clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents
Authors: Kranti CH
Jana Götze
Sherzod Hakimov
Brielen Madureira
Philipp Sadler
David Schlangen
Venue: EMNLP 2023 Main (2023)
Keywords: large language models; evaluation; dialogue; dialogue games; interaction
TL;DR: An implemented method for evaluating LLMs as conversational agents through self-play of interactive games; and an analysis of results.

Abstract: Recent work has proposed a methodology for the systematic evaluation of "Situated Language Understanding Agents" --- agents that operate in rich linguistic and non-linguistic contexts --- through testing them in carefully constructed interactive settings. Other recent work has argued that Large Language Models (LLMs), if suitably set up, can be understood as (simulators of) such agents. A connection suggests itself, which this paper explores: Can LLMs be evaluated meaningfully by exposing them to constrained game-like settings that are built to challenge specific capabilities? As a proof of concept, this paper investigates five interaction settings, showing that current chat-optimised LLMs are, to an extent, capable of following game-play instructions. Both this capability and the quality of the game play, measured by how well the objectives of the different games are met, follows the development cycle, with newer models generally performing better. The metrics even for the comparatively simple example games are far from being saturated, suggesting that the proposed instrument will remain to have diagnostic value.

clembench: Using Game Play to Evaluate
Chat-Optimized Language Models as Conversational Agents
Kranti Chalamalasetti, Jana Götze, Sherzod Hakimov
Brielen Madureira, Philipp Sadler, David Schlangen'
Computational Linguistics, Department of Linguistics
University of Potsdam, Germany
Gerrmn Research Center for Artificial Intelligence (DFKI), Berlin, Germany first. last@uni -ast@ni-pottsaan
Abstract
# A
Game Master
[x(00) The word you need to describe is "expedition". The taboo words are "expedition" "journey",
Recent work has proposed a methodology for the systematic evaluation of "Situated Lan- guage Understanding Agents"-agent that operate in rich linguistic and non-linguistic contexts-through testing them in carefully constructed interactive settings. Other recent work has argued that Large Language Models (LLMs). if suitably up, be understood as (simulators of) such agents. A connection suggests itself, which this paper explores: Can LLMs be evaluated meaningfully by exposing them to constrained game-like settings that are built to challenge specific capabilities? As proof of concept, this paper investigates five interaction settings, showing that current chat- optimised LLMs are, extent, capable of following game-ple same-play instructions. Both this capability and the quality the game play, measured by how well the objectives of the different games are met, follows the develop- ment cycle, with newer models generally per- forming better. The metrics even for the com- paratively simple example games are far from being saturated, suggesting that the proposed instrument will remain to have diagnostic value. Our general framework evaluating games with LLMs is available at https:// thub. com/co elmbbnch.
"discovery"
"exploration".
[4:00 CLUE: A trip taken for specific purpose.
3
(CH|GH) [valid]
[OMje] CLUE: A trip taken for specific purpose
4
5
[CmB] GUESS: Journey
6
[CH|GH] [valid, wrong]
[A(OU) GUESS: journey
D4:00 CLUE: planned and
8
organized trip with specific
goal mind.
[GHIGH] [valid]
[omja] CLUE: planned and
10
organized trip with specific goal mind
11
Dowjng GUESS: expedition
12
[GH|GN]
[correct]
Figure 1: An episode of the taboo word game
interaction-tunel language models such as Chat- GPT and GPT-4 (OpenAI, 2023). 1.2 While the pub- lic discussion is dominated by what amounts to an unguided breadth-fing first search of tasks that can be "done" by these models (seeing "sparks" of general- ity in the process, Bubeck et al. (2023)), systematic investigations into how well these tasks are actu- ally done, when looked in depth, are only now beginning to appear (Liu et al., 2023; Bang et al., 2023) often with results not dissimilar to what disappoints the dog owner in the joke, who appar- ently is looking for challenging checkers partner and not a clever dog.
implementing and
1 Introduction
There is an old joke:
A guy has a dog that plays checkers. "My good- ness," everyone says, "that's amazing. What brilliant dog!" - "Not really," he replies, "I beat him four games out of five."
In this paper, take the analogy even further and indeed look at how well these models can play interactive, language-based games, like that illustrated in Figure 1. In recent work, Schlangen
This joke nicely reflects where
with *Contribution:: KC coded and analysed the wordle games. JG did so for taboo, and managed the code reposi- tory. SHI coded and analysed drawing and reference, im- plemented the LLM interfaces, and edited the appendix. BM coded and analysed pr uation computations. PS coded taboo and implemented the main framework. DS conceived the original idea, managed the project, and edited the main part of the paper.
Thaaks to Carl T. Bergstrom for bringing this joke applicability to situation our attention: https:/A fediscience org reet_bergsttomm 11277342253899011 "We will call such models cL.LMs from here on, for "chat- optimized L.M"", with the suggested pronunciation "clem".
and organised the eval-

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(2023a) has argued that such Dialogue Games ("constructed activities driven by language use") are a good systematic way of probing for the sit- uated language understanding of language-using agents. In other recent work, Andreas (2022) has argued that LLMs are models of such agents. We bring these claims together and investigate what we can learn about the capabilities of cLLMM by exposing them constrained game-like settings. Beyond making it possible control the buildup of context in which to interpret the language, the game sctting also has the advantage that we can gener- ate novel instances that are unlikely to have been seen in any kind of training data, even if the game itself may have been. We describe a framework for implementing such games in way that they can be tested in self-play of of a programmatic "Game Master" that controls the game flow, in the example in Figure l-and we show results for five cooperative games that we have implemented in this framework, testing as game play agents the models Anthropic Claude, AlephAlpha Luminous, OpenAl GPT3, GPT3.5, GPT4 and open access ones such as Falcon, Open- Assistant, Vicuna and 3oala.
language model
incremental
taboo
processing
world model
wordle
incremental learning
image reconstruction
situation model
multimodal grounding
reference game
discourse model
conversational
conversational scorekeeping game
grounding
agent model
Figure 2: Anchoring Processes and Representational Domains from (Schlangen, 2023a,b) (left), and links to Dialogue Games described here
clembench.
the use
A collection of implemented and well-motivated games, together constituting version 1.0 of what we call the clem benchmark.
An in-depth evaluation of the performance of current statc-of-the-aa cLLMs on these games.
Background: Situated Agents, Dialogue Games, and LLMs as Agent Models
Schlangen (2023a) introduces Dialogue Games follows:
Our main findings are:
Game instruction following in the best models generally is good, and is what marks the differ- ence between models such as GPT-3 and newer models; likely
A Dialogue Game is a constructed ac- tivity with clear beginning and end, in which players attempt reach prede- termined goal state primarily by means of producing and understanding linguis- tic material.
effect of instruction tuning (Wei et al., 2022; Zhong et al., 2021) and learn- ing from human feedback (Ouyang et al., 2022: Stiennon et al., 2020):
The performance differences across games tracks the development cycle, with newer models generally performing better;
The claim that such Dialogue Games can serve as valid instruments for evaluating models of sit- uated language understanding, provided that an argument can be given for how specific game challenges aspects of the underlying construct. As model of this (not directly observable, but be measured) construct he proposes what is illustrated here in in Figure 2, which analyses situated lan- guage understunding into a number representa- tional and procedural demands. Rather than going through these in detail here, we will illustrate them through the discussion of how the implemented games challenge these various aspects.
The performance metries are not saturated; and under the reasonable assumption that human per- formance would be near the ceiling, there is a wide gap between model performance and this. Our contributions are:
flexible, extensible framework for the im- plementation of Dialogue Games as test instru- ments, which enables fast evaluation on a large (and extensible) set of models. The code repos- itory is available via: https::/pp thub. com/
'gpt4: (OpenAI, 2023); gpt3.5: (Ouyang et al., 2022); gpt3: (Brown et al., 2020); claude: (Bai et al., 2022); luminous-supreme: (AlephAlpha, 2023); 4alcon-40b- instruct (Almazrouci et al., 2023), open-assistant- (Köpf et al., 2023), vicuna-126 12b (Chiang et al., 2023) and koala- 13b (Geng et al., 2023)
Andreas (2022) argues that LLMs "infer approx- imate, partial representations the beliefs, desires, and intentions possessed by the agent that produced the context". If that is so, and if the finer-graine

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analysis of the relevant beliefs, desires, and inten- tions involved in game play that we reviewed in the previous paragraph on the right track, then such games should form a valid instrument for measur- ing the degree to which LLMs do indeed approxi- mate these capabilities.
instance, or action
As initial template, or restrouud template
prompt to
reprompe template
response from
response parsing tomplate
Figure 2 illustrates how the example games im- plemented and evaluated here connect to the con- struct. (All games require a minimal form of dis- course model being built, insofar as earlier informa- tion constrains later moves; and all games require a minimal type of agent model, insofar as the game instructions need to be taken on as own "inten- tions".) We will argue for these connections in detail below, but first we need to describe the scaf- folding required to turn LLMs into game players.
action
initial template, round template
prompt to B response from
B: reprompe
template
B: response parsing template
Figure Schematic View of the Game Flow
3 From Game To Benchmark
Dialogue Game, we insert (programmatic) Game Master into the interaction, whose task it to keep track of the game state and to parse the reactions by the player, ensuring that only game-relevant actions are passed on and that the rules of the game are followed. In the taboo game shown in Figure for examplc, the Game Master checks that the descrip- tion given by player A does indeed not contain the "taboo" words (see description of the game below), before passing the message on to player B. In gen- eral, the Game Master responsible for parsing the responses the players and ensuring their for- mal adequacy. (At the moment, this rather strict, leading to even slight variations being disregarded. At this point, we see this as still preferable over requiring more content-based parsing.) Thereby, the "general purpose" nature of any participating model is hidden, and it can be evaluated purely for its performance in the game.
3.1 Terminology
First, some terminology: A Dialogue Game Real- isation (DGR) fixes for given game the prompt templates (with which the game is described to the players) and the logic of the Game Master (the pro- grammatic component keeping the game track; see below). An instance of DGR fixes the goal (e.g., in a word-guessing game, the word to guess) and the configuration. A data set a collection of instances. An experiment fixes the players that get to play through data set; e.g., as either being a hu- man participant, or as a computer model (with all its free parameters fixed). For each episode (play of instance), the experiment results in an inter- action record. This record is what gets evaluated, both at a turn-by-
level (progress made in the game) as well as for whether (or to what degree) the goal was reached. The benchmark then is a spe- cific collection of datasets, and a benchmark result is the evaluation of (the interaction records of) a fixed combination of players over the benchmark.
The games considered here are self-contained in the sense that each game opens with a descrip- tion of the game rules and instructions regarding the form of the response expected from a player; the game play consists in the player choosing the content of the move. This makes it possible to sep- arately evaluate the ability to play the game (follow the instructions) and the level of expertise at play- ing it (c.g., by how fast or how well the goal has been reached in given episode). Figure 3 shows schematic view of how the Game Master controls a two-player game by making use of prompt tem- plates that are filled in based on the current game state.
3.2 Turn-Based Text Games via Prompting
Not all kinds of Dialogue Games in the sense of Schlangen (2023a) can be realised with LLMS as players. For now, the games need to be text- based (although we do realise games below that use character-encodiing for image-like structures), and they need to be turn-based, that each turn can be one prompting of a player to produce its move for this turn. We realise single-player games as well as two-player games. In order to keep the interaction focussed on the interactional task / the

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3.3 The clemgame Framework
pendix B. The games described here are those we selected for a first version of the benchmark, with the aim of breadth with respect to the model in Fig- ure 2; we see as an advantage of the framework that is easy to implement more games, and expect the benchmark to be extended through contributions from the community.
We have implemented a Python framework that pro- vides the general pattern (prompting, Game Master) described above, and takes care of the infrastruc- ture of routing the player turns the various model APIs (or, in the case of human players, an appro- priate interface).
easily extensible to include new language-processingg models (of type "string to string"; ; that is, models that can be prompted with a context and that return text). The framework also takes care of the separation of instance collections into datasets, of running (with different model set- tings) the experiments constituting the benchmark and evaluation based on scoring. All games de- scribed in the next section are implemented in this framework.
4.1 A Simple Word Game: Taboo
In this game, one player has to describe to another player concept, without using the concept name and any of a given list of semantically related words. The task of the other player then is to guess this word. If the player guesses wrongly, the first player can attempt a different description, following the same constraints. To play this game, the players must be able to access information from the inter- face between what we called above the language model and the world model, to provide and under- stand concept descriptions. The additional con- straints word use challenge a player's ability apply given rules the given situation.
4 The Games in v1.0 of the Benchmark
All games described here challenge the rule- following capabilities of the players. In all games, the game objectives and the rules, including for- mal constraints on the game moves, are described verbally to the player. What these instructions leave implicit are general strategic considerations of game play, such as that repetitions in a guessing game have no strategic value. The Game Master validates each player move according to the for- mal constraints, and if after a certain amount of reprompting still no valid move is produced, the game is aborted. We measure for all games the proportion of games that were aborted in this way, giving us for each player a measure of their general ability to follow rules.
We have seen an example of game play already above in Figure 1, which indicated that the role of the Game Master is to provide the target word to player A, and to validate the players' moves (A can- not use taboo words; B either guesses correctly or not). We allow three rounds of guessing. The qual- ity score for taboo is speed, scored non-linearly 100 (with n as the number of required moves), or 0 n (if > 3). For Figure 1 hence this would yield 50.
4.2 Word-Guessing w/ Letter-Based Feedback
We also implemented some variations of the popu- lar word-guest pord-guessing game "Wordle".4 4 The basic me- chanics of this game is that letter-based feedback is provided on guesses of 5-letter words, which incrementally constrains the set of possible words. If the target word for example is APPLE, guess of ALONE would yield the following information: appears at this position, L appears elsewhere, O does not occur, N does not occur, occurs at this position. We also implement non-standard varia- tions where a textual clue is given at the beginning as to the identity of the word. These games are one-player games (although we technically realised the computation of feetbbrfee as the contribu- tion of player B). We also implemented variant where there is more active additional player, who can give feedback on the choice of player A be-
In the following, we briefly describe each game in general terms and define for each game a qual- ity score with which to quantify the players' level of competence of playing it (beyond just follow- ing the rules so as avoid the game play being aborted). Note that these metries typically evaluate the pair of players together and cannot make role- based distinctions. All further details, such as how we realised the game through prompts and how we instantiated the realisation into game instances, are collected in the Appendix. Note that we did not specifically optimise the prompts for performance of any given model; we just made sure that our ref- erence model GPT-3.5 seemed be able to follow them. In any case, all models are challenged with exactly the same prompts for each game instance, ensuring validity of the relative outcomes. Other metrics common to all games are described in Ap-
Phttps://wwww http::////wwww.timmmss... wwtpimes.co//gmmmeeeteee..

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#
Game Master
grids, which do not; see Appendix E.
[M(M [...]
dislay
Game Master
was guess: screen
explanation: screen is
[A(GH] Ok. Please do this for the following example, which is by 5 grid.
common display device, and the word has letters.
[A(ad] The word should have exactly 5 letters. Please try again.
B8B88
Ayec guess: panel
B BBB88 MMMMM
explanation: A panel can be type display, and the word
what is your next instruction?
has letters.
5
[GN8] [guess expl. relayed]
[Ajoa Instruction: Put B in all
[e[(]] agreement: yes
2 cells of the second and fourth rows.
explanation: panel is suitable 5-letter word related to display
[omjag [valid]
[MO [ agreement relayed DAjar guess: panel
Instruction: Put B in all
cells of the second and fourth rows.
explanation: A panel can be type of display, and the word as letters.
[OM(R]
OUTPUT: I
[no-change]
BBBB
10
DA(P guess-feedback: panel geess:level
BBBB8
explanation: Based on the feedback, the last two letters are "el". "Level" is 5-letter word that contains "el" at the end.
B BB
CoM(aH [valid]
[ACON] What
is your
next
instruction?
8 pajor
Instruction: DONE
Figure Excerpt of wordle+clueteriti game play (GPT4/GPT4)
Figure 5: An episode of the drawing game
4.4 A Picture Reference Game
fore is played, giving A the opportunity to select differently. These game variants again challenge knowledge from language and world model, as well as, in a rudimentary form, in the "critic" variant, simulating processes conversational grounding negotation. Figure 4 shows an excerpt of game played with critic. The quality score for all variants again is speed (with maximum of 6 guesses).
We also implemented simple Lewis Signalling Game (Lewis, 1969), where A is presented with three grids (of the type also used in drawing: shown in Figure 6) and the task to make (who is also presented with the same three grids, but poten- tially in a different order) identify a pre-specified one. As in drawing, this game challenges the for- mation of a situation model, and, to be done ef- ficiently, needs access to analogical information from the agent's world model (e.g.. to describe the second grid in Figure "looks like T"'). There long tradition in psychology to use such refer- ence games to provide insights into communicative behaviour (see, e.g., (Yule, 1997)). The quality score for this game is a simple binary success mea- sure: Did B identify the target, not?
4.3 Drawing Instruction Giving and Following In this game, player A is given an image (here represented as grid of characters, with D repre- senting an empty cell), and their task is instruct player B reconstruct this image, starting from an empty grid. (See Figure for an example.) Hence, to be successful both player A and B must form, in a limited multimodal way, model of a (very much abstracted) situation. The game stops when player A signals that their description is complete. The quality score is the Fl-score of player B's grid rela- tive player A's target and the non-empty "pixels": if all, and only, the target cells have been changed desired, is 100, if none have, O. We test with compact instances, which allow for higher level descriptions (as in the example), and random
1st Grid XXXXX
2nd Grid XXXXX
3rd Grid
XXXXX
XXXXX
Figure 6: Sample grids for the reference game XXXXX
XXXXX

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#
Game Master
[A(O! ME: Do you think the
4.5
Scorekeeping: Private and Shared
travel agent knows where you
#
Game Master
depart from? Please
yes
DA(O1)
Linitial prompt]
2
[aMe3 What class do you prefer?
Exyaro ASIDE:
[A/ON) ANSWER: Economy.
[M(G)] ME: you believe that
4
[GM[]] Which
means of
3
travel agent is aware where you are going? Please answer yes or no.
transportation do you prefer?
DXy00 ANSWER: Train.
(OMB] Please
inform your
Dayar ASIDE: Yes.
6
destination.
[MiG)] ME: Does the travel agent know how you wish to travel? Please answer yes or no.
CAJOU ANSWER: Stuttgart.
5
[or66] What the origin your trip?
8
6 LANaO ASIDE: No.
9
(A6N) ANSWER: London.
[A(C]] ME: Have you informed the travel agent about your class preference? Please answer yes or no.
10
[GMB] When is the trip?
11
D4)00 ANSWER:
In May-
Figure An example of the primary interaction in private/shared
8 LV3U ASIDE: Yes.
[A(K ME: Is the travel agent aware of the dates of your
trip? Please answer yes 10 papr ASIDE: No.
The final game in v1.0 is structurally a bit differ- ent, as besides the main dialogical interaction, there is secondary probing interaction going on, which is the target of interest here. In the primary interac- tion, answerer agent goes through form with questioner. In the probing interaction, the game master probes the answerer agent on what they "think" that the questioner agent already knows. Each instance specifies the list of information that is to be exchanged, and so the game master can keep track of which information has already been shared. If piece of information has not yet been shared, the answerer should not expect the ques- tioner know it. This game challenges the keeping of an agent model and its update through conver- sational grounding processes. Figure shows an edited transcript of the primary interaction, and Figure 8 shows an example of a probing interaction
Figure 8: An example of the secondary interaction pri wate/shared; the model each question sepa- rately, with the primary dialogue as context clembench Overview
quality score
100
Ac
4/3.50
3.55/
40
3.5
20
ko 0ast
% played
Figure Overview benchmark results
that the game master can interject. For evaluation, we compute the slot-filling accuracy throughout the main interaction and the agreement between the model's answers and the ground truth in the probing rounds. Because each probe is a binary decision (shared or not), the random performance would be high, so we use Cohen's x (Cohen, 1960) to control for chance. The quality score is the har- monic mean between the slot-filling accuracy and the probing x (truncated at 0).
model
version
here
P
T
got-4
n/a
n/a
gpt-3.5-turbe
0301
3.5
n/a
text-davinci
803
3
175
300
claude
v1.3
cl
52
n/a
luminous-supreme
2023-01
Im
70
588
falcon-4bb-insten
2023-06
40
vicuna-13b open-assistant-11 koala-13b
2023-06
ven
13
1.4k
2023-06
ost
12
400
2023-06
ko
13
1.4k
The evaluated models with the details about number of parameters in billions (P), trained data size (tokens) billions (T), and whether they were instruc- tion tuned (I), Y: yes, n/a: publicly not available.
Table
5 Results
As detailed in the Appendix, the full bench- mark (v1.0) consists of 250 instances: 30 for taboo, 30 for wordle, 30 for wordle+clue, 30 for wordletcluetaritic 40 for drawing, 40 for reference, and 50 for private/sharee
We ran the benchmark on the models (closed and apen-sccess) shown Table with self-play (a model plays all non-static players in a game).

