The Gap in the Strategy of Recovering Task Failure between GPT -4 V and Humans in a Visual Dialogue (2024)

Abstract: Goal-oriented dialogue systems interact with humans to accomplish specific tasks. However, sometimes these systems fail to establish a common ground with users, leading to task failures. In such cases, it is crucial not to just end with failure but to correct and recover the dialogue to turn it into a success for building a robust goal-oriented dialogue system. Effective recovery from task failures in a goal-oriented dialogue involves not only successful recovery but also accurately understanding the situation of the failed task to minimize unnecessary interactions and avoid frustrating the user. In this study, we analyze the capabilities of GPT-4V in recovering failure tasks by comparing its performance with that of humans using Guess What?! Game. The results show that GPT-4V employs less efficient recovery strategies, such as asking additional unnecessary questions, than humans. We also found that while humans can occasionally ask questions that doubt the accuracy of the interlocutor’s answer during task recovery, GPT-4V lacks this capability.

## introduction goal-oriented dialogue systems work with humans on tasks to achieve a goal @xcite @xcite . they do not always succeed in their tasks in one shot due to the failure to establish a common ground of dialogue @xcite with their human interlocutors. the task failure occurs due to various factors, including human error @xcite , system error @xcite , and misunderstandings between the two @xcite . in human-to-system dialogue, it is important for humans to finally achieve a successful goal regardless of the factor of task failure that occurs along the way. in this case, the system needs the capability to continue the failure dialogue and cooperatively recover from the task failure rather than terminating the dialogue @xcite . for example, suppose a task where an interactive autonomous driving system and a user tackle the task of going to an interior shop. the task may fail due to unexpected events, such as the destination being closed for construction or the user miscommunicating the desired location @xcite . in these cases, the system must offer alternatives or confirm the user's statements to recover from the task failure. of course, successful recovery from failure is not the only requirement for this dialogue task. as a goal-oriented dialogue, the system also demands minimizing the number of interactions to avoid frustrating the user. the system should have a dialogue strategy that makes good use of the information in the failure dialogue history and efficiently recovers the task. while the recent vision-language models (vlms) integrated with large language models (llms) have garnered attention for their ability to solve tasks at a high level through dialogue @xcite , the performance of these vlms in "failure task recovery" remains unclear. investigating and analyzing these models' failure task recovery capabilities can lead to the development of robust dialogue systems for real-world applications. for example, instead of relying solely on vlm for the entire task recovery process, we can enhance the system's overall performance by implementing rule-based modules and preprocessing vlm inputs to compensate for vlm's weaknesses. in this paper, we analyze the vlm's ability to recover the course of the dialogue as a first step toward the goal of building a system that can efficiently return to success after a task failure. we consider a problem setting in which the system performs a recovery action after a goaloriented visual dialogue with a human interlocutor fails. specifically, we leverage guess what?! game (de vries et al., 2017), which is a widely used game of goal-oriented visual dialogue. guess what?! game (de vries et al., 2017) is appropriate for a study of failure task recovery in dialogue because the goal of this game is building common ground directly. in experiments, we prepare 100 task failure samples in guess what?! game (de vries et al., 2017) and analyze the recovery capabilities of vlm by comparing them in human-to-system dialogue. our experiments reveal that vlm struggles with recovering from task failure in a goal-oriented dialogue. it frequently performs unnecessary recoveries and uses ineffective repair utterances. furthermore, humans tend to check the reliability of previous answers when errors are suspected. on the other hand, gpt-4v does not question prior utterances and fails to generate repair utterances that express doubt. ## experiments in this study, we analyze the success rate and features of the failure recovery task for humans and gpt-4v foot_1 (openai, 2024). we first collect the failure game of guess what?! @xcite to investigate failure recovery capability. then, we q1: それは、屋外で⾒られるものですか？ それは、⽣き物ですか？ それは、⼦供が使⽤するものですか？ それは、スポーツに関連するものですか？ それは、何かの道具ですか？ q5: q3: q4: q2: (is it something that can be seen outdoors?) (is it a living thing?) a1: a2: a3: a4: a5: (is it for use by children?) (is it related to sports?) (is it a tool of some kind? いいえ。 (yes.) target object: no. 7 1st prediction object: no. 5 はい。 (no.) いいえ。 いいえ。 いいえ。 figure 3: failure game example collect human and gpt-4v (openai, 2024) failure task recoveries for the collected task failure game. ## discussion we observed that gpt-4v is significantly poor at the failure recovery task ( §5.1), and gpt-4v's approach differs from the strong baseline of human behavior in both step 1 and step 2 ( §5.2). the significant difference in failure task recovery capabilities between humans and gpt-4v can be attributed to the models' difficulty with logical reasoning @xcite @xcite . gpt-4v may struggle to integrate three pieces of information from failed games (game image, dialogue text, and first prediction object) to identify potential target objects. unlike typical goal-oriented dialogues, conducting failure task recovery requires understanding complex dialogue and game situations. therefore, the preprocessing step that explicitly organizes the context of the failed game and dialogue rather than executing direct recovery actions may be practical. in guess what?! game, output which objects remain as potential targets is an example of this strategy. we also found that gpt-4v tends to refrain from questioning the interlocutor's answer ( §5.2.2). this feature is undesirable for the failure task recovery in goal-oriented dialogues, where the user's answers might contain errors @xcite . this issue is not crucial during initial task attempts because humans do not frequently make response errors. however, when the task fails, the possibility of user answer errors increases. therefore, considering the possibility of user errors is a key factor when developing recovery strategies. if a system cannot doubt the user's answers, it may fail to correct errors or waste time, as shown in figure 6 . to address this issue, instructing the llm first to evaluate the correctness of the user's answers and then use this evaluation to guide the recovery action may be effective. one future direction to use vlms @xcite as a recovery model is rethinking model training. for example, creating synthetic datasets that include incorrect utterances and using them for instruction-tuning data. this approach allows vlms to explicitly learn from erroneous scenarios, potentially enhancing their abilities to recover task failure accurately. ## limitations in this study, we examined the failure recovery task in guess what?! game, where one speaker only responds with "yes." or "no." however, this research does not address the recovery capabilities of gpt-4v and humans in more complex goal-orientated dialogues like autonomous driving dialogue systems. our results may differ for languages other than japanese, so it is essential to analyze gpt-4v's recovery performance in english, its most proficient language. this study focuses on gpt-4v, raising concerns about the generalizability of our findings to other vision-language models. we tested the failure recovery task with llava-1.5 @xcite , but it did not recover the tasks adequately, which suggests that the recovery task would require capabilities comparable to gpt-4v. we are concerned about the method of collecting intents by directly asking humans or using gpt-4v. this method assumes a causal relationship between subjective reasoning and actual behavior. as ayaß (2015) recommended, it is preferable to analyze speech intentions based on objective actual behavior rather than subjective reasoning. ## conclusion we tackled the failure recovery task in guess what?! game and analyzed gpt-4v's capabilities. the results showed that gpt-4v demonstrated a significantly lower ability to correct task failures than humans. furthermore, gpt-4v tended to perform unnecessary repair utterances, ask inefficient questions, and fail to doubt users' answers. in future work, we aim to investigate the generalizability of our findings to real-world goal-oriented dialogues.