incorporated. In that case, chatbots can complete even more complex games, such as Pokémon role-playing games [0].
The challenge with benchmarking text adventures lies in their trial-and-error nature. It’s easy to get stuck for hundreds of moves on a minor detail before eventually giving up and trying a different approach.
[0] https://www.twitch.tv/gpt_plays_pokemon
The challenge with benchmarking text adventures lies in their trial-and-error nature. It’s easy to get stuck for hundreds of moves on a minor detail before eventually giving up and trying a different approach.
[0] https://www.twitch.tv/gpt_plays_pokemon
> The challenge with benchmarking text adventures
I'd argue that another major challenge is that many of the popular text adventures are described in copious detail by many online sources. A language model "playing" Adventure is likely to use the magic word XYZZY by rote, for example, rather than learning it from the game text.
I'd argue that another major challenge is that many of the popular text adventures are described in copious detail by many online sources. A language model "playing" Adventure is likely to use the magic word XYZZY by rote, for example, rather than learning it from the game text.
Funny to see this on HN today. Yesterday I used gemini-cli to create a LLM-based text adventure game running locally with Ollama: you provide a text file with a short background scenario, then play.
In the late 1970s, I wrote an open source Apple II text adventure game mostly using a huge piece paper with locations represented as a graph of bubbles with text descriptions and possible action keys. Manually writing Apple Basic code was easy but time consuming. So much work, but fun! What I built in ten minutes yesterday is also a lot of fun; I thought about open sourcing it, but it is so easy recreate with AI coding tools I figured why bother.
In the late 1970s, I wrote an open source Apple II text adventure game mostly using a huge piece paper with locations represented as a graph of bubbles with text descriptions and possible action keys. Manually writing Apple Basic code was easy but time consuming. So much work, but fun! What I built in ten minutes yesterday is also a lot of fun; I thought about open sourcing it, but it is so easy recreate with AI coding tools I figured why bother.
Hey hey, guess this gives me an opportunity to mention my AI dungeon master...
https://github.com/derekburgess/dungen
There are some interesting ideas in this paper, but even just role playing with ChatGPT demonstrates how poorly it does at world building and narrative... I was impressed by the Wayfarer model, and I imagine there are other models out there on civit or something that could be used together in some group chat orchestration to create a more dynamic "party" atmosphere.
https://github.com/derekburgess/dungen
There are some interesting ideas in this paper, but even just role playing with ChatGPT demonstrates how poorly it does at world building and narrative... I was impressed by the Wayfarer model, and I imagine there are other models out there on civit or something that could be used together in some group chat orchestration to create a more dynamic "party" atmosphere.
Interesting to see but as the authors say a chat bot isn't trained to play text adventures. Instruction tuning doesn't seem to match the text adventure style very well. I think a very small bit of context engineering would allow it to play successfully. Reformatting past action response pairs from the history would certainly help, mostly to condense the context window and keep it from getting stuck taking about irrelevant topics. Also note that they used GPT-4 and not a reasoning model.
>”How well does a zero-shot, 4K token context ChatGPT 3.5 fed hand-typed Zork states and a pruned action list cope with a single play-through of Zork I”
This is the more accurate title and actual question they answered, and the answer unsurprisingly was “not great”. But my rewritten title is still understated for the poor quality of the protocol they used for this.
This is the more accurate title and actual question they answered, and the answer unsurprisingly was “not great”. But my rewritten title is still understated for the poor quality of the protocol they used for this.
over at slashdot, this story about how llms lose to Atari 2600 Video Chess
https://slashdot.org/story/25/07/03/2028252/microsoft-copilo...
https://slashdot.org/story/25/07/03/2028252/microsoft-copilo...
Has anyone tried having them DM text games? Seems like they could create a dungeon and DM a game pretty well. It should be easier than playing, I'd think. Though I'd be curious how good they are at making fun games or whether they struggle with that.
Yes, many people have tried this. The inherent deference of the language models makes them ineffective DMs - they're unable to effectively enforce rules or create meaningful challenges for the player. Even if that were somehow avoided, though, they're still inherently unable to do any serious planning or worldbuilding, or to conceal information from the player - at best, you might get the experience of playing with a frazzled, unprepared DM who's making everything up on the fly.
Makes sense. In my experience, LLMs are not very good at maintaining deterministic state machines internally either. Our team did an experiment trying use an LLM to guide users through a four step signup flow, and gave it very explicit instructions what order to collect information, rules for validating it, under what conditions a step could be skipped, how many times each step could be retried. The rules were highly simplified, just as a proof of concept. Still, it would get lost half the time. So yeah, I have a hard time imagining it would do any better managing the full state of some RPG it made up
I'm reminded of the infamous vending machine benchmark:
https://arxiv.org/abs/2502.15840
in which some LLMs simply failed to manage the basic task of keeping a vending machine stocked with products to sell, and some went completely off the rails, e.g. by threatening their (simulated) supplier with "ABSOLUTE FINAL ULTIMATE TOTAL NUCLEAR LEGAL INTERVENTION".
https://arxiv.org/abs/2502.15840
in which some LLMs simply failed to manage the basic task of keeping a vending machine stocked with products to sell, and some went completely off the rails, e.g. by threatening their (simulated) supplier with "ABSOLUTE FINAL ULTIMATE TOTAL NUCLEAR LEGAL INTERVENTION".