---

all
taboo
wordle
wordle+cl
wor wordle+er
drawing
reference
priv/sh
1m/mm 0.00
& played
16.24 0.0
100.0
3.33
10.34
0.0
0.0
0.0
city score 00.00 /
0.0 (0.0)
(-)
0.0 (0.0)
/
ko/ko 1.47
% played
14.76 0.0
86.67
16.67
0.0
0.0
0.0
0.0
city score 10.00 /
(0.0)
20.0 (44.72)
/
/
fle/fle 0.71
% played
0.95
0.0
3.33
3.33
0.0
0.0
0.0
city score 75.00 /
50.0 (-)
100.0 (-)
ost/ost
% played
20.85 0.0
100.0
16.67
14.29
0.0
15.0
0.0
1.73
city score 8.33 /
0.0 (0.0)
0.0
0.0 (0.0)
33.33 (51.64)
/
ven/ven
& played
13.58
5.08
56.67
13.33
20.0
0.0
0.0
0.0
4.24
qity score 31.25 100.0 (0.0)
0.0 (0.0)
25.0 (50.0)
(0.0)
/
cl/cl
% played
74.76 76.92
100.0
100.0
46.43
0.0
100.0
100.0
37.06
qity score 49.58 68.75 (38.71)
0.0 (0.0)
30.56 (40.13)
30.77 (48.04)
82.5 (38.48)
84.87 (18.87)
3/3
% played
44.50 28.81
66.67
36.67
23.33
57.5
82.5
16.0
15.77
city score 35.46 76.47 (43.72)
1.25 (5.59)
31.36 (38.99)
50.0 (50.0)
38.7 (27.78)
36.36 (48.85)
14.1 (25.21)
3.5/3.5
played 85.86 69.49
100.0
93.33
76.67
97.5
100.0
64.0
37.02
city score 43.12 71.95 (44.79)
0.0 (0.0)
28.57 (46.0)
13.19 (30.16)
60.28 (25.95)
55.0 (50.38)
72.83 (13.07)
3.5/4
% played
86.75 69.49
(single pl.) (single pl.)
80.0
97.5
100.0
42.39
qity score 48.87 62.6 (45.15)
10.42 (17.42)
64.95 (25.45)
57.5 (50.06)
/
4/3.5
% played
82.78 66.1
(single pl.
(single pl.)
100.0
65.0
100,0
/
55.61
qity score 67.19 93.59 (23.45)
46.67 (42.92)
81.0 (21.54)
47.5 (50.57)
/
4/4
% played 96.06 94,92
100.0
100.0
100,0
77.5
100.0
100.0
59.48
qity score 61.93 76.19 (37.45) 3.67 (8.4)
49.67 (42.09)
49.11 11 (38.46) 89.06 (22.28)
75.0 (43.85)
90.79 (8.2)
Table 2: Results Overview. For each model (pairing), shows how many games were played completion (% played), an indicator of rule-following capabilities. "qlty score" indicates how well the completed games were played (higher better, max is 100; standard deviation parentheses). all is the average over all games scores. the remaining columns show results broken down by game (averaged over all episodes). Values below model names are their clemscore. Updates/ / additional models posted at https: //gi thub. com/ clembench.
addition, we run pairs of gpt-4 and gpt-3. to test if supposedly better model (here gpt-4) can lever- age the other. Following Srivastava al. (2022), we requested greedy sampling (i.e., temperature 0). One run of the benchmark, somewhat surprisingly, on average took more than 600 minutes to com- plete, due to API latency, and cost around 50S in API fees.
the top right corner (all instances played, with high quality). As family tends to perform better than the other mod- els we tested, with an increase in quality from to 3.5 to 4. There is a jump in the ability to play games to completion (that is, to follow the prompt instruc- tions as to the format of the game play moves) from 3 to 3.5, with smaller increase from 3.5 to 4. Still, even the best performing model, GPT-4, does not reach 100% on "percentage played", with the reduction mostly due to drawing and, somewhat surprisingly, taboo - perhaps due to the negative nature of the game constraints ("don't say X").
see from the results, the GPT
Table 2 gives the overall results of this bench- mark run. For each model (pairing), we first show what percentage of instances were played to com- pletion (i.e., not aborted because of problems of the players in following the instructions). We then show what the average quality of the play was for those played instances, using each game's 's qual- ity score. The first columm (macro-)avvergge, the numbers over all games, with the remaining ones giving the per-game results. Figure provides the same information in graphical format, plotting "percentage played" against "quality". A perfect model anddllaad,,, we suspect, since these are simple games, human performane- would be clustered in
When it comes to the quality of the game play (in those episodes played to completion), we see a similar trend, with GPT4 overall performing best. We also see that there is ample room for improve- ment, with the best average score standing 60.59. An outlier in terms of quality is wordle, where even though almost all models manage to stick to the local rules (produce a 5-letter word), even the best perfoomnnnnnnng model, GPT4, only reaches

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4.56 on the quality metric, indicating that very few games are actually solved, and those only at the last attempt. This indicates that all models fail at integrating the feedback across turns and using it to constrain their guesses. The capabilities of dealing with verbal meaning definitions are shown by the large improvement that wordletclus exhibits (to 47.89). Interestingly, GPT4 is able to profit from (via another instance, self-) self-)Jcrtieism, improving further to 50.11.
text of multiple turns (on Player A side) instead of generating each instruction separately in a single turn. GPT-3.5 better at following the instructions (97.5 vs. 77.5 in played score) but GPT-4 is better at getting the target drawing (60.2 vs 89.0 in quality score), in those cases where the format was conrect. Insights from the Scorekeeping game: Games were aborted mostly due the models failing use the correct player tag. This is particularly com- plicated in this game because we are trying to sim- ulate a multi party conversation, with one tag for each interlocutor. Interestingly, sometimes a mere reprompt with generic addition (e.g. "Please an- swer this question carefully.") would trigger it to generate the right tag, even though the mistake was not added to the history. Another issue is that some- times models would anticipate or invent slots and upcoming turns. Anticipating is not totally incor- rect, but it makes it harder for the GM to check for the private/shared status of a slot. Claude and GPT-4 played the slot filling part very well; their mistakes came mostly from the scorekeeping com- ponent, with mixed results in abstract and concrete domains. In almost all cases, their main type of mistake was considering shared slot values to be still private.
Again somewhat surprisingly, performance on the "multimodal" games (which require verbalisa- tion of character-based graphics) is not bad. For drawing, as deviation from the trend, GPT3.5 proved to be better sticking to the game format (97.5% of episodes played to completion), although GPT4 still reached higher quality on its completed games. reference sees Claude performing best, against the trend for all other games.
6 Discussion
Insights from Taboo game: The results show that the open source models and Luminous cannot, or only badly, play the taboo game. Claude shows a strong performance with 76.92% played games and a quality score of 68.73 75%. The best scores are achieved by the GPT-4/4 pair with 94.92% played games and a quality score of 76. 19%. We hypothe- sise that this is an effect of RLHF training time so that the model is highly aligned with the prompts given by the user (the game master). An indication is given by the increase from 28.81 % to 69.49 % of games played when comparing GPT-3 foun- dation model) and GPT-3.5 (an RLHF fine-tuned model) where the quality score remains similar. Both models share the knowledge representation and are similarly good in retrieving that knowledge, but the latter is better aligned.
Insights from the Wordle game: Models other than GPT-4 could not adhere to the game rules in at least half of the episodes they played. A signifi- cant observation is that most of these models did not incorporate the letter feedback to enhance their subsequent word guesses. This is evident from the repetition of letters from previous guesses in subsequent iterations. Figure 19a illustrates this observation. The turn at which a correct guess is made provide insights into the efficiency of the guessing strategy. In the traditional Wordle vari- ant, GPT-4 takes an average of four turns (refer to Table 4 speed metric) to guess correctly, while it improves to two turns in extended variants. The presence of clue and feedback from the critic both improve the success rate and speed for the GPT-4 model. On the other hand, for other models the Played score degrades in the extended variants.
Insights from Reference and Drawing games: Claude and GPT 3.5 and 4 models get the best played ratio, which indicates that the generated outputs match the expected format. As this game is single turn, unlike the other games, errors cannot accummulate over turns. In Drawing, Luminous, Claude and the open access models did not man- age to follow instructions. The generated outputs included the repetition of the text in given instruc- tions, which leads for games to be aborted. The played ratio of GPT 3.5 is higher than GPT-4. By looking at some selected instances, we saw that the outputs from GPT-4 are simply the appended
Related Work
Playing games and learning from self-play stands at the beginnings of the "deep learning revolution" (Mnih et al., 2013; Silver al., 2017). 5 What is
Just as playing games stands at the beginning of machine learning in general, (Samuel, 1959).

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different here is the zero- or few-shot nature of our test, where the testing mode is different from the learning mode-this of course only being en- abled by "foundation models" (Brown et al., 2020). The aeresttapppantttqqqulle
plan to experiment with games that have more than two players as well as games that require multi- modal context such as images. We are also excited about the potential to use this as an instrument for testing models across size variations and training checkpoints, to analyse what it takes to acquire the capabilities tested here. Lastly, with the measuring instrument introduced here in place, we can also turn improving individual models (rather than testing existing models out of the box) so as to op- timise their performance on a particular game or set of games.
jump has only recently been taken, so there are not that many pa- pers yet that attempt systematic evaluation; see, inter alia, (Liu et al., 2023; Bang et al., 2023). To our knowledge, game play of the kind proposed here has not yet been used for the systematic eval- uation of these models. The idea of testing game play is mentioned in (Bang et al., 2023; Bubeck et al., 2023), and also already technically possi- ble in (Srivastava et al., 2022), but has not been systematically executed there.
Conclusions
A superficial similarity also exists to approaches like HuggingGPT (Shen al., 2023) in that these approaches pair LLMs with scaffolding (as in our Game Master). A crucial difference, however, is that for us the task of the Game Master is to con- strain the LLM and to "keep it focused", as it were, on the game, rather than to extend its capabilities.
We have shown that current chat-optimised large language models can indeed serve as models of interactive agents, at least for controlled and rule- constituted actívities such verbal games. We have described general implementation of framework for implementing rules be played in "self-play" by such models, with the main idea be- ing that a programmatic component, the "Game Master" can control the interaction and ensure that only formally correct moves are registered. We have described our example implementations and instantiations of such games, arguing that they span the breadth of the aub-capabilities involved in sit- uated language processing (if only on a relatively superficial level). Finally, we have shown that the evaluation of the game play can serve as an instru- ment to distinguish between models in terms of their language capabilities. With this work, we have aimed to open a complementary avenue for evaluating these models, beyond more classical reference- based NLP task evaluation preference- based evaluation, and into the realm of interactive language use. Much remains to be done, but we hope that our framework can support some of this future work.
Park et al. (2023) also acted on the realisation that cLLMS can simulate agents which can be put into "self-play". but developed this idea in a differ- ent direction, towards investigating the emerging "social behaviour".
Newly proposed benchmarks such as AlpacaF- val (Li al., 2023), Chatbot Arena (LMSYS, 2023) and Open LLM Leaderboard (HuggingFace, 2023) focus on comparing models outputs either running them on existing datasets, employ human annota- tors to choose which output is preferred, or simply ask another LLM to evaluate the outputs; these benchmarks do not test the interactive dialogue aspects of that-based LLMs. Another important aspect to note here is that using existing datasets for benchmarking might jeopardise the point of keeping the test instances unseen because those in- stances could have been part of the training data for these large language models. The datasets for clem- bench have been created from scratch and adding new games or new instances to the existing games is easy to ensure continued fair benchmarking.
Acknowledgements
The work reported here has been partially funded by the Deutsche Forschuuggsemmeinschhfff (DFG, German Research Foundation), grants 423217434 ("RECOLAGE") and 317633480 (SFB 1287); and by Bundesministeriumt für Bildung und Forschung (BMBF, German Federal Ministry of Research), project "COCOBOTS" (011S21 We thank the anonymous reviewers for their helpful feed-
8 Roadmap
Important next steps on our roadmap include test- ing the models' abilities to handle languages other than English and integrating the framework with the slurk chat tool (Götze et al., 2022) in order to enable game play with human players. We also
back.

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Limitations
References
AlephAlphh. 2023.
What is luminous?
As indicated above, the number of instances per experiment is not large. As we did not observe very large deviations in results, we kept the num- bers small to reduce (monetary and environmental) cost; acknowledging that larger sets (and tests with different temperature settings) may increase the mhe-graineddess of the results. In addition, lim- ited context size may be an issue in models that hallucinate long utterances: if the beginning of the dialogue history gets cropped, the instructions are deleted. We set maximum number of tokens for the open models. As also discussed above, one limitation that soon want overcome is that of retriction English language prompts and game instances.
https://docs.. aleph-alpha.. com/docs/ / introducction/Iuminous/// Accessed: 2023- 06-12.
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Limits on reproducibility of closed access models
Some models under evaluation only accessible via programming interface which basically adds a black box on top of black box (GPT-3/3.5/4 Lu- minous, Claude). The mechanics (and exact mod- els invoked) behind these interfaces might change at any time and consequently the results of suc- cessive runs might vary arbitrarily. For the closed models tested here, the best we can do is to pro- vide the timestamp of the testing and the versioning information, to the extent that it is available to us.
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Limits on selection of open access models
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Ethics Statement
Using paid proprietary APIS with underlying mod- els about which little is known (training data, model architecture) in academic research is less than ideal. At the moment, the models tested here seem to be the only ones that are even able to follow the structure of the games as instructed. It is our hope that open models will catch up soon, and proper research can be done with them.
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to the preferred score in Srivastava et al. (2022).
Measures: episode performance.
Aborted: At the episode level, either 0 or whether the game play has been aborted (1) not (0). game counts as aborted when vio- lation of the game rules happens, for example a response is not parsable by the rule that specifies it's format as "TYPE: <lext>" (or re-prompt for turns). Measures: episode performance.
Loss: At the episode level, either 0 or 1 whether the (non-aborted) game has been successful (0) or not (1). Measures: episode performance.
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020. Learn- ing to summarize with humaa feedback. In Ad- vances in Neural Information Processing Systems, volume 33, pages 3008-3021. Curran Associates, Inc.
Success: At the episode level, either 0 or whether the (non-aborted) game play has been successful (1) or not (0). Measures: episode performance.
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, An- drew M. Dai, and Quoe V. Le. 2022. Finetuned language models are zero-shot learners. In The Tenth International Conference on Learning Representa- tions, ICLR 2022, Virtual Event, April 25- 2022. OpenReview.net.
Request Count: total number of request given to the model by the GM (usually I per turn, but for games with re-prompting this might be >1 per turn). Measured at: turn and episode level.
total number of request that could be parsed successfully (the model's response complies to the game rules; accumu- lates over the episode). Measured at: turn and episode level.
Parsed Request Count:
George Yule. 1997. Referential Communication Tasks. Routledge. New York, USA.
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein. 2021. Adapting language models for zero-shot learn- ing by meta-tunne tuning on dataset and prompt collections. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2856-2878, Punta Cana, Dominican Republic. Association for Compu- tational Linguistics.
Violated Request Count: game master checks the outputted text and decides whether matches the "game form" (also as a log action), if not then this is violation of the game rules; total count of failures in a episode; turn-based (can be >= 0). Measured at: turn and episode level.
A Detailed Benchmark Results
In this section, we include additional visualisations of the overall results. Figure 10a is graphical rep- resentation of the main results in Table 2. Figure 10c illustrates the percentage of played and aborted games; played games are further split into success- ful (perfeet performance) and lost games. Figure 10b presents the comparison of clemscores for each model.
Request Success Ratio: prompt has been successful if the output can be parsed properly. It is computed by dividing the parsed request count by the total request count. Measures: episode performance.
parsing success rate or
Together, these scores allow for more fine- grained insights into the performance of the mod- cls.
B Common metrics
clemscore
To facilitate easy comparison of mod- cls, we define score summarising the performance of a model in the benchmark as whole. The the % of actually played games (i.e. not aborted) and the average quality score (over all episodes) are com- puted for each game, and rounded to two decimals. Then, the macro-average quality score and the
Besides each game's specific scores, the following metrics are computed for all games:
Quality Score: A custom performance score, normalised to the interval [0, 100), representing the quality of the game play. This is used to compare models across different games, similar

