Codenames: A Unique Test for AI

Codenames is a popular word-based board game that requires players to work together in teams to identify certain words based on clues given by their teammates. The game incorporates elements of language understanding, strategy, and teamwork. Recently, researchers have proposed using Codenames as a way to test the reasoning abilities of Large Language Models (LLMs). These models are big computer programs that can process and generate human-like text. They’ve been making waves lately in various fields, including gaming.

The interesting twist is that Codenames isn’t just a fun party game; it also creates a unique challenge for AI. It demands not only a good grasp of language but also an ability to think about what someone else might be thinking—sort of the AI equivalent of a mental chess match.

#The Game of Codenames

Codenames is played with two teams, each comprised of a Codemaster and a Guesser. The game starts with a board featuring 25 words. Each Codemaster has a secret map showing which words belong to their team, which are neutral, and which lead to an instant loss. Their job is to give a one-word clue that connects as many of their team’s words as possible without hinting at the opponent’s words or the assassin.

For example, if the words on the board include "apple," "orange," and "banana," the Codemaster might say "fruit" (1) as a clue. The Guesser, knowing they need to find words related to the clue "fruit," can then select "apple" or "banana." If they guess correctly, they can keep going. But if they pick a word that belongs to the opposing team or the dreaded assassin, they lose.

The game is won when all of one team’s words are identified first, or if one team picks the assassin word, leading to their immediate defeat. The social interaction and strategic thinking involved in Codenames makes it an exciting game for players of all ages.

#Why Codenames for Testing AI?

Using Codenames to assess LLMs offers several advantages over more traditional benchmarks. For starters, many existing tests focus on straightforward tasks, like answering questions or translating text. Codenames, however, requires nuanced reasoning—players must think about language, strategy, and teamwork simultaneously. This presents a more complex challenge, meant to mimic real-life communication and cognitive processes.

Moreover, unlike pure strategy Games like Chess, which have been popular for AI testing, Codenames focuses heavily on language. Since LLMs are designed to handle and generate text, it makes perfect sense to see how they perform in a setting where language is key.

#The Challenge for AI

While LLMs have been improving quickly, they still face hurdles when it comes to reasoning and strategic play. In Codenames, getting a clue just right can be tricky. It requires predicting what words will make sense to the Guesser and avoiding clues that might lead them to the opposing team’s words. This aspect involves something called "theory of mind," where players need to understand what others are likely thinking.

So, putting LLMs through their paces in Codenames reveals whether they can not only generate text but also demonstrate an understanding of context and strategy. It’s not just a simple word game; it requires a bit of finesse and cleverness—think of it as a wordy wrestling match!

#The Research Design

In the research setup, several state-of-the-art LLMs were tested using Codenames. This included notable models like GPT-4o, Gemini 1.5, Claude 3.5, and Llama 3.1. Each model was evaluated through different scenarios of the game to see how well they could function as Codemasters or Guessers.

#Game Versions Explored

Two versions of Codenames were tested. The first was a single-team version, where the sole focus was on understanding how well agents could work together to identify their team’s words. The second version introduced competition—two teams pitted against each other—putting the LLMs’ collaborative and strategic skills to the test.

#Single-Team Version

In this version, the Codemaster and Guesser aimed at selecting all their words in the fewest turns possible. If they guessed incorrectly, their score would be impacted, pushing them to make smarter choices. The goal was to see how well the models could generate clues and make guesses reliably.

#Two-Team Version

The two-team version added a competitive twist. Here, Codemasters had to be more strategic, weighing the risks of their clues against the potential for the opposing team to guess incorrectly. It made things much more intense, as success hinged not only on identifying one’s own words but also on outsmarting the opponent.

#The Findings

#Performance of Language Models

The results of the experiments showed that while some LLMs performed better than others, there was no clear winner across all dimensions. Each model had its strengths and weaknesses, leading to diverse play styles.

1. Risk vs. Caution: The analysis revealed a correlation between the riskiness of the Codemasters’ clues and the outcome of the game. Those who played it safe had a higher chance of success in the single-team version. However, in the two-team version, a riskier approach often led to more wins.

2. Emergent Play Styles: The LLMs exhibited a range of behaviors and strategies that were not always optimal. Some models would focus too heavily on one connection, leading their guessers to make poor choices. Sometimes this resulted in players selecting assassin words, leading to a swift defeat.

3. Team Dynamics: When LLMs were paired together, they demonstrated greater adaptability compared to when they teamed up with traditional word-vector agents. Traditional agents struggled when paired with different models. LLMs, however, showed improved performance, indicating a more generalizable ability to adapt.

#Qualitative Observations

While crunching the numbers provided valuable insights, the research also noted peculiar behaviors from the LLMs during gameplay.

1. Outlandish Clues: There were instances of LLMs using fictional clues—like “Hogwarts— which were not found in standard word lists. This demonstrated their unique understanding of context, but it also left traditional models scratching their heads.

2. Playing by the Rules: Occasionally, LLMs provided invalid clues or made incorrect guesses. Sometimes they couldn’t distinguish between valid and invalid clues based on the game rules, causing some hiccups during gameplay. It’s like when someone tries to take an extra slice of pizza but forgets that there are rules about sharing!

3. First Word Problems: Many Codemasters often emphasized a single word connection, neglecting other viable options. Their guessers sometimes ended up selecting unrelated words due to this narrow focus. It’s as if they’d forgotten they were in a team—“Hey, there’s more than one word here!”

#Implications for Future Research

Codenames provides a valuable playground for researchers looking to study and improve LLM capabilities. Here are some promising avenues for future studies:

1. Understanding Competitor Behavior: Future experiments could encourage the models to analyze the opposing team's moves. This would showcase how well the AI can adapt based on the actions of others.

2. Improving Clue-Giving: Researchers could tweak the way LLMs generate clues, perhaps measuring how well they evoke connections based on the situation or cultural references. This could lead to better communication strategies.

3. Word Associations: By testing different word setups, researchers can observe how LLMs relate words. Varying types of word pools could help evaluate how well models can distinguish between closely related words or identify cultural references.

4. Multimodal Experiments: For a more adventurous twist, researchers might explore picture-based versions of Codenames to challenge the LLMs’ visual reasoning, pushing them into the realm of image understanding.

#Conclusion

Overall, using Codenames as a benchmark has proved beneficial for assessing the intricate reasoning and strategic skills of LLMs. The interplay of language understanding and teamwork makes Codenames an ideal arena for testing AI abilities.

As researchers continue to explore this field, it’s not just about improving AI’s performance but also about making these models more relatable in human interactions. Imagine having an AI friend who can give you clever clues while playing Codenames!

And while they might still stumble over a few words and give you some unusual hints, just remember—they’re trying their best in this wordy game of wits! Next time you play Codenames, think of it as a mini-Olympics for language models, where athletes are made of code and words, and the prize is just bragging rights (and maybe a cookie).