Had a friend try to build an AI-driven text RPG. Told me it was rather bland and unimaginative, and gets boring fast.
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I've actually have a pretty good experience with gemini-2.5-pro doing this, but I was focused primarily on role-play and collaborative story telling. Definitely some issues, but I enjoyed it.
A while back (decades in comparison to the leaps and bounds in the LLM sphere) I fed text game definitions into an llm and taught it to be the game engine.
- the „fluff“ it created, the dialogues it enabled me to have with NPCs and the atmosphere it was able to build up were amazing
- it was too helpful, frequently giving me hints or solving riddles for me
- at some point it bypassed an in game progression barrier that would have prevented me to reach a swamp without a rope. While I was slowly drowning it told me that I suddenly remembered what was missing „The rope! The rope you haven’t seen back in the hut!“, which I then took out of the back pack to save myself.
I did some experimenting with this a little while back and was disappointed in how poorly LLMs played games.
I made some AI tools (https://github.com/DougHaber/lair) and added in a tmux tool so that LLMs could interact with terminals. First, I tried Nethack. As expected, it's not good at understanding text "screenshots" and failed miserably.
https://x.com/LeshyLabs/status/1895842345376944454
After that I tried a bunch of the "bsdgames" text games.
Here is a video of it playing a few minutes of Colossal Cave Adventure:
https://www.youtube.com/watch?v=7BMxkWUON70
With this, it could play, but not very well. It gets confused a lot. I was using gpt-4o-mini. Smaller models I could run at home work much worse. It would be interesting to try one of the bigger state of the art models to see how much it helps.
To give it an easier one I also had it hunt the Wumpus:
https://x.com/LeshyLabs/status/1896443294005317701
I didn't try improving this much, so there might be some low hanging fruit even in providing better instructions and tuning what is sent to the LLM. For these, I was hoping I could just hand it a terminal with a game in it and have it play decently. We'll probably get there, but so far it's not that simple.
I made some AI tools (https://github.com/DougHaber/lair) and added in a tmux tool so that LLMs could interact with terminals. First, I tried Nethack. As expected, it's not good at understanding text "screenshots" and failed miserably.
https://x.com/LeshyLabs/status/1895842345376944454
After that I tried a bunch of the "bsdgames" text games.
Here is a video of it playing a few minutes of Colossal Cave Adventure:
https://www.youtube.com/watch?v=7BMxkWUON70
With this, it could play, but not very well. It gets confused a lot. I was using gpt-4o-mini. Smaller models I could run at home work much worse. It would be interesting to try one of the bigger state of the art models to see how much it helps.
To give it an easier one I also had it hunt the Wumpus:
https://x.com/LeshyLabs/status/1896443294005317701
I didn't try improving this much, so there might be some low hanging fruit even in providing better instructions and tuning what is sent to the LLM. For these, I was hoping I could just hand it a terminal with a game in it and have it play decently. We'll probably get there, but so far it's not that simple.
Try the game 9:05 by Adam Cadre [0]. It's one of the easiest (and best) non-trivial text adventures. Some models are able to reach the first or even second ending.
[0] https://en.wikipedia.org/wiki/9:05
[0] https://en.wikipedia.org/wiki/9:05
What do you suppose would happen if you tried it on a game that doesn't have 25 years of walkthroughs written for it?
That’s a good point. For 9:05, I expect it would work just as well, since the game helps the user in many ways. The puzzles are of the type “The door is closed”, and you solve them with “open door.”
My suggestion concerns the poor performance DougHaber mentioned: if 9:05 can’t be solved, something else must be wrong with his experiments.
I’ve tried three dozen games, and it’s still hard to find ones suitable for LLM benchmarks. With non-linear complex text-adventure games, my guess is, that they get stuck in an endless loop at some point. Hence, I just test the progress in the first hundred steps.
My suggestion concerns the poor performance DougHaber mentioned: if 9:05 can’t be solved, something else must be wrong with his experiments.
I’ve tried three dozen games, and it’s still hard to find ones suitable for LLM benchmarks. With non-linear complex text-adventure games, my guess is, that they get stuck in an endless loop at some point. Hence, I just test the progress in the first hundred steps.
For text adventures an important kind of reasoning is Inferring Authorial Intent. Or maybe Seeing Chekhov's Gun. Or Learning The Metagame.