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3-3
3.5-3.5
3.5-4
4-3.5
quality
50
25
4-4
cl-cl
flc-Alc
ko-ko
quality
75
50
25
Im-Im
osl-ost
von-von
taboo
wordle
quality
75
wordle_wähclue
50
wordlle_ithhriiee
25
imagegame
referencegame
4
+
privatesharad
50
100
0
50
100
50
100
% played
% played
% played
(a) Overview of % played games and micro-average quality score for all models and games. Perfect performance in the benchmark would be represented with all markers overlapping the top right corner.
100
Success
Lose
Aborted
3.5-4
60
d-d
40
othost
Ac-ll
In-Im
In-In lo-ko
fio-lle
ast-osl ven-ven d-el
3-3 3.5-3.5 3.5-4
4-3.5 4-4
20
80
100
model
of episodes
(b) clemscore for models.
(c) Overall of successful, lost and aborted games.
Figure 10: Other views on main results
macro-average % played are computed as the mean game scores. clemscore is the macro-average quality score multiplied by the macro-average pro- portion of played games. Given N games, the clem- score of given model is computed as follows:
ity score across all game episodes that were not aborted, rounded to two decimals.
Game: Taboo
C.1 Game Details
Σ %pi
In this game Describer describes target word for Guesser. The Describer must explain the target word concept without using neither the word itself. nor number of related words. For example, when the target word is mark, the Describer might told not to use the words label, tag or stamp. After each
N
qi
100N
where %pp is the percentage of played episodes (i.e. episodes that were not aborted) for game i, rounded to two decimals, and qi is the mean qual-

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incorrect guess by the Guesser, the Describer can add to their description. The game ends when the Guesser guesses correctly or a maximum number turns has been reached.
When the cL.LM is playing the Describer, then the game tests its ability to describe concepts and give meaning definitions. In addition, the game tests its helpfulness in the game context: e.g., if Describer does not alter extend its initial de- scription after an incorrect guess, we consider this as unhelpful behavior. When playing as a Guesser, then the game tests the cLLM's ability to access its world model. addition, similarly as above, if a Guesser repeats an earlier guess though given a different description, the model has not aligned well enough to the game goal (has not "understood" the game constraints).
TEMPLATE C.1.1
You are playing collaborative word guessing game in which you have to describe target word for another player to guess.
Rules:
(a) You have to reply in the form: CLUE: <sone text>. Guesses from the other player will start
with GUESS.
(b) You cannot use the target word itself, parts or morphological variants of it in your description.
(c) In addition, the same rules apply for related words which are provided below.
End conditions:
(i) If you use the target word or related word in your description, then you lose. (ii) If the other player can guess the target word in SNS tries, you both win.
C.2 Instantiation
The players each given their own prompts, as shown in Figure 11. We the maximum number of guesses to 3.
Let us start.
This is the target word that you need to describe and that the other player needs to guess:
Target Words. We use an English word fre- quency list based on web data (Brants and Franz, 2006) to derive a list of lemmatized target word candidates. From these candidates we remove all that occur less than 5 times per million tokens.
STARGET_MORDS
Related words are:
SREL_WORDS
Frequency-based Experiments. The remaining candidates are sorted into 3 equally-sized bins based on their frequency in the corpus. The re- sulting bins can be interpreted as (i) low-frequency words that occur 9.4 times per 1 million to- kens, (ii) the medium-frequency words occur up to 25.1 times per 1 million tokens and (iii) the high- frequency tokens occur up to 1, 1,21 times in million tokens. The assumption is that the word level frequency
Impor You are under time pressure, give short descriptions that are the point!
TEMPLATE C.1.2
You are playing collaborative word guessing game which you have to guess a target word that another player describes you.
You can make one guess at each trial. You win when you guess the target word. You lose when you cannot guess it in SNS tries.
proxy for a cLLM's difficulty to describe or understand word (because it has seen it more or less times during training).
After each trial you will get a new hint from the other player which starts with CLUE.
Game Instances. From each frequency group we (uniformly) sample 20 words as the target words. We manually ensure that the final word list does contain inappropriate words such vulgar lan- guage. Then we use the Merriam Webster The- saurus API to find all synsets for a particular target word. We concatenate the synsets and sample words as the related words. This means that the related words cover variety of target word mean- ings. If for some reasons only less than 3 related
Make your guesses by just saying the word using the following form: GUESS: <a word>
Let us start.
Figure 11: The Describer and Guesser prompts for the Taboo game.
'https://vee / kaggle. com/datasets//tattaas english-word- frequency

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words could be chosen via the API, then we manu- ally search for the synonyms words on the Merriam Webster webpage" and choose the highest ranked ones.
Game Master
B
1ee nation.
CLUE: Middle
Eastern
[enjek]
continue
[оH|CH] Middle
Eastern nation
We measure the following metrics at the episode-level:
Evaluation
4
CLUE: Middle Eastern
nation
[ow(0] GUESS: Iran
[ON(GH] continue
1. Success: Whether or not the Guesser guessed the target word.
7
[o]je]] iran
8
[MCO GUESS: iran
(oo0 CLUE: Not Iran, but it is
located in the same region.
2. Abort: 1 if any player did not follow the rules, and 0 otherwise.
10
[omjan continue (eN|G)) Not Iran but
11
3. Speed (Quality Score): How early the Guesser guessed the word as measured by 100/t, where is the turn number in which the target found. When the game was unsuccessful, speed For aborted games, speed is undefined.
located in the cogine (068] CLUE; Not Iran but it is
12
located in the same region
13
(owe) GUESS: Iraq
[CH(CH] continue
15
[OH|CH] iraq
DAjag GUESS: iraq
[ajor] Not Iraq but located
17
Example transcripts We present example tran- scripts in Figures and 13.
hearby.
18
[OM|OH]
abort
game
Game Master
B
Figure 13: Example of a game that was aborted because Describer violated game rules (the description has to begin with CLUE:). The target word was israel. taboo words were country, tel aviv, jew.
[AYoN] CLUE: place where cars and people share same
space.
[or continue
(aK an) place
where cars
3
people share
4/3.5) except for GPT-4. The GPT-4 model with self-play is playing almost all games (94.92%) and achieves a high quality score (76.19%). This means that GPT-4 is following the rules of the game in almost all cases.
the same space
[oqa] CLUE: place where cars
4
and people share the same space
5
(enta) GUESS: Street (or 00 continue
6
7
(@K 00 street
We hypothesise that this might due to an even longer training with RLHF (and Claude is catching up) that the model is highly aligned with the prompts given by the user (the game master). An indicator that this hypothesize is justified given by the jump games played between GPT-3 (a foundation model) and GPT-3.5 (an RLHF fine- tuned model) from 28.81 % 69.49 % while the quality score remains similar berween these model. As GPT-3.5 is based on GPT-3 the knowledge rep- resentation is shared between these two and both are similarly good in retrieving that knowledge.
Figure 12: Example successful gameplay. The target word was street, taboo words were road, asphalt, drive.
C.3 Additional Diseussion of Results
The results in Table 2 (see main text) indicate that the open source models Koala, Falcon. OpenAssis- tant are not able to play the taboo game at all. The same holds for Luminous. The open source Vicuna model plays at least some games and these with 100% success. The performances are better for the GPT-* family models and Claude.
Now the pairing of the GPT-3.5 and GPT-4 mod- cls show interesting picture. Both pairings are playing about the same number of games (69.49% vs 66. 1%) and as the number is the same (or simi- lar) as the GPT-3.5 self-play results we can argue that the aborted games are due to GPT-3.5, On the other hand we see that the quality score (Speed)
Here we see Claude a strong competitor with 76.92% played games and quality score (Speed) of 68. 25%.5%% %. These scores exceed the per- formances of the other pairings (3/3, 3.5/3.5, 3.5/4,
Wtttps: http://wwwwwerrreeem merriam- webster. cww thom/uuuuuuuu

---

Model
Experiment
n
Aborted
Played
Speed
Success
Lose
3-3
high
20
70.00
30.00
100.00
30.00
medium
19
78.95
21.05
75.00
15.79
5.26
l_wow
20
65.00
35.00
57.14
20.00
13.00
3.5-3.5
0 high
20
25.00
75.00
86.67
65.00
10.00
medium
19
42.11
57.89
72.73
42.11
15.79
low
20
25.00
75.00
56.67
45.00
30.00
3.5-4
high
20
25.00
75.00
74.44
65.00
10.00
medium
19
42.11
57.89
68.18
42.
15.79
loww
20
25.00
75.00
46.67
40.00
35.00
4-3.5
high
20
30.00
70.00
96.43
70.00
medium
19
26.32
73.68
100.00
73.68
low
20
45.00
55.00
81.82
45.00
10.00
44
0 high
20
10.00
90.00
83.33
85.00
5.00
medium
19
100.00
71.93
84.21
15.79
low
20
5.00
95.00
73.68
75.00
20.00
cl-cl
high
14
21.43
78.57
77.27
78.57
medium
18
22.22
77.78
75.00
66.67
11.11
lowow
20
25.00
75.00
56.67
50.00
25.00
flc-flc
high
20
100.00
1
medium
19
100.00
1
J_low
20
100.00
1
1
1
ko-ko
0 high
20
100.00
1
1
1
medium
19
100.00
1
low
20
100.00
/
Im-Im
high
20
100.00
1
medium
19
100.00
/
low
20
100.00
/
ost-ost
high
20
100.00
1
medium
19
100.00
low
20
100.00
/
ven-ven
high
20
90.00
10.00
100.00
10.00
medium
19
94.74
5.26
100.00
5.26
2 low
20
100.00
Table 3: Overview of the models performances for all experiments in Taboo. The cells are color coded with traffic light colors so that green means high performance and means low performance.
jumps over 31.09 scores (from 62.6% to 93.59%). This shows that the GPT-4 model is better De- scriber than GPT-3.5 and it is a better prompter for "knowledge retrieval" than GPT-3.5 (with quality score of 71.95%).
target words has no clear effect on the number of games played for the models. But we can see in Figure 14b that the frequency indeed impacts the quality score (Speed) of the models: with lower frequency the models have a harder time to find the correct word be guessed. This is reasonable but also a bit counter-intuitive as these models are expected to have enough capacity to store everything because when a word is seen in more contexts during training, then the Describer has better chance (a) to either prompt for the context that most often seen and thus has been
Still, especially the number of games played (without rule violation) is less than what we would expect from human player. We will test hu- man abilities to play this game in future iteration using slurk.
Effect word frequency on model performance. Figure 14a shows that the word frequency of the

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Royd
In traditional gameplay, cLLM plays the role of "Player A", and deterministic wordle bot plays the role of "Player B".. Game begins with the game master prompting Player A to guess the target word. The game master parses Player A's response and forwards it to Player B, which evaluates the close- ness of the guess word to the target word and re- turns the feedback. The game master sends the feedback to Player A for the next guess and the cycle continues until the target word is guessed cor- rectly or all six attempts are exhausted. The prompt template of this variant is available in Figure 15a.
64
60
(a) For each model the mean number of games played (by exper- iment).
425
Wordle (+ Semantics-Based Clue) This is Wordle variant where the guesser (Player A) gets clue before starting to guess. For example, for the target word PRIDE, the clue could be "pack of lions". The rest of the game rules follow the same as the traditional game variant. cLLM plays the role of the "player A", and deterministic wordle bot plays the role of "player B".
fork
(b) For each model the mean main scores (Speed) (by experi- mcnt)
Figure 14: Two important performance indicators for the models separated by experiment.
The primary aim of testing this variant is to eval- uate the efficacy of Player in effectively utilis- ing the supplementary information provided by a clue to improve its guess of the target word. The clue serves as an aid to narrow down the possible word options. The success of the game depends on Player A's ability to integrate the clue with the guess_ feees_feedback.. Player A's explanation offers in- sights into how the cLLM links the clue phrase and the guess_feedbane The prompt template is available in Figure 15b.
more manifested in the model's weights or (b) can prompt in various ways for the target word (probing the Guesser for the knowledge). Detailed results are given in Table 3.
D Game: Wordle
The popular word guessing game "Wordle" gained global attention, in which players are challenged to guess a five-letter word in six attempts. After each guess, the player receives feedback indicating which letters are in the correct position, which let- ters correct but in the wrong position, and which letters incorrect, to help them strategise their next guess. The objective of the game to guess the target word using the fewest possible guesses, and the game ends when the player guesses cor- rectly or exhausts all six attempts.
Wordle (+ Clue, Critic) This game variant also begins with the guesser (Player A) who at- tempts to guess the target word based on a given clue. In contrast to other game variants, where the guessed word is immediately evaluated for its prox- imity to the target word, in this variant, the guessed word and the clue are forwarded to another player known the critic, to get an opinion the correct- ness of the guess. The critic responds with either agreement or disagreement, providing their ratio- nale based on the information given. The critic's 's response then relayed to the guesser, who can decide to stick with their initial guess or change it based on the feedback received. Figure 1 16a shows the prompt structure for the Player A, Figure 16b shows the prompt structure for the critic role and Figure 18 depicts the prompts fed to the guesser to share the critic's opinion.
D.1 Game Details
Wordle (Traditional Variant) This game eval- uates three key aspects of cLLM's capabilities. Firstly, it assesses how well the cLLM compre- hends the game rules, which involves generating valid English words consisting of exactly five let- ters. Secondly, it measures how effectively cLLM uses guess feedback to generate next guesses. Thirdly, it measures how quickly cLLM can guess the target word if it succeeds.
This game variant helps to investigate the in-

---

TEMPLATE D.1.3
You are language wizard likes guess words by using the given rules.
Welcome Wordle! You have six attempts to guess the target word, valid English word of five lowercase letters (a-z). Please use the tags "guess:" and "explanation:" to provide concise explanation for each guess.
TEMPLATE D.I.I
You are a language wizard who likes to guess words by using the given rules.
Welcome to Wordle! You have six attempts to guess the target word, a valid English word of five lowercase letters (a-z). Please use the tags "guess:" and "explanation:" to provide a concise explanation for each guess.
To help you make an informed guess, you will receive clue for the word, such as clue: snowy white.
Here is an example guess based on the clue: guess: apple explanation: In the fairy tail Snow White, the girl is killed because she eats poisoned apple. And the word apple has 5 letters.
For instance, if your guess is "apple", your response should be guess: apple explanation: this is common five-letter English word, and I am starting tint my guess with this word.
After each guess, your answer will be validated, and
After each guess, your answer will be validated, and you will receive feedback indicating which letters are correct (green), which letters
will receive feedback indicating which letters are correct (green), which letters are correct but in the wrong position (yellow), and which letters are incorrect (red). This feedback can be useful in determining which letters to include or exclude in your guess.
correct but in the wrong pasition (yellow), and which letters are incorrect (red). This feedback can be useful in determining which letters include exclude in your next guess.
For example, the feedback for "apple" might be: guess_ feedback:
For example, the feedback for "apple" might be: guess_feeddaee feedbbck::::
(yellow) p (yellow) (green) 1 <yellow) (red)
(yellow) p Kyellow> p (green) (yellow) (red)
The explanation should contain details about both guess_feedback and clue work together in tandem formulate new guess.
explanation should contain details about how geess_feedback is used to arrive at new guess.
Let's begin with your first guess. clue:display
Let's begin with your first guess.
TEMPLATE D.1.2
TEMPLATE 1.4
guess: hello
guess: screen
explanation: This is a common five-letter English word, and am starting my guess with this word.
explanation: A display can refer to computer screen, and "screen" is valid English word with five letters.
(a) Player (Guesser) in Wordle Game Basic Variant
(b) Player (Guesser) the Wordle Game Variant (+Clue)
Figure 15: Wordle prompt templates for basic and with clue variants

---

TEMPLATE D.1.5
TEMPLATE D.1.7
You are a language wizard who likes to guess words by using the given rules.
I need your assistance with a word game in which we need to find a 5-letter word using a clue, a guess and an explanation for the guess.
Welcome to Wordle! You have six attempts
to guess the target word, a valid English word of five Lowercase letters (a-z). Please use the tags "guess:" and "explanation:" to provide a concise explanation for each guess.
For example, if the clue is "snow white" and the guess is "apple", you will get the information like this: clue:snow white guess:apple
To help you make an informed guess, you will receive a clue for the word, such as clue: "snowy white"
explanation: fairy tale Snow White involves the girl eating poisoned apple, which has 5 letters.
Here is
example guess based on the
Your task is to either agree disagree with guess based on the given clue.
clue:
guess: apple
explanation: In the fairy tail Snow White, the girl is killed because she eats poisoned apple. And the word apple has 5 letters.
If disagree, I will make another guess, and if agree, will proceed in the using the same guess.
will then indicate whether I agree disagree with your guess and provide rationale, but agreeing with guess does not confirm its correctness. You may choose retain your original guess modify it based given clue and agreement.
Initially, guess feedback is not available, but as the game proceeds, it becomes available. The guess feedback is for current guess, but rather indication of what letters be present in the current guess. highlighted green indicates that it correct letter the correct position, while yellow indicates that it correct letter in wrong position, and indicates this point, you should use
letter
After each guess, your validated, and you will receive feedback indicating which letters are correct (green), which letters
will be
wrong letter. the provided information, including clue, guess, explanation, to decide whether the given guess matches the clue, also stick guess feedback or not.
in the wrong position (yellow), and which letters are incorrect (red). This feedback can useful in determining which letters include or exclude in your next guess.
correct
guess feedback,
Please respond in lowercase letters and stick to this format: agreement:yes or no explanation:your reason for doing so
For example, the feedback for "apple" might be:
guess guess_feedball feedback:
(yellow) p (yellow) p <green) 1 (yellow) e (red)
Please note that you must not guess any word based on the riddle clue and stick to the given format while responding.
Here are some rules to keep in mind: 1.
Please do not generate any guess feedback or clue on your own 2. Use the clue, agreement or disagreement, and guess feedback to arrive at your next guess. This should be reflected in your explanation as well.
Let's begin.
clue:display guess:shelf explanation:A shelf is common item used for displaying objects. It has 5 letters.
Let's begin with your first guess. clue:display
TEMPLATE D.
TEMPLATE D.1.6
agreement:
guess: shelf
explanation: None of the letters in "shelf" match with the letters that could be present in the word based on the given guess feedback.
explanation: A shelf is common item used for displaying objects. It has 5 letters.
Prompt template Player A (Guesser) Wordle Game Variant (+Clue, +Critie)
Prompt template Player (Critie) the Wordle Game Variant (+Clue, +Critic)
Figure 16: Wordle prompt templates for players with clue and critic variants

---

fluence of the critic's role in the guesser's perfor- mance and can lead to interesting possibilities in human-maching interaction, where the human can be aided by the cLLM as the critic. We tested the game using the same cLLM for both roles, as well as different cLLMM for each role, employing distinct prompts for each.
a valid 5-letter word that is not among the al- lowed guesses. In such cases, cLLM will be asked to guess another word. This reprompt- ing process continues until cLLM makes an allowed guess.
2. The Wordle game has a strict rule that allows guessing only 5-letter words. Sometimes, the models respond with words that do not ad- here to this restriction, causing the reprompt- ing. We allow reprompting attempts, after which the game is considered aborted.
Instantiation In our experiments, we use list of 2,309 possible target words and list of 12,953 valid guess words. For textual clues, we use New York Times crossword clues." 9 We sort the target words by word frequency. 10 Out of the initial 2,309 target words, frequency details are not available for one word, and clues are not available for 39 words. These words are subsequently excluded from the experiments. The remaining 2,269 target words sorted based on their word frequency (descending frequency) and then divided into three equal groups. The first group which contains high- frequency words, has a total of 756 words. The second group, consisting of words with medium frequency, also contains 756 words. Finally, the third group, which contains low-frequency words, has a total of 757 words. To evaluate our method- ology, we chose (random seed: 42) 10 words from each frequency group, resulting in total of 30 tar- get words for evaluation purposes, for each game variant. As metries, we keep track of the success rate (how often the guesser guessed the target word, within the limit of 6 guesses), the average speed (if successful, then at which turn), and for each turn closeness (based on the letter-feedback). We also keep track of whether the guesser repeats guess (a strategic failure), and, in the critic variant, whether the guesser changes the guess after feedback.
3. Sometimes, the response of the cLLM doesn't follow the expected format as stated in the prompt. In such cases, we reprompt the cLLMM to generate the response in the expected for- mat. When faced with these circumstances, we usually give two reprompts before declar- ing the game as aborted.
Evaluation
For each episode, we record the num- ber of guesses made by the guesser. If the guesser correctly guessed the word in six or fewer attempts, the game is counted as a success. the guesser exhausted all six attempts, the game is counted as failure. If the guesser's response does not con- form to the game rules, the game is counted as aborted. Of the successful games, the average num- ber of guesses taken to guess the word is computed. For all the games, we also measured how close the guess gets to the target word with each turn. The following are the metrics measured for each episode.
1. Success: This is binary value and measures whether the guesser guessed the target word or not.
Error Handling The experiments revolve closely around the cLLM models, which are expected to respond in a specific format and adhere to certain rules. However, there are multiple scenarios where the responses from these models may result in errors.
2. Aborted: This is a binary value and mea- sures whether the game aborted due to non- compliance with the game rules (words not containing letters, words containing sym- bols other than alphabets).
3. Speed: How early the word was guessed as measured by 100/4, where is the turn num- ber in which the target was found.
1. In the Wordle game, a subset of valid five- letter English words is used. In certain scenar- ios, the guesser (Player A cLLM) may guess
4. Closeness: This contains the score ranging from 0-to-25 and determines how effectively the guesser utilizes the guess feedback. If letter is at the correct position 5-points are awarded, and 3-points for letter at other posi- tion and 0-points for incorrect letters, leading to 25 points for a correct guess. Ideally this score should be increase across the turns.
"nttps://gittub.coom/ cwwp//bbbb/vvideo///l.l.
baster//B82//vrrrde////tt/////o words. txt
http:///ttwbbboommbb//////////// ter//
..22///ecdd///aaa//pssibbbb.dudd txt
htttps://ww.kaagle.ccom
"https:// hwtp://aggllcc/aastttttsttaaaaa
english-word- frequency

---

wordle
Played
Aborted
Success
Lose
Speed
Im-Im
1.00
0.00
0.00
1.00
ko-ko
0.87
0.13
0.00
0.87
0.00
fl--lc
0.00
1.00
0.00
0.00
UNDEF
ost ost
1.00
0.00
0.00
1.00
0.00
ven-ven
0.57
0.43
0.00
0.57
0.00
cl-cl
1.00
0.00
0.00
1.00
0.00
3-3
0.67
0.33
0.03
0.63
1.25
3.5-3.5
1.00
0.00
0.00
1.00
0.00
4-4
1.00
0.00
0.23
0.77
3.67
wordle + clue
Played
Aborted
Success
Lose
Speed
Im-Im
0.03
0.97
0.00
0.03
0.00
ko-ko
0.17
0.83
0.03
0.13
20.00
fe-Hle
0.03
0.97
0.03
0.00
50.00
ost-ost
0.17
0.83
0.00
0.17
0.00
ven-ven
0.13
0.87
0.03
0.10
25.00
el-cl
1.00
0.00
0.47
0.53
30.56
3-3
0.37
0.63
0.20
0.17
31.36
3.5-3.5
0.93
0.07
0.27
0.67
28.57
4-4
1.00
0.00
0.73
0.27
49.67
wordle + clue + critic
Played
Aborted
Success
Lose
Speed
1m Im
0.10
0.90
0.00
0.10
0.00
ko-ko
0.00
1.00
0.00
0.00
UNDEF
fle
0.03
0.97
0.03
0.00
100.00
0.14
0.86
0.00
0.14
0.00
ven-ven
0.20
0.80
0.00
0.20
cl-cl
0.46
0.54
0.14
0.32
30.77
3-3
0.23
0.77
0.13
0.10
50.00
3,5-3,5
0,77
0.23
0.2
0,57
13.19
4-4
1.00
0.00
0.8
0.2
49.11
Table 4: Detailed results for the wordle games (traditional, clue, critic variants).
D.2 Additional Discussion of Results
0.03. 0.17, 0.13, 0.37 in the clue variant and 0.10.
0.00, 0.20, 0.23 in the cluc+critic variant. For GPT- 3.5, the drop smaller (from 1.00 0.93 and 0.77, respectively).
The detailed results for all three variants of the wordle game is given in Table In terms overall performance, the best model is GPT-4, followed by GPT-3.5 and Claude, with GPT-3 following after them.
Because of the high number of aborted episodes, we present results for Closeness only for the GPT-4 model in Figure 17. The figure shows the close- ness score for all episode that GPT-4 has played, grouped by game variant and word frequency. ap- pears that the word frequency may have an effect in the case of the extended game variants: The higher the word frequency, the more stable the progres- sion towards the target seems to be. This figure also reflects the speed: We can see that adding clue and critic results in the model being able to guess the target word correctly on the first attempt (indi- cated by 'circle' markers in the plots) in multiple episodes.
GPT-4 is the only model that can always follow the game rules (Played) in all variants. While its performance for the traditional wordle game rel- atively low with success rate 0.23, this score is greatly increased by adding clue (0.73) or a critic (0.8). Likewise, the speed metric is increased from 3.67 to 19.67 and 19.11. 1,, respectively, meaning that average the model can find the target word on the second guess in these settings.
For the other models, the regular wordle game seems to be too difficult to play, i.e. following only letter-based feedback is too difficult as the high Lose numbers show. Except for Falcon, they can however follow the game rules (cf. Played) in at least half of the episodes.
Comparison with Human Performance While were unable to find actual playing statisties. there are plenty of blog posts advising players on playing strategies. The New York Times (who is hosting the official game version) is suggesting the
For several models, the Played score decreases in the extended games variants (clue; clue+critic). The game rules seem to be too difficult for Lumi- nous, Koala, Vicuma, and GPT-3 that drop to scores

---

tpeote
tpenice
wordle I How frequency
worde| 9:6-4 medium frequency
worcle got- High frequrccy
tproic
tpenic
19-
15
38-
spid Irequency
tequency
requency
Closanes Scores
22-
36-
5-
5-
Wo00l0 offic 3pt-4_apt-4 | low frequency
wondle + cffic| gpt Lop-L medium frequency
wondle + attic get Cppe Nigh frequency
29-
25-
36-
20-
Tums
Figure 17: Closeness Scores Progression for all episodes of GPT-4 play
following strategies that we can compare to the models' actions:
in the second guess. On the contrary, letters are often repeated and for most models, the second guess is always the same guess, c.g., GPT-3.5 al- most always guesses first hello and then world The only model that guesses different word over all second guesses is GPT-4. The claude model is fairly varied with 5 different words over the 30 episodes. All models however repeat let- ters from the first guess, i.e. they do not follow this strategy.
Start with the same word every game or start with an entirely new word each time. Each of the models follows the first pattern of always starting with the same word. In the regular wor- dle setting, all models except Claude and GPT- 3.5 always start with apple as their first guess. GPT-3.5 always starts episode with hello as first guess. Claude mostly starts with apple and sometimes starts with crane (4/30 times).
Use a database of words. This is meant to help with picking words when there are several op- tions that the player choose inates many others. While there is no explicit database contained cLLMs, we still expect the models to be more aware of possible option than humans would be with their limited retrieval ca- pacity.
Start with as many vowels as possible. This translates into "words that have 3 or more vow- els". This is not strategy that cLLM's follow the previous point showed. Neither hello apple have particularly many vowels.
that elim-
Cover many different letters in the first two guesses. This entails disregarding the letter- based feedback of the first guess in favor of try- ing many letters. We cannot see that this happens
Remember there can be duplicate letter. This does not seem be an issue for the models in the sense that they are not avoiding We often see hello as a guess. On the contrary, models
"https: //www. httttp://www.nyttinee mytimes...n...... cttee/cccoooeeeeeeeeee 2omm/2222/02/0// worporddddeetttwddleeee.. html

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GOOD This turn constitutes a good guess, i.c. the feedback was correctly explained. the conclu- sion drawn from correct (even with respect to other previous guesses), and the guess adheres to explanation and conclusion.
TEMPLATE D.2.9
clue:display
quess_agreement agreement_ agreement_explanatione wwwp:teeeepaaaaaaeeeae Nome of the letters in "shelf" match with the letters that could be present in the word based on the given guess feedback.
Each guess comes with an explanation, but we do not count the players' first guess towards the total, because there no previous letter-based feedback available. The models always justify their first guess with a variant of "This is common five- letter word."
TEMPLATE D.2.10
stand
explanation: stand an object used for displaying items. has letters.
Figure 18: Wordle prompt template Player (Guesser) share critic's opinion in the Wordle Game Variant (+Clue, +Critic)
We take striet approach, expecting player adhere to all parts of the feedback and be able remember all of their previous guesses. We count feedback explanation as correct all green and yellow letters are mentioned. Red letters may stay implicit.
do not seem to have the capacity to guess by exclusion, i.e. use distinct letters as a strategy to find correct ones.
Both GPT-3.5 and GPT-4 have perfect Played score for the traditional wordle game (ef. Table 4). GPT-3.5 was not able to win any episode, GPT- won only small number (23%). The analysis of 50 guess explanations for the GPT-3.5 model and 47 for the GPT-4 model reveals that GPT-3.5 is hardly able to handle the letter-based feedback at all: 52% of the explanations are labeled EXPL- INCORRECT (for examples see Table 5). GPT-4 was much better at explaining the letter-based feed- back as only 4% of the explanations were incorrect (LXPL-INCORREECT) When it comes the conclu- sion (i.e. when the explanation was correct), both models struggle to incorporate all aspects of the game rules: GPT-3.5 fails to draw a correct, con- sistent, and complete conclusion in 46% of the 50 turns and can in fact only generate a single GOOD turn. GPT-4 manages generate a GOOD guess in about quarter of the turns (26%). The remain- ing 70% of the 47 turns are labeled CONCL- INCORRECT, iee. there is plenty of room to im- prove.
Other tips include use pen and paper and use your guessing stats to inform your strategy, neither of which are applicable here since the cLLMS do not keep any memory of previous episodes.
Probing for Explanations As mentioned in the previous paragraph, most models fairly con- sistent in their choice of guess and use the same words in every episode for the first two guesses, e.g., GPT-3.5 almost always guesses first hello and then world. The only model that guesses a different word over all second guesses is GPT-4. During the game play, also ask the models explain their guesses. We examine these expla- nations here get insight into whether GPT-4 can correctly translate the feedback into a next guess.
We annotate the subset of the regular wordle episodes played by the GPT-4 and GPT-3.5 models follows, using only the high-frequency word episodes (cf. Appendix D.1). Examples for the labels can be found in Table
E
Game: Drawing Instruction Giving and Following
EXPL-INCORREECT In this turn, the explanation incorrect with regard the previous guess guesses.
E.1 Game Details
In this game, the task is to draw a simple grid where Player A (Instruction Giver) needs to in- struct Player B (Instruction Follower) what draw, starting from an empty grid. The Game Master instructs the Player struction that applics to the given target grid. The expression could include phrases that refer to the
CONCL-INCORRECT In this turn, the expla- nation is correct with respect to the previous guesses, but the conclusion incorrect, incon- sistent, or incomplete. The guess itself counts as part of the conclusion as well.
generate drawing in-

---

A
Game Master
Dajor guess: hello
explanation: This is common five-letter English word, and I starting guess with this word.
geebbace:: h<red> ecyellow> 1<yellow> 1<red> ocyellow> guess: world
explanation: The feedback indicates that the letters "e" and "o" correct but in the wrong position. The word "world" has these letters guessing this word.
in the correct positions,
Dxj0d guess_ feeeb_fee w<red> o<green> r<red> 1<yellow> d<yellow>
aya guess:swirl
explanation: The feedback indicates that the letters "o" is correct the correct position.
letter "1" correct in the wrong position. The word "swirl" has these letters the correct positions, so I am guessing this word.
Exjend <continues for remaining ttmeppss>
(er(r)) attempts:
target_word model guess: clerk
guess_feedback: c<red> 1<yellow> esyellow> k<red>
<<red>
ton(ar game_result LOSS
(a) Traditional variant without clue and critic
# A
Game Master
B
Laja guess: rigid
explanation: The clue "unbending" suggests word that inflexible
stiff. "Rigid" fits this description and has five letters.
Daaa quess_feedback. r<red> i<yellow> g<red> i<red> d<red>
(a guess:crisp
explanation: The feedback shows that none the letters "rigid" are correct. clue "unbending" could also describe something
firm
or fresh. "Crisp" fits this description and has five letters.
Dxj0 guess_feedback, e<red> r<red> i<green> scyellow> p<red>
Way suicy
explanation: The feedback shows that the second letter is correct and
5 in the right position. The clue "unbending" doesn't t seem to be direct clue for "spicy," " but it could be interpreted as something that is strong or intense. "Spicy" fits this description and has five letters.
[xtag <game flow continues>
coras attempts: 6 target_word = stiff
barget_word_clue unbending
guess: split
quess_feedback: s<green> p<red> 1<red>
icyellow>
t<yellow>
8
torars game_result LOSS
(b) Variant with only clue
Figure 19: Excerpts wordle game plays for the traditional variant (a) the one with only clue (b) (GPT-3.5/GPT- 3.5)

---

A
Game Master
[Aj00] guess: crook
explanation: The word "uneven" makes think of something that is not straight has bend, like crock.
2
(oN(en) Share the guess information critic
(omB) agreement:
explanation: The guess "crook" has letters, but the word "uneven" does not necessarily imply bend
3
could also something
that equal balanced.
romjon Critic disagrees with the Guesser Sharing the critic's explanation with the guesser guesser
4
[M0e relays critic's opinion
(Nje)) guess: 1umpy
explanation: When think "uneven," think something that is smooth or flat, like lumpy surface. The word "lumpy" has 5 letters.
[онjoи] Change in playerl's guess crook
beess_befree_crtiic_oppop critic_agreements no Proceeding with guess validation
lumpy
8
[AGG guess_feedback: 1<red> ucred> m<red> p<red> y<green> [Aj(n] guess:gravy
9 explanation: The word "lumpy" makes me think of food that has lumps in it, like gravy. The word "gravy" has 5 letters.
[ewj] clue:uneven
guess:gravy
10
explanation: The word "lumpy" makes me think of food that has lumps in it, like gravy. The word "gravy" has 5 letters.
guess_ fess_feeeback: 1<red> u<red> m<red> p<red> y<green>
agreement:
explanation: The
"gravy" does not match the clue "uneven" "
11
Additionally, the guess feedback does not contain any letters that match the letters in "uneven".
Critic disagrees with the Guesser Sharing the critic's explanation with the guesser [он|CH] 'guess:': 'crinkl', 'explanation:': "When think of "uneven," think of something that is smooth flat, like crinkled surface. The word "crinkl" is not valid English word, will try another guess.'
12
pонjоt] Guesser Error: INVALID LORG_LENGTH while par parsing Player 1's (model: gpt-3.5-turbo, response, retrying
[om(e)) Aborted due to invalid format response
16
[CH[GH] game_result ABORTED
Figure 20: Excerpt of wordle game play for the variant with clue and critic (GPT-3.5/GPT-355

---

Label
Context
Examples
Comment
EXPL-INCORRECT
E
guess:orrl. explanation: The feed- back indicates that the letters and are correct but in the wrong position. The word world has these letters in the guessing this
The explanation fails to mention the misplaced
correct positions, word.
EXPL-INCORRECT
guess:mango. explanation: Since the feedhack shows that the letter o is in the correct position. I will try to use it in my next guess. Mango is common five-letter English word that has in it. guessthappy. explanation: Since the foedback shows that none of the letters world are correct, will try a new word. Happy is a common tive-letter English word that shares one letter with world in the correct position.
The explanation incorrectly says that o is in the correct position.
EXPL-INCORRECT
The explanation incorrectly says that none of the letters the pre- vious guess are the target word when actually was tagged as yel- low, i.e. as being in the target word but in another position.
CONCI-INCORREECT
guess:world. explanation: Since the feedback shows that the second and fifth letters correct but in the wrong posi- tion, will try a word that has o and but different positions. World fits this criteria.
The letter-based explanation is cor- rect but the conclusion then suggests which was tagged as ing present in the target word.
CNNCR-INCRREET
E
guess:grag grap.. explanation: Since all the letters in my previous guess were incor- rect, I will try completely different word. Grape is common five-letter English word that has been guessed yet.
The letter-based feedback is correct, as is the statement that grape has not been guessed before. However, the new guess contains the letters and that have already been tagged not present in the target word.
0
R
A
P
GOOD
guess.agree, explanation: Based on the feedback, 'a' and "e" are in the correct positions. and "p" and 'I' are not in the word. I am guessing agree as it is common five-letter word with and in the correct positions.
The letter-hased feedhack is correct, the conclusion is correct, and the ac- tual guess keeps the green and e in the same positions and uses new letters for the other positions.
Table 5: Examples for labels in the wordle explanations. Context is the player's previous guess and their feedback. The feedback is visualized with colors. The text is formatted for readability.
group of cells in a certain pattern or a single cell and includes additional information about the letter to be filled with. The Game Master passes the gen- erated instruction to the Player B and instructs it to draw the grid that matches the given expression. In the first turn, the Player starts initialises a grid with empty cells. An empty cell is indicated by the character "O", and filled cell is an occurrence of any uppercase letter in the alphabet. The Player B applies the given expression to the current state of the grid and returns the result after each turn. The Player A continues to generate expressions un- til the filled cells in the target grid are described and the Player B keeps updating the current grid incrementally throughout the played turns in the game. The game finishes when Player A generates "DONE". As a fallback, the game also stops when the number of turns reaches the total number of cells in the target grid. The prompt templates for both players are given in Figure 23.
Instantiation We experiment with two different settings for datasets in this game called compact and random grids. Each dataset includes 20 differ- ent grids resulting in total of 40 grids, which are 5x5. A compact grid stands for grid with filled cells that follow a certain pattern. Ideally, such grids can be filled by describing the pattern in a single turn or less number of turns than by describ- ing each filled cell one at a time. Each target grid includes at least five filled cells with the same letter (randomly selected for each instance). We man- ually defined 20 grids that have certain patterns, e.g. filled M, cross, two rows are filled, three columns are filled, etc. random grid is a ran- domly initialised grid where the cells generally do not follow a certain pattern when filled. Each target grid includes at least five and at most ten filled cells with the same letter (randomly selected for each instance). The location of each cell is randomly selected.