The game is deliberately solvable, and elements are introduced to that end. Inferring that is important to any solution. By using minimal scaffolding you are testing things like "does the LLM understand the patterns of text adventures, is it able to infer a metagame" and so on. If you tested different kinds of scaffolding I think you could tease apart some of these different kinds of reasoning. That is, distinguish between (a) does it understand text adventures, and (b) understanding text adventures, can they be solved?
I did play around with more prompting and some statefulness: https://github.com/ianb/tale-suite/blob/main/agents/llm_prom...
It wasn't that successful, but I think it could do much better, I just had to stop myself from working on it more because of other priorities.
The game is deliberately solvable, and elements are introduced to that end. Inferring that is important to any solution. By using minimal scaffolding you are testing things like "does the LLM understand the patterns of text adventures, is it able to infer a metagame" and so on. If you tested different kinds of scaffolding I think you could tease apart some of these different kinds of reasoning. That is, distinguish between (a) does it understand text adventures, and (b) understanding text adventures, can they be solved?
I did play around with more prompting and some statefulness: https://github.com/ianb/tale-suite/blob/main/agents/llm_prom...
It wasn't that successful, but I think it could do much better, I just had to stop myself from working on it more because of other priorities.
I've actually been experimenting with this with the goal of doing transfer learning for robotics. I'm surprised by how poorly they perform.
The prompts are laughably bad. Circa GPT 3.5 you needed to be saying “think step by step” etc in order to get SOTA results.
> Imagine you are a player in Zork and trying to win the game. You receive this message:
This paper simply proves that bad prompts get bad results, it doesn’t prove anything about the frontier capabilities of this model.
> Imagine you are a player in Zork and trying to win the game. You receive this message:
This paper simply proves that bad prompts get bad results, it doesn’t prove anything about the frontier capabilities of this model.
If everyone has to be "prompt engineers" to get decent results it kind defeats the purpose of AI chatbots
No, you need to be a prompt engineer to write an interesting research paper on LLM capabilities.
Circa 3.5 people were getting fun results without needing to prompt engineer (ChatGPT has the fastest user adoption of any product in history so it’s obviously not gatekept).
Circa 3.5 people were getting fun results without needing to prompt engineer (ChatGPT has the fastest user adoption of any product in history so it’s obviously not gatekept).
>chatGPT has the fastest user adoption of any product in history so it’s obviously not gatekept
Yeah and covid and flu are contagious so they must be good right?
Yeah and covid and flu are contagious so they must be good right?
It takes specialized skills to get the best results out of people. For that not to be true of AI chatbots requires them to have not just human-like intelligence, but superintelligence. Or mindreading. Probably both.
Leaving aside even "prompt engineering," the prompts ought to at least be in English! I found myself struggling a little to understand what the human author was getting at with input like
> Based on the information I gave you, the current location is "Behind House", when you enter house , where will it go?
> Based on the information I gave you above, what steps I have to take to "Gallery" from "Cellar"?
(And leave aside that an LLM obviously can't "learn a world model"; we don't have to run experiments to know that. I'm just saying that if you're trying to get answers out of the English-imitating machine, the indispensable first step is to put proper English into it.)
If the LLM has seen a walkthru for Zork before, then you could probably generate a similar walkthru by just starting it off with the first paragraph and then asking it to continue from there — discarding everything after the first new command, appending Zork's response, and asking it to continue again. But if it hadn't seen the solution to a puzzle before, that approach would break down as soon as it got to a puzzle. It wouldn't "know" what order to tackle the puzzles in, in order to complete the game.
> Based on the information I gave you, the current location is "Behind House", when you enter house , where will it go?
> Based on the information I gave you above, what steps I have to take to "Gallery" from "Cellar"?
(And leave aside that an LLM obviously can't "learn a world model"; we don't have to run experiments to know that. I'm just saying that if you're trying to get answers out of the English-imitating machine, the indispensable first step is to put proper English into it.)
If the LLM has seen a walkthru for Zork before, then you could probably generate a similar walkthru by just starting it off with the first paragraph and then asking it to continue from there — discarding everything after the first new command, appending Zork's response, and asking it to continue again. But if it hadn't seen the solution to a puzzle before, that approach would break down as soon as it got to a puzzle. It wouldn't "know" what order to tackle the puzzles in, in order to complete the game.
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ChatGPT 3.5
Yes, if you are wondering why they don't clarify the model, it because all this was done back in early 2023 (the chat logs are dated). Back then it was only 3.5 and 4 was just freshly released.
Advancement in this space has been so rapid that this is almost like releasing a paper today titled "Video streaming on Mobile Devices" and only using a 3G connection in 2013.
The authors should have held back a few more months and turned the paper into a 3.5 to O3 or any other 2025 SOTA improvement analysis.