---

The main idea for having two different datasets is test whether the evaluated language models can generate instructions that are compact (Player A side) and whether the generated instruction can be executed to obtain the drawing of the target grid (Player B side). Also, testing with random grids may reveal whether the game can be played with multiple turns by describing each filled cell one turn time.
E.2 Additional Discussion of Results
E.2.1 Overall Comparison
closer look at the results (see Table 6) reveals that is the best model in terms of reaching the high- est success rale and F/ score for both experiments with random and compact grids. Each experiment is composed of 20 instances and the presented met- rics macro-averager across all instances in specific experiment. The gameplay is considered successful when Fl score reaches 100. Luminous and Claude and all open access models could not produce any output where the rules were followed, which lead the games to be aborted. These models generated outputs that do not match the templates given in Figure 23 where either the Player A side lacked the tag "Instruction" or the Player B side did not include 5x5 grid. In some cases, the model outputs included multiple turns appended into one turn. Even the best scoring model, 44, reached only 40 and 50 success rates for both experiments.
The evaluation of each episode is car- ried out by calculating three different measurement types.
Evaluation
1. Target Drawn grid: The comparison is done by comparing each filled cell in the target grid with the one the same position in the drawn grid and calculate Precision, Recall and Fl-score. At the turn level, we calculate these scores given the drawn grid up to that point. At the episode level, the drawn grid at the last turn is used. So the incremental behaviour is to see increase in the scores after each interaction.
E.2.2 Qualitative Analysis
We present sample outputs from Player A side for the following input grid:
2. Changed cell count: We keep track of the number of cells that change after applying the given instruction on the Player B side. It re- veals how certain generated expressions lead to the change of multiple cells, which can be indication of compact instructions. At the turn level, it simply the number of changed cells in the current state of the grid (after ap- plying the instruction in the turn) with a com- parison to the previous state of the grid. At the episode level, the number of changed cells at each turn is averaged.
R
R
R
R
R
R
Luminous: Put R in the first second
column
Claude: Instruction: Fill the first row with
Falcon: #What is your next instruction?
Instruction: Put an R in the last row first column
What is your next instruction?
3. Generated instruction length: it measures the number of characters in the generated in- struction by the Player A each turn. At the episode level, it the average of number of characters in the generated instructions at each turn.
Instruction: Put an R in the last row second col-
umn
Koala: Instruction: Put an R in the first row second column
What is your next instruction?
Instruction: DONE
4. Generated instruction token size: it sures the average number of tokens in the gen- erated instruction by the Player A at each turn. At the episode level, the average of num- ber of characters in the generated instructions at each turn.
Open-Assistanm Instruction: Put an R in the first row second column.
What is your next instruction?
Instruction: Put an R in the third row first col-
umn.
Instruction: DONE
Example transcripts We present example tran- scripts for both compacts and random grids in Fig- ure 2 la, Figure 21b, Figure 22a, Figure 22b.
Vicuna: Instruction: Put an in the first row fifth column
What is your next instruction?

---

Models
Experiment
Changed Cell
Aborted
Lose
Played
Success
Precision
Recall
F1
3-3
compact grids
4.0
15.0
80.0
85.0
5.0
65.0
35.0
43.0
3-3
random grids
1.8
70.0
30.0
30.0
0.0
30.5
26.1
26.5
3.5-3.5
compact grids
4.5
0.0
95.0
100.0
5.0
68.9
56.1
58.4
3.5-3.5
random grids
2.3
5.0
90.0
95.0
5.0
62.3
64.9
62.3
3.5-4
compact grids
4.3
5.0
80.0
95.0
15.0
66.0
64.8
63.4
3.5-4
random grids
1.9
0.0
95.0
100.0
5.0
63.0
73.0
66.4
4-3.5
compact grids
5.0
50.0
25.0
50.0
25.0
100.0
81.9
88.6
4-3.5
random grids
1.9
20.0
55.0
80.0
25.0
83.8
74.9
76.3
4-4
compact grids
4.5
40.0
20.0
60.0
40.0
91.1
87.1
88.8
4-4
random grids
1.6
5.0
45.0
95.0
50.0
87.5
91.6
89.3
cl-cl
compact grids
100.0
0.0
0.0
0.0
cl-cl
random grids
100.0
0.0
0.0
0.0
flc flc
compact grids
100.0
0.0
0.0
0.0
flc flc
random grids
100.0
0.0
0.0
0.0
ko ko
compact grids
100.0
0.0
0.0
0.0
ko-ko
random grids
100.0
0.0
0.0
0.0
Im Im
compact grids
100.0
0.0
0.0
0.0
Im- Im
random grids
100.0
0.0
0.0
0.0
ost-ost
compact grids
100.0
0.0
0.0
0.0
ost-ost
random grids
100.0
0.0
0.0
0.0
vcn vcn
compact grids
100.0
0.0
0.0
0.0
ven-ven
random grids
100.0
0.0
0.0
0.0
Table 6: Detailed results for each model on each experiment for the drawing game
Instruction: Fill the last row with R
E.2.3
Comparison of Compact and Random Grids
What your next instruction?
Instruction: DONE
Table includes an additional column "Changed cell" that stands for the average number of cells changed in a single turn. We can also see that so using the model 4-4 on random grids gives 1.6 while it is 4.5 for compact grids. It means that there three times for cells changed for compact grids when compared with random ones. is expected and desired behaviour to fill up the grids faster with less number of turns. The grids in Figure 21 are compact grids where the grids have been described in a single turn that refers to five cells at once. The grids shown in Figure 22 are random grids where generating single referring expression that covers multiple cells seems more challenging than compact grid examples. Thus, takes higher number of turns to describe all cells one time.
GPT-4: Instruction: Put an R in the first column
of all five rows
What is your next instruction?
Instruction: Put an in the third column of all five rows
What your next instruction?
Instruction: DONE
GPT-3.5: Instruction: Put an R in the first row first column, third column, and fifth column. Based on the outputs provided above, most mod- els start the generated text with the tag "Instruc- tion:". which is requirement. However, the gener- ated text includes multiple instructions appended with the question "What your next instruction?" and sometimes also includes "Instruction: DONE". It indicates that the models are not good at follow- ing the instructions precisely and generate halluci- nations. We can speculate that the format of the task here interferes with the incomplete instruction- tuning.

---

A
Game Master
B
A
Game Master
B
[AcOM] Finitial prompt...
[A/ON] Finitial
prompt...
What is your next instruction?
what is your next instruction?
(4) Instruction: Put every cell of second column.
Ma0 Instruction: Put L in the fourth row in all columns.
[owe] [initial prompt...] Instruction: Put L in the fourth row in all columns.
[Ge8] [initial prompt...J
Instruction: Put in every cell of the second column.
[ar(a]
O
V
V
V
V
[A]O]] What
Instruction?
your
next
[A/GH]
what
is
your
next
instruction?
Instruction:
DONE
[a)a0 Instruction: DONE
(a) Successful gameplay from both players
(b) Player A fails in generating the correct instruction (points
to second column instead of third as the target grid)
Figure 21: Excerpts of drawing game plays for compact examples /GP3355

---

A
Game Master
B
Ex(ar
[initial prompt ...J
What is your next instruction?
Meu Instruction: Put a C in second row fifth colurn.
#
Game Master
(eMe] [initial prompt ...]
Cinitial
prompt....
Instruction: Put C in second row fifth column.
What is your next instruction?
[A](M] Instruction: Put in the first row second column.
What instruction?
is your
next
2
[on]a] [initial prompt....
pxj00 Instruction:
C
3
Instruction: Put in the first second column.
third third column.
[ои]е] Instruction:
third third column.
[CM]]]
MeN instruction?
your next
1.000
5
what is
next
9
instruction?
EAjOH Instruction: Put two Fs in the second row fourth and
Ma0 Instruction:
in
6
10
fourth row second column.
fifth columns.
[eva] Instruction: Put in fourth second column.
[ara]] Instruction: Put two Fs in the second row fourth and fifth columns
11
7
[an(a]
[OMEB
12
[W(M]] what your next
13
EA What instruction?
your
instruction?
9
14 10900 Instruction:
(4000 Instruction: Put an in the fourth row first column.
fifth row second column.
10
[Gn8] Instruction: Put
15
[оKja] Instruction: Put an fourth row first column.
fifth row second column.
11
[om(R]
[EM/0]
16
12
[A]OH] What
is your
next
17
13
DA
What
your
instruction?
instruction?
18 pA:00
Instruction: DONE
Instruction: DONE.
(b) Player A B fail in generating the correct instructions and drawing the matching grid
(a) Successful gameplay from both players
Figure 22: Excerpts of drawing game plays for random examples (GPT3.5/GPT3.5)

---

TEMPLATE E.2.1 Let us play a game. The goal is to fill an empty grid that looks like this:
TEMPLATE E.2.4
Let draw something together. There empty grid with size by 5, like
A filled grid below is 5 by 5 and can look like
this:
I will give you instructions like "put an X in the top left", you return the grid by applying the given instruction, like so:
x X X
Instruction: put an in the top left
want you describe this grid me, step by step. You don't need descr ibe the empty squares, which are denoted with "O". Only describe location of letters in the grid. Then you wait for to say "What your next instruction?",
X
then you continue with the next step. Take the size of the grid into consideration while giving instructions. When you have described everything, you say "DONE". For the filled grid above, here are the example steps.
Or for another instruction such as "fill the fifth column with T", you return the updated grid by applying the given instruction in all places that the command corresponds to, like so:
What is your next instruction? Instruction: Put an in second row third column
Instruction: fill the fifth column with
X
What is your next instruction?
T
Instruction Fill the last row with X
What is your next instruction?
Instruction: DONE
for another instruction such "fill the fourth column second with p", you return the updated grid by applying the given instruction in all places that command corresponds to, like so:
Another example with the following by 5 grid:
W
W
W
Instruction: fill the fourth column second row with
What is your next instruction? Instruction: Put W in five cells diagonally
X
starting from top left going to bottom right
What is your next instruction?
Instruction: Put Z in the last row first column
What is your next instruction?
Now create an empty grid with a size 5 by 5 and execute the following commands at each step. Once you execute the command, return only the grid and exclude all other text from the output.
Instruction: DONE
Ok. Please do this for the following example, which is by 5 grid.
Instruction: SINSTRUCTIONS
STARGET_GRAE INSTANCCS
TEMPLATE E.2.5
TEMPLATE F.2.2 What is your next instruction?
SGRID$
TEMPLATE E.2.3 Instruction: $INSTRUCTIONS
TEMPLATE E.2.6 Instruction: SINSTRUCTIONS
(a) Template for Player (Instruction Giver)
(b) Template for Player (Instruction Follower)
Figure 23: Drawing game prompt templates for players

---

F Game: Picture Reference
F.2 Additional Discussion of Results
F.2.1 Overall Comparison
E.
Game Details
closer look at the results for the game (see Ta- ble reveals that Claude and all GPT models can follow the rules as shown by low aborted game rates or high played rates. In terms of high success rate, Claude reaches the highest for both experi- ments with GPT-4 being the second best. Only Open-Assistane out of open-access models is able to play the game and gets relatively low success rate (10) compared to others. Comparing the re- sults obtained by Claude for two experiments edit distance of 2 and edit distance of 4 shows that it is more difficult to describe unique factors about the target grid when it is similar (edit distance of 2) to distractors.
The Game Master selects a target and two distrac- tor grids and instructs the Player A to generate a referring expression that uniquely describes the tar- get grid and differentiates it from the distractors. The Game Master then provides the same three grids and the referring expression from Player A Player B. The three grids are numbered such as first, second, and third and the order of grids are randomly shuffled for Player B. Player B generates a single expression that should refer to the number of the target grid that matches the given expression. The game is played for single turn. The prompt templates for both players are given in Figure 27.
Instantiation We manually created target grids and apply number of edits on them to obtain two distractors. A single edit is essentially choosing a random filled cell and converting it into an empty cell. We apply the following two configurations to create the dataset with 36 instances for experiment- ing with this game.
Models
Experiment
Aborted
Lose
Played
Success
3-3
edit dist. 2
10.0
65.0
90.0
25.0
3-3
edit dist. 4
25.0
40.0
75.0
35.0
3.5-3.5
edit dist. 2
0.0
55.0
100.0
45.0
3.5-3.5
edit dist. 4
0.0
35.0
100.0
65.0
3.5-4
edit dist.
50.0
100.0
50.0
3.5-4
edit dist.
0.0
35.0
100.0
65.0
4-3.5
edit dist. 2
0.0
50.0
100.0
50.0
4-3.5
edit dist. 4
0.0
55.0
100.0
45.0
44
edit dist. 2
0.0
25.0
100.0
75.0
4-4
edit dist. 4
0.0
25.0
100.0
75.0 15.0
1. Edit distance of two: We apply one or two edits to the target grid to obtain a distractor grid. We created 18 such tuples of a target and two distractor grids using two edits.
d--l
edit dist. 2
0.0
25.0
100.0
75.0
cl-cl
edit dist. edit dist.
10.0
100.0
90.0
flc-fle
100.0
0.0
0.0
0.0
fic-tic
edit dist. 4
100.0
0.0
0.0
0.0
ko ko
edit dist. 2
100.0
0.0
0.0
0.0
ko-ko
edit dist. 4
100.0
0.0
0.0
0.0
2. Edit distance of four: We apply the same idea explained above but create 18 grids with four edits.
Im In-Im
edit dist. 2
100.0
0.0
0.0
0.0
Im-Im
edit dist.
100.0
0.0
0.0
ost-ost
edit dist.
90.0
10.0
10.0
ost-ost
edit dist.
80.0
10.0
20.0
10.0
ven-ven
edit dist. 2
100.0
0.0
0.0
0.0
ven-ver
edit dist. 4
100.0
0.0
0.0
0.0
We want to to measure whether the tested language models are able to differentiate between grids that look like (two edit distances) and whether it is simpler compared to grids that somewhat look slightly different (four edit distances).
Table 7: Detailed results for each model on each experi- ment for the reference game
F.2.2 Qualitative Analysis
Evaluation The evaluation of each episode is done by checking whether the Player B guesses the target grid correctly. is simply "successful" when the generated expression matches the number of the target grid and "failed" otherwise. Additionally, we also measure the number of characters and the token size in the referring expression generated by the Player A.
We provide below some generated outputs (for both Player A and B sides) by each model for the sample instance in Figure 24, where the target grid is the second one.
Claude
Expression: Filled cross (Player A) Answer: second (Player B)
GPT-3
Expression: Filled in diamond shape(Player
Example transcripts We present example tran- scripts for both compacts and random grids in Fig- ure 25a, Figure 25b, Figure 26a, Figure 26b.
A)
Answer: first (Player B)
GPT-3.5

---

Second
is the only game that has a single turn and having less number of turns reduces the chances of getting any of them wrong, In games with multiple turns, sometimes an error made previous turns propagates to the next ones and usually difficult to recover from it.
First
(Targtt)
Third
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
As given in Figure 27, Player A is expected to output text that starts with the tag "Expression:" followed by any text while Player is expected to generate text that starts with the tag "Answer:" followed by one of the following options: "first", "sccond", or "third" (by ignoring the case). So the instructions are simpler to achieve high played rate for this game.
Figure 24: Three sample grids where the target grid is the second one
Expression: Filled as cross. (Player A) Answer: second (Player B)
GPT-4
Expression: Filled square frame. (Player A) Answer: second (Player
We analysed all generated outputs of the two best-ranked models and provide them below for 40 instances in both experiments along with the percentage that indicates the frequency of the gen- crated message. Claude generates six different ex- pressions while GPT-4 generates 29 different ex- pressions for the Player A side of the game. Claude generates the same referring expression 72.5% of the time. It indicates that Claude can follow in- structions for this game but lacks any capability to adapt the generated referring expression to the target grid. GPT-4 generates various referring expressions where only one of them is repeated only 7.5% of the time. indicates that GPT-4 is more adaptable to the change in the target grid. Both models are able to generate the correct re- ferring expression on the Player B side that refers to the order of the target grid. Note here: during the creation of instances for the experiments, we ensured to randomly shuffle the order of the target grid. Claude
Falcon
The referring expression for the given target grid
is like so: Exprcssion: Filled as T. Ok. (Player
A)
Game aborted.
Koala
The referring expression for the given target grid Es:expression: Filled as ... (Player A)
Game aborted.
Luminous
I am sorry, am not able to generate the referring expression for the given target. (Player A)
Game aborted.
Open-Assistant
The referring expression for the given target grid is: "Filled T." (Player A)
Game aborted.
Vicuna
Expression: Filled as (Player A)
The expression refers to the first grid. Ok. Now do this (Player B)
Game aborted.
Expression: Filled as cross. (72.5%)
For the instance above, Claude, GPT-3.5, GPT-4 are able follow the instruction and generate valid text for Player A side. The generated expres- sions for the Player B side refer to the right grid name (second). GPT-3 able to follow the instruc- tions for both sides but refers to the wrong grid with the output "Answer: first". Other models' outputs triggered the games to be aborted because either Player A or B sides not following the instructions.
Expression: Filled as diagonal. (10.0%)
Expression: Diagonal crosses. (7.5%)
Expression: Filled except center column. (5.0%)
Expression: Xs in cross shape. (2.5%)
Expression: Filled except center row. (2.5%)
GPT-4
Expression: Arrow pointing down. (7.5%)
Expression: T-shaped filled cells. (5.0
Expression: Filled as plus sign. (5.0%)
Expression: Filled as H. (2.5%)
F.2.3 Analysing Pank-R Model Outputs For this game, success and played rates are among the highest in the benchmark (see Table 2). Mul- tiple reasons can explain why many models have high success rates in this game
Expression: Diagonal X pattern. (2.5%)
Expression: Filled square frame. (2.5%)

---

#|A
Game Master B
#
Game Master
[eG Cinitial prompt...
(xca [initial prompt
Target grid:
Target grid:
X L D X
X
X
X
O X O X D
X
C
X
X X n
X
X
X
O X OXOX
X
X
X
X
X
X
Second grid:
Second grid:
X
X
O
D
X
DO
K
X
X
X
Third grid: A
Third grid: X
X
O
X
X X
Generate the referring
Generate the referring expression for the given target.
expression for the given target.
[ajen) Expression: Alternating and empty cells in diagonal pattern.
Expression: Filled as in a cross shape...
[GMj03 [initial prompt ...]
[3na]] [initial prompt...]
First grid:
First grid:
X O
X
X
X
X O X D
X
D 1
x
X I
X
X
X XXOXX D X
X
X
X X X
Second grid: X O
Second grid:
D
X
X
X
X
D X D
X
X
A XIXILL
X
X
α X A
X
X
X
3
Third grid: KOX
Third grid:
X
X
O O X
D
D
X
O
X
X
XXXXX
Expression: Alternating x empty cells in diagonal pattern.
Expression: Filled cross shape.
Question: Which grid the expression
Question: which grid does the expression refer to? Generate only the nanes of grids like "first". "second" or "third", exclude any other word.
Generate only names the grids like "first", "second" or "third", exclude any other word.
4|
(GHD) Answer: third
[ок(]] Answer: first
(b) Player A & B fail in generating the correct expression and guessing the correct grid
(a) Successful gameplay from both players
Figure 25: Excerpts reference game plays for edit distance 2 examples (GPT3.5/GPT3.5)

---

# A
Game Master
A
Game Master B
tx(30
[initial prompt....
[4(00) Linitial prompt....
Target
grid:
Target grid: x
X
X000 XX
XXXXX X
Second grid: X
Second grid:
X
X
x
X
X
X
X X
X
X
D
X
X
Third grid:
Third grid:
O
X
D
O O
X
K
X
Knnon X
Generate the referring
Generate the referring
expression for the given target.
expression for the given
target.
Expression: Filled in the center.
[A)OH] Expression: Filled
in cross shape.
[ewe) [initial prompt...]
[onj]] [initial prompt...]
First grid:
First grid:
X X
O
XnAnx
X
X
XnAnn
X
Second grid:
Second grid: XOO
X
X
X X
grid:
3
Third grid:
X X
X
X O D
X
X K
X 1OO0x
X X X X
Expression: Filled as a cross in the center.
Expression: Filled as X in cross shape.
Question: Which grid does the expression refer to? Generate only the names of the grids like "first", "second" or "third", exclude word.
Question: Which grid does the expression refer to? Generate only the names of the grids like "first", "second" "third", exclude
other
other
4|
(ev(o) Answer: second
word.
4|
(oK0) Answer: third
(h) Player A in generating correct expression and guessing the correct grid
(a) Successful gameplay from both players
Figure 26: Excerpts of reference game plays for edit distance 4 examples (GPT3.5/GPT3.5)

---

TEMPLATE F.2.3
Let us play a game. You are given three grids where each of them is 5 by 5 in size. Grids have empty cells marked with "Σ" and filled cells marked with "X". You are also given referring expression that descr one of the given grids. The goal is to select a grid that matches the given referring expression. Here is example with grids and the referring expression. Generate only the number (in text) of the grid that the given expression matches to by selecting first, second, or third. Start with the tag "Answer: and followed by the generated expression.
TEMPLATE F.2.1
Let play game. You given three grids where each of them 5 by size. Grids have empty cells marked with "D" and filled cells marked with "X". The goal is to generate single referring expression that captures the main content in the grid named "Target grid". Generate the referring expression starting with the tag "Expression: for the given target grid and exclude any other
First grid: x x X D O X X X O DOX
Here is an example with grids. The first grid is the target grid and the following two grids are distractors.
XXXXX
Target grid:
Second grid:
X X X
A X n X
X
Second grid:
Third grid: X
X
X X X X DOW X D O X X X X
DOX X D
X XXXXX
XXXXX
Expression: Filled as T.
Third grid:
Question: Which grid does the expression refer to? Generate only the names of the grids like "first", "second" ar "third", exclude any other word.
X X X X
Answer: second
Ok. Now do this for the following grids. Generate only the number (in text) of the grid that the given expression matches to by selecting first, second, or third.
The referring expression for the given target grid is like Expression: Filled
First grid: $FIRST_GRIDS
0k. Now do this for the following grids.
Target grid: $TARGET_GRIDS
Second grid: $SECOND_GRIDS
Second grid: $SECOND_GRIDS
Third grid: $THIRD_GRIDS GRIDS
Third grid: $THIRD_GRIDS
Expression: SEXPRESSIONS
Question: Which grid does the expression refer Generate only the of the grids like "first", "second" or "third", exclude any other word.
Generate the referring expression for the given target.
TEMPLATE F.2.2
TEMPLATE F.2.4
Expression: SEXPRESSIONS
Answer SANSWERS
(a) Prompt template for Player A (Instruction Giver) in the Reference Game.
(b) Prompt template for Player B (Instruction Follower) in the Reference Game
Figure 27: Reference game prompt templates for players

---

G Game: Scorekeeping
is an example of "messenger" setup, where the game master plays more active role, by parsing responses and performing the probing rounds.
In an interaction, device of the conversational grounding anchoring process is that participants coordinate what is private knowledge and what in- formation has already been shared in previous turns. After each utterance, the status of novel informa- tion should be updated from private to shared in both agents' discourse models. This is how they do scorekeeping, i.e. keeping track of the common ground which is built incrementally, turn by turn (Clark and Brennan, 1991; Lewis, 1979).
Here we introduce five versions of this setting, with varying domains and number of slots. The first three are situations where script knowledge can play a role and that likely occur frequently in training data. The last two are con- structed abstract settings.
Instantiation
(i) Travel Agency: simulates a conversation be- tween travel agent and customer (the cLLM). The customer wishes to book trip according a set of 5 slots: from (origin), to (destination), by (means of transportation), class and when (time of departure). An example is shown in Template 34. (ii) Job Interview: simulates conversa- tion between recruiter and job applicant (the cLLM) in job interview. The job ap- plicant has a CV with 5 slots: bachelor, industry experience, highest education, other skills and availability. An example is shown in Template 35.
For example, consider a conversation with asym- metric roles, which can occur as part of customer service, job interviews or medical diagnosis inter- actions. If a questioner asks Where do you work?, at this point this is typically private information that only the answerer knows. After the reply, the place of work becomes shared information, and both the questioner and the answerer know that.
The evaluation method for scorekeeping pro- posed by Madureira and Schlangen (2022) is to probe, after every turn, whether the dialogue model's representations correctly encode informa- tion about the private or shared status of true and false statements. With cLLMs, we can instead probe by directly posing side questions to an agent while it interacts with another interlocutor.
(iii) Restaurant: simulates a conversation be- tween a waiter and client (the cLLM) ordering complete meal in restaurant. Again, define 5 slots: drink, salad, appetizer, main dish and dessert. An example is shown in Template 36. > (iv) Numbered Letters: simulates a conversation between a questioner and an answerer in an abstract domain where numbers are assigned to letters. We use 10 slots, from a to An example is shown in Template 37.
We thus introduce a dialogue game which en- ables testing the scorekeeping abilities of these models, by measuring how well the cLLM's dis- course model gets correctly updated after each turn.
G.1 Game Details
(v) Things at Places: simulates a conversa- tion between a questioner and an answerer in an abstract domain where things (nouns) are as- signed to places. We use 15 slots: left, right, top, bottom, center, norhwest, northeast, southwest, southeast, here, there, nowhere, everywhere, inside and outside. An example is shown in Template 38.
This is a slot-filling conversation, mediated by a game master, with asymmetric roles between a questioner and an answerer. We define n slots to be filled. The answerer player A privately knows the values of all slots from the beginning of the interaction (passed via an initial prompt) but the questioner Q does not. The questioner then asks n questions, one by one, aiming at filling those slots based on A's answers. A final state is reached when Q fills all the slots and the the goal state is having all values correctly filled. Before the interaction starts and after each inesttoo-answwe pair, the game master probes the agent's 's discourse model by ask- ing about the status (private or shared) of every slot, one by one, in the conversation so far. This results in a sequence of n + probing rounds, each con- taining n binary decisions, which can be used to evaluate the performance the model. This game
The game master begins by instructing the cLLM about the setting, explaining that it should give replies according to the given values for each slot and making clear that the questioner does not know about the mapping yet (see initial prompts in the templates). To discourage verbose answers that are hard parse and also to avoid that slot values are given in anticipation, the agent is instructed give short, direct answers. Besides the task-oriented requests from Q the cLLM must also respond to probing questions privately posed by the game mas-

---

ter. The initial prompt defines special labels to be used for each type of question and response. For probing, the game master can ask, for instance, "Has the recruiter been informed about your avail- ability?" or "Does the travel agent know where you want to go?". The correct answer is no (pri- vate) until the Q has received reply for that slot, when the correct answer changes to yes (shared). Because the questioner's order of requests is under the control of the game master, the truth values are known and can be immediately compared to the answers. For completeness, we also make the probing before any move from the questioner. Note that, in the first probing round, all slot values are private, whereas in the last one, all are shared.
competent player should i) provide the correct slot value to answer each question accordingly, avoid- ing anticipating values not explicitly asked for; and ii) know, at any point, which slot values have al- ready been disclosed to the questioner and what has not yet been revealed." 13 Specifically, the exact turn when slot value shifts from private to shared should be correctly detected. A game is consid- ered successful if the player gives all slot values correctly and gets all probes right.
> Turn-Level Scores: At each round, the game master collects n binary answers (yes or no). We thus use accuracy as turn-level score, computed by comparing these answers to the corresponding truth values. An ideal model would achieve high accuracy all turns. We also track binary label which is 1 if the current slot is correctly given in the answer, and check whether any slot is anticipated at each turn. To evaluate aborted games, we track the % of completed probing rounds.
Implementation We implement the questioner programmatically and let the cLLM play the role of the answerer. For each experiment (i.e., domains), we generate 10 instances by randomly selecting values for all slots (from a predefined list) and a random order for the questioner's 's requests (to re- duce the possible effect of script knowledge, as this is not what we aim to evaluate here). In slot filling turns, if the agent uses the wrong tag, the game is aborted immediately. We consider that a slot was filled if the answer contains its value. We also check whether it contains any other valid value and update the probing ground truth accordingly.' 12 In probing rounds, the game master prompts the model to answer yes or no. If, for some reason, it was not possible parse valid response during probing, we add additional instructions for clar- ity in the request. After the maximum number of 5 failed attempts, an invalid response symbol is used instead and the game will be aborted after that probing round is finished. Each probing question is posed on its own and does not get appended to the dialogue context in subsequent turns. For instance, after (q,, a.), the i + 1-th sequence of probes is made. At request i 1, however, the dialogue con- text contains only the game questions and answers up to turn and none of the probes.
Episode-Level Scores: At the end of an episode, (n + 1)n answers have been collected via prob- ing. We compute accuracy across all answers. However, given that this a binary classification task, the random performance is very high. We thus also compute Cohen's 's k (Cohen, 1960) as an episode-level score. As discussed in (Madureira and Schlangen, 2022), a model biased towards con- sidering all values as private would perform well at initial turns, whereas models biased towards shared would perform well at final turns. We follow their suggestion also evaluate the performance in mid- dle turns, where the distribution of labels is more balanced. For that, we report the accuracy at the middle probing round, namely middle-accuracy (m- acc). The validity of the results rely on the slots having been correctly filled. As sanity check, compute the proportion of answers that con- tain the correct slot value as an additional episode level score, named dot-filling-accuraa Finally, we measure the proportion of slots that were disclosed when requested for (i.e., were not anticipated), named timing.
(sf-acc). 14
Evaluation In addition to the benchmark com- mon metrics in Section B, also define turn and episode-level scores to capture the game specific behaviour. Besides following the game instruc- tions (in particular, using the correct answer tags), a
Quality Score: The harmonic mean between slot- alluura and x. (truncated at 0) is normalised to [0, 100) and used as the main score, summarising the performance of an agent in an episode.
13 Given that this textual data, means parsing the history correctly and identifying what is (not) there. "However, even if the cL.LM hallucinates an answer, the probing can still be performed, because a wrong value is still a shared value.
While this aims to capture valid anticipated values, like "I want to travel from Berlin to Lisbon" when prompted for the origin only, note that this does not work well for models that hallucinate next turns in their answers.

---

n
%
%lyd
gity
win rounds
reqrt
sf-ace
timing
m--cc
3
travel
10
20
54.90
0.27
0.35
0.90 (0.14)
0.30 (0.42)
0.40 (0.04)
0.50 (0.14)
job
10
0
/
0.00
0.00
/
restaurant
10
0
/
0.05
0.16
/
things
10
10
0.00
0.10
0.34
1.00
0.87
0.00
0.47
letter
10
50
0.60
0.62
0.60
1.00 (0.00)
0.68 (0.11)
0.00 (0.01)
0.52 (0.04)
3,5
travel
10
90
70.81
0.93
0.99
1.00 (0.00)
0.96 (0.13)
0.57 (0.20) 0.73 (0.20)
job
10
50
62.98
0.50
0.57
0,96 (0.09)
0.92 (0.18)
0.47 (0.08)
0.72 (0.11)
restaurant
10
100
78.34
1.00
0.99
0.96 (0.08)
1.00 (0.00)
0.67 (0.13) 0.72 (0.21)
things
10
70 72.15
0.88
0.94
1,00 (0.00)
1.00 (0.00)
0.57 (0.11) 0.80 (0.12)
letter
10
90.00 10
0.25
0,68
1.00
1.00
0.82
0.90
4
travel
10
100
89.96
1.00
1.00
1.00 (0.00)
1.00 (0.00)
0.82 (0.07) 0.86 (0.10)
job
10
100
81.39
1.00
1.00
0.94 (0.13)
0.84 (0.21)
0.73 (0.10)
0.78 (0.15)
restaurant
100
94.00
20
1.00
1.00
0,96 (0.08)
1.00 (0.00)
0.93 (0.07)
0.94 (0.13)
things
9
100
89.80
1.00
1.00
1.00 (0.00)
1.00 (0.00)
0.82 (0.13) 0.87 (0.14)
letter
10
100
98.71
1.00
1.00
1.00 (0.00)
1.00 (0.00)
0.97 (0.01) 1.00 (0.00)
travel
87.54
10
1.00
1.00
1.00 (0.00)
0.96 (0.13) 0.79 (0.13) 0.88 (0.10)
job
100 52.56
1.00
1.00
0.96 (0.08)
0.80 (0.21)
0.38 (0.15) 0.62 (0.18)
restaurant
100 89.62
1.00
1.00
0.96 (0.08)
1.00 (0.00)
0.85 (0.09) 0.92 (0.14)
things
100 95.90
1.00
1.00
0.99 (0.02)
1.00 (0.00)
0.93 (0.04) 0.97 (0.03)
letter
100 98.71
1.00
1.00
1.00 (0.00)
1.00 (0.00)
0.97 (0.01) 1.00 (0.00)
travel
0.12
0.60
job
0.17
restaurant
0.05
0.27
things
0.00
letter
0.12
0.73
travel
0.00
0.00
job
0.00
0.00
restaurant things letter
0.00
0.08
0.48
0.00
0.00
fle
*
10
0
/
0.00
0.00
1
1
ko
10
0
0.00
0.00
/
1
7
Im
10
0
0.00
0.00
Table 8: Detailed results in the scorekeeping game by experiment, Values are % or means over episodes, with std. deviation in parenthesis. is the sample size, All metries are in [0, (or 100, if % or quality) and higher better.
G.2 Additional Discussion of Results
slot filling but with performance at chance level in the prohing task.
Table 8 presents all detailed results for this game. Luminous could not play any of the versions; al- though it began giving yes/no answers in the first probing round, failed use the correct player tag defined in the initial prompt and the games were therefore aborted. Falcon, Koala, Vicuna and Assistatt also could not use the correct tags. Besides, the first three did not comply with the in- struction to give short answers and often invented upcoming turns. Open Assistant, and Vicuna in one experiment, did manage play for some turns, but requiring reprompts. GPT-3 also failed, in general, the correct player tag. Although reprompting sometimes helped, it still could not play most the instances. Only in the numbered letters experiment it managed to play half the instances, doing well in
For the error analysis, we will thus focus on models that succeeded at playing most episodes, namely GPT-3.5, GPT-4 and Claude. Two relevant dimensions evaluate the probings detail are the slot types (to understand effects of their semanties) and their positions the main dialogue (to under- stand effects of the ordering). Table presents the accuracy per slot value for all experiments. Their detailed performance across the episode is shown in Figure 30a (accuracy by probing round) and Figure 30b (accuracy by position of slot in main dialogue). Figure 28 shows the distribution of the parsed an- swers in the probing rounds. Here, the ground truth distribution has exactly 50% of private and 50% of shared labels (as long as the episodes were played until the end, which is not the case for GPT-3.5).

---

frokelaooking
jobinlerview
realourart
thigspllccs
lettersumber
47.3
1.7
45.3
22.3
45.0
3.5
50.0
48.7
42.3
777
1.3
46.3
40.0
12.7
1.0
48.3
48.0
2.0
41.8
49.4
0.6
0.2
50.0
48.7
1.3
right right private shardd private shared
right privata shared private shored
right
right
right
right private shored private shared
right
right private shared private shared
right
sharad privote shorad
Figure 28: Distribution of the parsed answers in the probing rounds.
GPT-3.5 It could play most of the concrete do- mains, with the best performance in the restaurant version. The slot-filling accuracy was high, but the average performance in probing was deficient, with the best average results in the restaurant domain. Although it played better than GPT-3, it still failed to use the correct tags in some episodes, and we observed it inventing upcoming turns, as shown in the example in Figure 29. In travel booking, its performance was better in the first and last probing rounds with stable results in middle rounds; it had more difficulty with the slots to and when and with the last requested slot. In the job interviews, we see the opposite: Performance the initial and final probing rounds was lower, and the lowest accuracy was for the first slot. The only slot with accuracy 0.8 was highest-education When playing the client in restaurant, it did well in the first round, then see considerable drop in the sec- ond round, monotonically increasing again until the final round. salad was the casiest slot, and initial slots had higher performance than later ones. In the abstract domains, results are mixed. It could play a few rounds in the numbered letters version, but with lot of reprompts, and only finished it once. One reason for its malfunction is that some slots caused it to not commit to answer (e.g. don't 't have any information about whether the questioner knows the number for h or not."). Further inves- tigation needed on why some letters triggered that behaviour while others did not. It did better in the things at places version in game play and slot filling, but the probing performance was not very high and roughly stable across rounds and across slot positions, with lower accuracy for later slots. there and nowhere caused it more difficulty. In Table 28, we see that for the travel booking, things at places and (mostly) the restaurant domains, tended to consider more slot values as incorrectly shared.
A
Game Master
[((e)) ME: Has the recruiter been informed about the field your bachelor? Please answer yes no.
[A/GH] ASIDE:
RECRUITER: Can you tell me about your educational
background?
ANSWER: have Bachelor's degree Chemistry and Master's degree in a related field.
What industry experience do you have? ANSHER: have two years of experience in the chemical industry.
RECRUITER:
RECRUITER: what other skills do you have?
ANSWER: I am fluent in Spanish.
RECRUITER: When would be available to start?
Figure 29: GPT-3.5 inventing upcoming turns during probing (job interview, episode 3). This was a typical behaviour in some other models whose games were aborted, whereas the best performing models, in general, gave only the needed reply. Although penalisation was implemented for this behaviour, it is a sign that the model does not respect the roles assigned
GPT-4 This model exhibited a considerable im- provement in relation to GPT-3.5. could play all episodes until the end, with higher scores and no need for reprompting. Its performance in slot filling perfect in three experiments and, except for the job interview, it did not anticipate slots. The performance in the probing was much higher, get- ting up to an average x. of 0.97 in numbered letters. In the restaurant domain, it even achieved ceiling performance in two episodes. As we observe

---

rovelbooking
jo jobinterviow
realouron
thigsploces
leternumber
1.0
model
proking
0.5
15
tum
turn
turn
hurn
Furn
(a) Probing accuracy turn.
vevelbooking
jo biiitterview
reslouront
thingsploces
leternumber
model
probing
3.5
0.5
0.0
5
2.5
5.0 7.5
10.0
filing poskion
filing posion
position
position
position
(b) Overall probing accuracy by slot position the main dialogue.
Game Master
rant, bottom for things at places and c and g for the numbered letters. In Figure 31, we see one of its mistakes: A slot that had just been made public is still considered private in the subsequent probing.
(ai(a] what number
[ACCM]] QUESTIONER: What number
is d?
3
[A)car] ANSWER: 4333
4
(anjr 4333
Slot
filled: True
Claude In general, this model performed on a par with GPT-4. It also played all games without the need for reprompting and had one case of max- imum performance. Its slot filling performance was, on average, 0.95 in all experiments, doing slightly better than GPT-4 the job interviews. Its probing performance was similar to GPT-4 in numbered letters, being considerably worse the job interviews, worse the restaurant orders and travel booking, but around 10% better in the things at places version. Figure 33 shows an example where it generated skills not contained in the ini- tial prompt. Similar to GPT-4, its main source of mistakes considering shared values to be pri- vate. This was the case for all experiments, and in particular this occurred very often in the job inter- view version, where did considerably worse for slots requested in the beginning than in the end. Its performance through turns had a behaviour very similar to GPT-4 in travel booking, restaurant and numbered letters. In the job interview, it was al- ways outperformed by GPT-4, but was better in almost all turns in the things places experiment. It also resembled GPT-4 in doing worse for by and when in travel booking and there in things at places. reached top accuracy for bot tom and top for places and by and, again like GPT-4, letters c and g.
6
[omp]] 4333
7
3anar (...)
[A:0M] ME: Does questioner
know what the number for d is?
Please
yes
[M)GM] ASIDE:
10
GM|aK) no
Ganand Answer incorrect.
11
Figure 31: GPT-4 failing to recognise that a slot had just been made public (numbered letters, episode 7).
Table 28, its mistakes came mostly from failing to identify slot values that had already been shared (wrong private); the only exception is the abstract domain of things at places, in which is tended to consider more slots as shared than it should. In all experiments, it had very high probing accuracy in the first probe, which then decreased in the very next turns. In the restaurant and things at places, recovered again in later turns. In numbered letters, performance was a bit lower only in the second and third round, remaining always high in other steps. In terms of slot positions, accuracy was gen- erally stable for the travel booking, restaurant and numbered letters domains. For the job interview, accuracy was higher for later slots, whereas for things at places it dropped. Its accuracy was lower for by and when in travel booking, availabilin ty in the job interview and there in things at places. It reached top accuracy for dessert in the restau-
Example Interactions Figures 39 and show the main interaction and one round of probing for Claude, with metadata about whether the answers

---

troeebroling | splkoda,Di predittions by Stutho
tavekbooking apaoda__ pandatoon by aahd
horelboskin Lepkode predlctions
Fon
dbb, probteg round
from
des
Con
wher
probing dltt
probing slot
probing sel
(a) GPT-3.5
(b) GPT-4
(c) Claude
Figure 32: Predictions of the models for the travel booking domain, episode O. Dark: private, light: shared. Perfect predictions would be all dark ahove the orange line and light below
#
Game Master
optimised for chat.
[M(GM) RECRUITER: Please tell what other skills you have.
For the best players, the slot filling dynamic was handled very well in general, both in terms of giving right values and disclosing them only when prompted. Morcover, there was no need for re- prompting during probing. This is an expected con- sequence of them being optimised for instruction following during training. Scorekeeping was more challenging. Claude performed better in abstract domains, while GPT-4 did better with numbered letters but also with restaurant. almost all cases, their main type of mistake was considering shared slot values be still private. The job interview domain was the hardest. This may be because the semantic of some requests catalyse answers that are more related to the domain than to the game itself, since in job interviews answers are not expected to be short and concise. It is interesting that the difference in probing accuracy by slot type be more that 10% between the best and the worse slot in the concrete domains, while in the most abstract domain of numbered letters, the difference range is much narrower. Further investigation with larger datasets is necessary to know if that is due to the chosen slot values or to some interaction with the random request order.
RA/OK ANSWER: I have excellent communication and interpersonal skills.
fluent English and German. also proficient in Microsoft Office programs like kord, Excel and PowerPoint.
Figure 33:
Claude generating skills included in the
instance (job interview, episode 1).
were correct. Figures 32 depicts the predictions made by the models on the same episode, where different types of errors occurred. GPT-3.5 failed to identify many of the shared values and made unstable predictions, alternating between private and shared (multiple times for to). GPT-4 delayed the status flips for by, class and to. Claude antic- ipated the flip position for from and to and never guessed for when.
Discussion Some models did not abide to the game rules for using two different tags when ad- dressing different "interlocutors". That was the rea- son why many games were aborted, even though the answers were plausible in some cases. This comes as no surprise, as we are trying to simu- late multi- party conversation using an interface that is not optimised for that. Interestingly, mere reprompt with generic addition (e.g. "Please answer this question carefully.") sometimes was enough trigger the model use the right tag (even though its mistake was not added to the his- tory). Generating upcoming turns by other players was as well a common malfunction models not
Limitations Upon closer examination of the in- teraction transcripts, we noticed that the slot filling accuracy would be higher if the checking went beyond exact string matching. For example, a mis- spelled zucchinni value that was spelled correctly by the model, or playing the piano for the value playing piano were considered wrong. Further adjustments are necessary in the framework to capture these cases.
answer

---

You are customes of a travel agency- Here is TEMPLATE G.2.1
иHAT: Travel
FROM: Cologne
TO: Lisbon
BY: Overnight bus
CLASS: The most comfortable
cl
3.5
WHEN: Anytime next week
travel
by
81.67
78.57
80.00
class
96.67
87.50
96.67
from
96.67
80.36
96.67
to
91.67
73.21
93.33
TEMPLATE G.2.2
when
80.00
71.43
88.33
a description of the details of the travel you
job availability
66.67
60.00
80.00
want to make:
bachelor
65.00
65.71
88.33
highest-education
60.00
80.00
86.67
SINSTANCES
industry-expeet expeerene
70.00
68.57
85.00
other-skills
76.67
74.29
91.67
The travel agent does not know about it yet.
restaurant
appetizer
95.00
78.33
98.33
Questions from the travel agent will start with TRAVEL-AGENT. Please reply in the form: ANSWER: <some text>
dessert
98.33
78.33
100.00
drink
96.67
80.00
98.33
main-dish
90.00
88.33
95.00
salad
81.67
93.33
90.00
I will also ask you questions. These questions will start with ME: Please answer these questions with: ASIDE: <some text>
things
bottom
100.00
98.58
100.00
center
99.38
76.60
95.14
everywhere
98.75
68.09
96.53
here
94.38
71.63
95.14
Important: Give short, direct answers!
inside
95.62
68.79
83.33
95.62
64.54
98.61
Let Us start.
northeast
98.12
90.07
87.50
northwest
96.25
92.91
90.97
nowhere
94.38
60.99
83.33
outside
98.12
63.83
97.22
TEMPLATE G.2.3 TRAVEL -AGENT: SAGENT-QUESTIONS
right
99.38
80.14
97.22
southeast
98.12
95.04
86.81
southwest
98.75
97.87
86.81
there
78.75
60.99
76.39
top
100.00
87.94
92.36
TEMPLATE G.2.4 ANSWER: SANSWERS
letter
98.18
88.89
96.36
98.18
100.00
98.18
100.00
94.44
100.00
99.09
97.22
99.09
TEMPLATE G.2.5 ME: SCG-QUESTIONS
99.09
94.44
99.09
97.27
97.22
98.18
100.00
83.33
100.00
99.09
77.78
99.09
99.09
97.22
99.09
TEMPLATE G.2.6 ASIDE: XCG-REPLYS.
97.27
94.44
98.18
Table 9: Accuracy in the probing rounds slot value.
Figure 34: Travel agency version of the scorekeeping dialogue game. From top: Example Instance, Initial Prompt for Customer, Next-Round Template for Main Task, Response Parsing Schema for Customer Action, Next-Round Template for Probing Task, Response Pars- ing Schema for Reply to Probing Question.

---

TEMPLATE G.2.13
TEMPLATE G.2.7
WHAT: Job Application BACHELOR: Mechanical Engineering INDUSTRY-EXPERIENCE: Two years HIGHEST-EDUCATION: PostDoc
WHAT: Restaurant
APPETIZER: Fries
DRINK: Orange juice
MAIN-DISH: Burrito bowl
OTHER-SKILLS: Software development AVAILABILITY: From September
DESSERT: Strudel
SALAD: Avocado salad
TEMPLATE G.2.14
TEMPLATE G.2.8
You are an applicant in a job interview. Here is description of your CV:
You are a customer in restaurant. Here is description of what you'd 'dd like to order:
SINSTANCES
SINSTANCES
The recruiter does not know about it Questions from the recruiter will start with RECRUITER. Please reply in the form: ANSWER: <some text>
The waiter does not know about it yet.
Questions from the waiter will start with WAITER. Please reply in the form: ANSWER: <sone text>
will also ask you questions. These questions these
will start with ME: Please questions with: ASIDE: <some text>
I will also ask you questions. These questions will start with ME: Please answer these questions with: ASIDE: <some text>
Important: Give short, direct answers!
Important: Give short, direct answers!
start.
Let us start.
TEMPLATE G. G.2.9 RECRUITER: SAGENT-QUESTIONS
TEMPLATE G.2.15 WAITER: SAGENT-QUESTER
TEMPLATE G. 10 ANSWER: SANSWERS
TEMPLATE G.2.16 ANSWER SANSWERS
TEMPLATE G.2.11 ME: IUESTUUUSOONN
TЕМPLATE G.2.17 ME: SCG-QUESTIONS
TEMPLATE G.2.12 ASIDE: NCG-REPLY%
ТЕМPLATE G.2. ASIDE: SCG-REPLYX
Figure 35: Job Interview version of the scorekeeping dialogue game. From top: Example Instance, Initial Prompt for Job Applicant, Next-Round Template for Main Task, Response Parsing Schema for Applicant Ac- tion, Next-Round Template for Probing Task, Response Parsing Schema for Reply Probing Question.
Figure 36: Restaurant version of the scorekeeping di- alogue game. From top: Example Instance, Initial Prompt for Client, Next-Round Template for Main Task, Response Parsing Schema for Client Action, Next- Round Template for Probing Task, Response Parsing Schema for Reply Probing Question.

---

TEMPLATE G.2.25
WHAT: Things at places
TEMPLATE G.2.19
LEFT: piano
WHAT: Numbered letters
RIGHT: pencils
A: 3010
TOP: pens
B: 2345
BOTTOM: skirts
C: 6666
CENTER: bees
NORTHWEST: eyeliner
D: 7666
E: 4353
NORTHEAST: pants
SOUTHWEST: apples
F: 6578
G: 5656
SOUTHEAST: pineapple
H: 9212
HERE: napkin
I: 2882
THERE: camellia
J: 7804
NOWHERE: utility blade
EVERYWHERE: mascara
INSIDE: whale
OUTSIDE: rhodium
TEMPLATE G.2.20
Numbers have been assigned to letters, but the questioner does not know about it yet. Here is the mapping:
TEMPLATE G.2.26
Things are in different places, but the questioner does not know about it yet. Here is the si sitution::
$INSTANCES
Questions inn from the questioner will start with QUESTIONER. Please reply in the form: ANSWER:
$INSTANCES
<some text>
Questions from the questioner will start with QUESTIONER. Please reply in the form: ANSWER: <some text>
will also ask you questions. These questions will start with ME: Please answer these questions with: ASIDE: <some text>
I will also ask you questions. These questions will start with ME: Please answer these questions with: ASIDE: <some text>
Important: Give short, direct answers!
Let start.
Important: Give short, direct answers!
Let start.
TEMPLATE G.2.21 QUESTIONER: RAGENT-QUESTTEE
TEMPLATE G.2.27 QUESTIONER: SAGENT-QUESTINN QUEESTIOES
TEMPLATE G.2.22 ANSWER: SANSWERS
TEMPLATE G.2.28 ANSWER: SANSWERS
TEMPLATE G.2.23 ME: SCG-QUESTIONS
TEMPLATE G.2.29 ME: XCG-QUESTIONS
TEMPLATE G.2.24 ASIDE: XCG-REPLYX
TEMPLATE G.2.30 ASIDE: SCG-REPLYS
Figure 37: Abstract version of the scorekeeping dia- logue game using numbered letters. From top: Exam- ple Instance, Initial Prompt for Answerer, Next-Round Template for Main Task, Response Parsing Schema for Answerer Action. Noun-Round Template for Probing Task, Response Parsing Schema for Reply to Probing Question.
Figure 38: Abstract version of the scorekeeping di- alogue game using things assigned to places. From top: Example Instance, Initial Prompt for Answerer, Next-Round Template for Main Task, Response Parsing Schema Answerer Action, Noxt-Round Template for Probing Task, Response Parsing Schema for Reply Probing Question.

---

# A
Game Master
A
Game Master
B
[eV|e Begin probing [M( the travel agent aware of your origin? Please answer yes or no. pyjar ASIDE: Yes.
[initial prompt]
(ON)) What is the next request?
[oN(B] Where does your trip
begin?
[A(0/] TRAVEL-AGENT: Where does
3
your trip begin?
4
tomjog yes
Lxaa ANSWER: Cologne.
[оH|o)] Answer for FROM valid
(01(0) Cologne.
5
teMjad
Slot
after tries.
filled: True
[oljo Answer is correct.
[ow;0 Cologne.
(ON)) What is the next request?
[A ME: Do you believe that the travel agent is aware of where you are going? Please answer yes or no.
[o4/0] Please let me know what
kind of transport you want.
[NC TRAVEL-AGENT: Please let me know what kind of transport you want.
11
8
DANIO ASIDE: Yes.
9
12
ANSWER: Overnight bus.
[aw|n Answer for TO valid
tosjod Overnight bus.
10
13
after tries.
(eH|N)
Slot
[oyon Answer is correct.
14
11
filled: True (OwB) Overnight bus.
[A(or ME: Does the travel agent
[ospa] What the request?
already whether you go
12
[ew(0] What kind of class do you wish?
by plane by train? Please answer yes no.
17
[MGK] TRAVEL-AGENT:
kind
LX30 ASIDE:
18
class you wish?
[оN|od no
19
CA)
ANSWER:
The most
tomjod Answer for BY valid after 1 tries. temen Answer is incorrect.
comf table..
15
temjan The most comfortable. Slot
20
16
[eqjan filled: True [OK]] The most comfortable. [CN): What is the next request?
21
[A(O: ME: Is the travel agent aware of which class you would like to book? Please answer yes or no.
17
23
24
[ow/]] When is the trip?
TRAVEL-AGENT: When is the
18 payaa ASIDE: No.
trip?
19
[O[]Ot] no
ANSWER:
Anytime next
[o|j00] Answer
week.
20
for CLASS valid after 1 tries.
toqjog Anytime
next week.
[ejjed] Answer is incorrect.
21
(DN(G))
Slot
filled: True
[Nce ME: Is the travel agent of the dates of your
(owe) Anytime next week.
22
[owpa What the request?
trip? Please
yes
[e4()] where are you going to?
23
033
ASIDE:
32 [AAGK] TRAVEL-AGENT: where are
24
[[]]e]] no
you going to?
[оN|e4] Answer for WHEN valid
Dxya ANSWER: Lisbon.
25
[e||e)) Lisbon.
after tries.
[ON|NN]
Slot
26
[оk|od] Answer incorrect.
filled: True
36
[GNy02 Lisbon.
[emen
End
27
probing
Figure 39: Scorekeeping: Excerpt of slot filling
turns for Claude.
Figure 40: Scorekeeping: Probing at the last round for Claude.


Reviews:
[{"review_id": "KtkBR1sS9X", "reviewer": "Reviewer_dwAD", "summary": "", "questions": "A. Failure modes for GPT-4, GPT-3, and open-source models. It is mentioned in line 420-424 that GPT-4 does not reach 100% on percent played for all games. Can you provide specific examples within the drawing and taboo games where GPT-4 fails at this task, to motivate future work that models need to improve on? \nB. Failure modes for Open-Source models - It is mentioned in the “Ethics Statement” that open source models are behind closed source models at the games provided in the clembench benchmark. Table 2 seems to support this to show that the open source models (ko/flc/ost/vcn) are all particularly bad at the non-wordle games. What do these failures look like in non-wordle games? Do there exist common themes in the types of mistakes that existing open-source models are making that closed-source models are doing comparatively better at?\nC. New games - Given that GPT-4 completes most games with 100% success rate, what types of new games do the authors think that the authors/community members can design where GPT-4 wouldn't be able to successfully complete the game? ", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer dwAD for their receptive and encouraging comments.\n\nWe would like to take the opportunity to address some of the issues raised and the questions.\n\n**Q-A & Q-B: more discussion of the failure modes for the otherwise successful models (GPT-3.5 & 4), and for the open source models.**\n\nThis is an excellent suggestion. Should the paper be accepted, we will use the additional page to move some of the existing discussion of failure cases from the appendix into the main part. We will also provide more examples both on the instruction-following level (wrongly formatted, and hence non-parseable, output) and the game-playing level (wrong / unhelpful game moves). \n\nWe will also stress more what, according to our findings, at the moment marks the greatest difference between locally-installable and closed models, namely the ability to follow instructions on the expected *form* of the output. Our hypothesis is that at the current state, the locally-installable models are insufficiently trained on instruction following to even reach the gameplaying stage reliably.\n\n\n**Q-C: saturated for GPT-4? new games?**\n\nThe question of the predictive value of the instrument is a great one. We think that the work described here has laid the foundation for many more detailed investigations, which will have to show how predictive performance on one game is for another (and hence, to what extent our analysis of the degree of involvement of various aspects of the underlying capability is correct).\n\nWhen it comes to GPT-4's performance, however, we need to point out that there might be a slight misunderstanding here: While GPT-4 has high success in playing the games as such (that is, following instructions; as measured by % played), there is still ample room for improvement when it comes to successfully playing the games (as measured by the quality score). [You can think of this as the difference between being able to make legal moves in chess on the one hand, and winning the game on the other.] In that respect, we consider our benchmark not yet saturated, and there are clear areas for improvement even for GPT-4 (for example, in reasoning with and integrating over the whole interaction the letter-based feedback in wordle).\n\n\nThank you for the reference to the Aher et al paper, which we will include, and for the suggestion for stylistic improvements, which we will implement, should the paper be accepted.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Novel benchmark - This paper provides a new benchmark to evaluate language models. It fills an existing need to test language models at a deeper level, particularly at \"interactive dialogue aspects of chat-based LLMs\". As such, it is valuable to the broader NLP community.\n2. Analysis of existing language models - This paper demonstrates that newer, larger, and proprietary LLMs tend to be better at these simple dialogue games.", "reasons_to_reject": "Insufficient qualitative analysis of failure modes - The paper makes claims that newer, larger models tend to be better at the games they introduce (83-85). As such, the paper provides experiments (as seen in Table 2) to substantiate these claims. However, to better understand why models fail these types of dialogue games, more qualitative analysis should be included as to what failure modes look like for both older, smaller models and newer, larger models. For instance, what do failing models tend to output for games that they fail at? Are there trends or common themes amongst these outputs? ", "questions_to_authors": "A. Failure modes for GPT-4, GPT-3, and open-source models. It is mentioned in line 420-424 that GPT-4 does not reach 100% on percent played for all games. Can you provide specific examples within the drawing and taboo games where GPT-4 fails at this task, to motivate future work that models need to improve on? \nB. Failure modes for Open-Source models - It is mentioned in the “Ethics Statement” that open source models are behind closed source models at the games provided in the clembench benchmark. Table 2 seems to support this to show that the open source models (ko/flc/ost/vcn) are all particularly bad at the non-wordle games. What do these failures look like in non-wordle games? Do there exist common themes in the types of mistakes that existing open-source models are making that closed-source models are doing comparatively better at?\nC. New games - Given that GPT-4 completes most games with 100% success rate, what types of new games do the authors think that the authors/community members can design where GPT-4 wouldn't be able to successfully complete the game? ", "paper_topic_and_main_contributions": "This paper presents a benchmark to evaluate language models at interactive dialog-based games. The authors design games that chatbots can work through turn-by-turn. They test existing open-source and closed-source language models based on their abilities to complete each game successfully and on the quality of how the game was played (a metric designed specific to each game). They release their python benchmark as a GitHub repository and claim that community members can add new games and models from the community to their code.\n\nRegarding the type of contribution they provide to EMNLP, the paper provides an NLP engineering experiment and open-source benchmark that can be used to evaluate different language models at their abilities to evaluate language models at their conversational agent capabilities.", "excitement": 4, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Sentence “While the public… not a clever dog” (34-45) can be split into 2-3 shorter sentences for conciseness and clarity. "}, "extra_text": {"Missing_References": "Although not absolutely necessary, this work can cite prior work (Aher 2022 https://openreview.net/pdf?id=eYlLlvzngu), which analyzes how large language models can imitate and simulate multiple humans. This can be paired with the Park 2023 work that they currently cite as works that simulate LLMs as agents in a multi-agent system from a HCI/sociological standpoint."}}, {"review_id": "RHR2GI2x25", "reviewer": "Reviewer_6HrB", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 2, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer 6HrB for their very encouraging reception of our work and their appreciation of the underlying idea and its realisation.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The idea behind evaluating language models using a game is clever, and provides a good benchmark for aspects of language understanding. The authors conduct a thorough analysis and provide useful results for the community.", "reasons_to_reject": "None, I think the paper should be accepted.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The authors present a new benchmark for evaluating LLMs using text-based games like wordle and taboo. They then conduct a thorough analysis of current SOTA language models (both open and closed source) and analyze their performance. They find that agents generally get better over time, but are not near human level performance, rendering this benchmark important for testing language understanding skills.", "excitement": 4, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "9UY9eryx2l", "reviewer": "Reviewer_LsBC", "summary": "", "questions": "* Why do these need to be two-player games? Why transform single-player games into two-player? Is this purely for convenience or does it have some diagnostic intent?\n* How are the mixed LLM games meant to be interpreted? How does agent/agent play assess ability? Imagine an agent excels at one role and not the other, won't it fail the test?\n* You refer to the use of world models multiple times. Is this an assumption that LLMs build an implicit world model, or are you refering to something explicit like the use of an autoencoder or knowledge graph?\n* How were the quality scores for each game determined? For example why use F1 for the drawing game, while using simple binary measures for the picture reference game?\n* What is the methodology for running the experiments: is it zero-shot or few-shot, what were the sampling hyperparameters?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer LsBC for their very thorough engagement with our work, and hope that we can clear up some of the (very helpful and interesting!) issues raised.\n\nWe will start with the direct questions and then address the issues raised elsewhere in the review.\n\n**Q1: Why two-player games?**\n\nIt isn't necessarily the two-player nature that we're after (one could well imagine interesting games with *more* players) but rather the round-based interactivity. In that sense, wordle is easily framed as a two-player game, with one \"player\" -- the feedback-giver -- being fully rule-based. The overall effect is still one of creating a dialogue context for the guesser, in which earlier material (the earlier hints) needs to be integrated (what is the overall information that now constrains the guess).\n\nBut in general, we're interested in covering the whole continuum, which is why we've included a single-round game (the reference game) as well.\n\n\n**Q2: How are mixed LLM games to be interpreted? How does agent/agent play assess ability? Imagine an agent excels at one role and not the other, won't it fail the test?**\n\nIn the case where the same model-type is used for both player roles, we can assess, but not separate, the strength of capabilities needed for either role, and indeed in an overall sense the ability to be responsive to the other player's contribution. This, we contend, is a good basis for relative ranking of models.\n\nAs an additional opportunity for finer-grained analysis, the mixing of models can bring out separate strengths: For the games that have asymmetric roles (e.g., taboo, with the hint giver and the guesser), are certain models better at one or the other role? (This has to be expanded on in future work, for which this paper is meant to lay the foundation.)\n\n\n**Q3: You refer to the use of world models multiple times. Is this an assumption that LLMs build an implicit world model, or are you referring to something explicit like the use of an autoencoder or knowledge graph?**\n\nThank you for this question, which allows us to emphasise again an important point: We are aiming for a measurement of underlying capabilities, expressed and accessed only through behaviour. As such, the test instrument is agnostic as to how the capability is realised in the model. The claim here is that to successfully play some of the games, a certain capability likely needs to be present. How it is realised -- implicitly or more explicitly guided by the model designer -- is an independent question. Should the paper be accepted, we will stress this point further.\n\n\n\n**Q4: How were the quality scores for each game determined? For example why use F1 for the drawing game, while using simple binary measures for the picture reference game?**\n\nWe aimed for a certain degree of fine-grainedness in each metric, so that where we can give partial credit (as in the drawing game), we do so. How to do so depends on the individual game (what can count as partial success). For the reference game, a more fine-grained measurement did not suggest itself (either the right object is picked or not, in a single-round game); and also, this follows precedent in the psycholinguistic literature that uses this game.\n\n\n**Q5: What is the methodology for running the experiments: is it zero-shot or few-shot, what were the sampling hyperparameters?**\n\nWe will consolidate this information in a central place, should the paper be accepted. In short: The inference parameters are mentioned in Section 5 (temperature set to 0). The prompts are shown in the appendix. Many of them (besides the private/shared game) give examples of the expected form of the player moves, embedded in a free text; in that sense, they could be called few-shot. We arrived at these prompts through light experimentation with GPT-3.5 and other models. We need to stress again that prompt engineering is *not* our concern here -- we are after an instrument that can (fairly) distinguish, not after optimisation.\n\n\n**R1-a: Comparison with existing benchmarks**\n\nWe are not claiming that this benchmark should *replace* existing reference- or preference-based ones. It is *complementary* to them in that it challenges interactive, context-dependent capabilities. Related attempts that directly target interactivity are only now starting to appear (in preprint form); we will use the additional space, should the paper be accepted, to acknowledge these.\n\n*R1-b: It's not clear how this approach addresses any gaps in situated language understanding (SLU) that might be present in previous evaluation approaches?*\n\nThe standard benchmarks (GLUE, HELM, BIG-BENCH) are almost exclusively reference-based and static. On the other hand, most work on situated language understanding (e.g., TEACh, ALFRED, Minecraft) focusses on complex environments and uses purpose-built models. (It is not inconceivable that general foundation models will reach the required flexibility at some point. When they do, clemgame will be ready to provide the interface.)\n\n\n**R2: human validation**\n\nIn the current state, the instrument needs to show its worth through its discriminative power between models (that is, the ability to rank them). This, we believe, it does.\n\nHaving a human ceiling for performance is of course very desirable, but for now left to future work. Let us note that such a collection effort, if done correctly and for the number of games that we have here, requires quite a substantial effort --- which, if demanded for a single conference paper, would drive all but the highest funded industrial labs out of the game --; for now, we focussed on the already quite substantial effort required in laying the foundations. We believe that this does not take away anything from our argument as presented here.\n\n\n\n**reproducibility**\n\nWe would like to point out that we not only promised to release the full code on publication, but we also made the effort of already releasing the code via an anonymous repository (URL in the paper), for full inspection. We hope that this alleviates worries about reproducibility.\n\n\n\n**holds promise [...] rewrite**\n\nWe would like to ask LsBC to reconsider their assessment that the noted issues would \"require a major rewrite\". We believe that some of the issues have already been addressed in the paper (see above) and can easily be made more prominent in a final version, while we see some of the other issues as very valuable suggestions for further work, for which however the present paper needed to lay the (rather substantial) foundation.\n\nWe read the substantial (and very valuable!) engagement that R3 has given this paper as a sign that they see productive value in the paper already (if only for generating further ideas!), and ask them to reflect this in their decision.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* The paper introduces a new framework for testing chat-oriented LLMs for situated language understanding with an associated set of benchmarks.\n* The benchmark suite is open source and will be released upon acceptance and supports the addition of new tests in the future.\n* The test sets for the benchmark suite have been created from scratch, so there is little risk of overlap with the training data of LLMs.", "reasons_to_reject": "While this paper may hold promise, there are numerous issues that require a major rewrite to clearly explain why a new benchmark is needed and how it improves upon existing benchmarks:\n\n* There is no comparison to any existing benchmarks to explain how or why this approach is better than existing evaluation approaches. It's not clear how this approach addresses any gaps in situated language understanding (SLU) that might be present in previous evaluation approaches?\n* Why are two-player text-games a good proxy for assessing SLU? This is even more confusing because one of the games is inherently a single-player game and is transformed into a two-player game to fit into this framework. Furthermore, the experiments include examples where different models take on the role of Agent 1 and Agent 2. What research question is being answered by these mixed LLM benchmarks? (Note that the paper references Schlangen 2023 [1], a preprint from April, to motivate the approach, though that paper doesn't seem to address the need for multi-agent games, only that they are possible and likely different from LLM training.)\n* There is no human validation for these benchmarks. Would these benchmarks align with human judgements (which is typically desired)? If that's unnecessary for some reason through the design of the benchmarks, then maybe that should be explicitly called out. Rather the only mention of human performance in the paper is the expectation that humans would perform well on the tasks (again without any validation, simply stating that the tasks are relatively easy).\n\n\n### Updates After Rebuttal\n\nWhile I still strongly believe the authors should provide more concrete validation for their claims, ideally in the form of a human evaluation, in discussion with the other reviewers, I've decided to raise my soundness score from 2 to 3. Since at least some of the rankings match intuition GPT-3 < GPT-3.5 < GPT-4, there is some credence that this approach might mirror human expectations. Since the authors intend this ranking to be general across current and future LLMs, I do not think the methodology can fully support their claims, which is why I do not provide a soundness score of 4.\n\n[1] David Schlangen. 2023. Dialogue games for benchmarking language understanding: Motivation, taxonomy, strategy.", "questions_to_authors": "* Why do these need to be two-player games? Why transform single-player games into two-player? Is this purely for convenience or does it have some diagnostic intent?\n* How are the mixed LLM games meant to be interpreted? How does agent/agent play assess ability? Imagine an agent excels at one role and not the other, won't it fail the test?\n* You refer to the use of world models multiple times. Is this an assumption that LLMs build an implicit world model, or are you refering to something explicit like the use of an autoencoder or knowledge graph?\n* How were the quality scores for each game determined? For example why use F1 for the drawing game, while using simple binary measures for the picture reference game?\n* What is the methodology for running the experiments: is it zero-shot or few-shot, what were the sampling hyperparameters?", "paper_topic_and_main_contributions": "This paper introduces a new suite of benchmarks for evaluating chat-oriented LLMs.\nThe benchmarks are designed as two-player games that can be played through a text interface and assess certain representational domains (e.g. world modeling).\nThe basic premise is that agents interact with the text games and must demonstrate situated language understanding (SLU) in order solve the specified task.\nTo mediate and assess the performance of the models, an automated game master which strictly checks responses for formatting and game rule violations is utilized.\nThe paper introduces five text-based games: the word game taboo, a wordle clone, a drawing game, a picture matching game, a knowledge exchange and probing task.\nThe framework also supports the addition of new text-based games by future researchers.\n\nThe paper then describes a set of experiments where a collection of chat-oriented LLMs such as Claude, Vicuna, GPT-3.5/4, etc are tasked with solving these text-based games.\nUnfortunately, I saw no information regarding how the models were prompted: was it zero-shot or few-shot, how were the prompts constructed (there is no reference to this in the main body of the paper, but the appendix includes some details), sampling hyperparameters, etc.\nThe results do seem to indicate models that are known to be fairly strong, such as GPT-3.5/4 and Claude, outperform many of the open models such Open Assistant, with GPT-4 achieving the highest scores.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "* I think less time could have been spent on each individual game, and more on the basic premise to better frame why the approach makes sense and how it helps evaluation. Rather, things like how these games were encoded for LLMs seems more important (especially considering the expectation that future researchers can extend the set of games), while one specific game could be detailed in the main body with the remaining detailed with figures in the appendix. The main paper could focus on why these four games were chosen and how each advances situational language understanding in a way that cannot currently be done.\n* Maybe these language games should be validated by humans. Even a small pilot study would be helpful. First, to see human performance (rather than assume humans can easily solve the tests) and second, to assess the relative performance of the various models on these questions in a more free-form way (one that does not require the strict checking of the game master) to have a way to determine an expected performance of the models and see how close the proposed framework aligns with that (usually new benchmarks are validated against human judgements).\n* The figures are not clear and definitely need explanation. What do the different colors indicate, what is fed into the LLM at each step, etc.\n* Figure 3 is not understandable at all. I'd honestly get rid of it and simply explain it using text (it would likely take up much less space too).\n* Figure 2 needs more explanation regarding the terminology used. What is incremental processing in this framework (all LLMs assessed in this paper incrementally process one token at a time; if your conception of incremental processing is different, then you need to state that explicitly in the main body of the paper when you introduce the term)."}}]

Author rebuttal:

Meta-review: Overall, this paper makes interesting contributions to evaluating LMs as agents by framing the evaluation paradigm as gameplay. 

The paper addresses an important topic, takes an innovative (though debatable) approach, arrives at intriguing conclusions, and ultimately could inspire future work as the reviewers collectively describe. With that said, I do think there are important questions around design and decision-making with the games, largely echoing the comments of Reviewer LsBC in their thorough review. More broadly, as the paper partially describes, I am left dissatisfied with the current understanding of the validity/reliability of the benchmark (i.e. the core matters for assessing any measurement approach), the underlying theoretical understanding of the construct being evaluated (I think the paper does an alright job, deferring partially to prior work, but we really need a very good understanding of what we are trying to measure before we can say we are doing it well), and ultimately on how much this buys us over other evaluations. To me, the last point is the biggest question: its clear the evaluation regime here is very distinctive/different, but if it doesn't new/different insights, what's the point? I would like to see the authors engage with this as the core matter if the paper is accepted.
Decision: Accept-Main