RS
R.R. Sobha
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Evaluating the Efficacy and User Reliance on RAG Model Outputs
A comparative study with human experts
The emergence of conversational AI systems like ChatGPT and Microsoft Copilot has impacted how users engage in information retrieval.
Retrieval Augmented Generation (RAG) harnesses the potential of Large Language Models (LLMs) with unstructured data, creating opportunities in science and business.
RAG-based models have gained popularity, but their effectiveness and user reliance in organizational settings call for exploration. This thesis involves a user study with policy experts in the financial domain.
They were tasked with text aggregation using a basic RAG model. The study delves into the model’s performance and the temporal development of user reliance among the experts over four weeks.
Our key findings reveal that outputs assisted by RAG do not match the quality produced by human experts.
The RAG model, however, excels in specific aspects such as structure, spelling, and grammar.
Additionally, the experts express satisfaction with the efficiency of RAG. Our findings suggest that user reliance on RAG increases with experience.
This underscores the need for interventions and policies to support responsible human-AI collaboration.
This work represents an effort to measure the temporal aspects of user reliance within an RAG system.
Simultaneously, it assesses the system’s efficacy in a field study with policy experts in the financial domain. ...
Retrieval Augmented Generation (RAG) harnesses the potential of Large Language Models (LLMs) with unstructured data, creating opportunities in science and business.
RAG-based models have gained popularity, but their effectiveness and user reliance in organizational settings call for exploration. This thesis involves a user study with policy experts in the financial domain.
They were tasked with text aggregation using a basic RAG model. The study delves into the model’s performance and the temporal development of user reliance among the experts over four weeks.
Our key findings reveal that outputs assisted by RAG do not match the quality produced by human experts.
The RAG model, however, excels in specific aspects such as structure, spelling, and grammar.
Additionally, the experts express satisfaction with the efficiency of RAG. Our findings suggest that user reliance on RAG increases with experience.
This underscores the need for interventions and policies to support responsible human-AI collaboration.
This work represents an effort to measure the temporal aspects of user reliance within an RAG system.
Simultaneously, it assesses the system’s efficacy in a field study with policy experts in the financial domain. ...
The emergence of conversational AI systems like ChatGPT and Microsoft Copilot has impacted how users engage in information retrieval.
Retrieval Augmented Generation (RAG) harnesses the potential of Large Language Models (LLMs) with unstructured data, creating opportunities in science and business.
RAG-based models have gained popularity, but their effectiveness and user reliance in organizational settings call for exploration. This thesis involves a user study with policy experts in the financial domain.
They were tasked with text aggregation using a basic RAG model. The study delves into the model’s performance and the temporal development of user reliance among the experts over four weeks.
Our key findings reveal that outputs assisted by RAG do not match the quality produced by human experts.
The RAG model, however, excels in specific aspects such as structure, spelling, and grammar.
Additionally, the experts express satisfaction with the efficiency of RAG. Our findings suggest that user reliance on RAG increases with experience.
This underscores the need for interventions and policies to support responsible human-AI collaboration.
This work represents an effort to measure the temporal aspects of user reliance within an RAG system.
Simultaneously, it assesses the system’s efficacy in a field study with policy experts in the financial domain.
Retrieval Augmented Generation (RAG) harnesses the potential of Large Language Models (LLMs) with unstructured data, creating opportunities in science and business.
RAG-based models have gained popularity, but their effectiveness and user reliance in organizational settings call for exploration. This thesis involves a user study with policy experts in the financial domain.
They were tasked with text aggregation using a basic RAG model. The study delves into the model’s performance and the temporal development of user reliance among the experts over four weeks.
Our key findings reveal that outputs assisted by RAG do not match the quality produced by human experts.
The RAG model, however, excels in specific aspects such as structure, spelling, and grammar.
Additionally, the experts express satisfaction with the efficiency of RAG. Our findings suggest that user reliance on RAG increases with experience.
This underscores the need for interventions and policies to support responsible human-AI collaboration.
This work represents an effort to measure the temporal aspects of user reliance within an RAG system.
Simultaneously, it assesses the system’s efficacy in a field study with policy experts in the financial domain.
This paper delved into the effects of domain expertise on a user's conversational search, because as the use and acceptance of voice assistants increase the need for conversational search agent that can accommodate to a human characteristic such as domain expertise. Accommodating to disadvantaged users of web search as earlier works showed that users with low literacy and low spatial visualization abilities are strongly affected in their searches compared to users who do not suffer from these impairments. Prior research into domain expertise demonstrated the influence it has had on the querying behavior of users in web search. They found that domain experts included more domain specific jargon in their messages, made longer queries and spent less time per search task. This paper examined these findings in a conversational search setting. Contrary to these findings, no significant relation between the domain expertise level and any of these results could be established. However, conducting the experiment to assess these findings has provided insight into how users respond to a conversational search study such as this one.
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This paper delved into the effects of domain expertise on a user's conversational search, because as the use and acceptance of voice assistants increase the need for conversational search agent that can accommodate to a human characteristic such as domain expertise. Accommodating to disadvantaged users of web search as earlier works showed that users with low literacy and low spatial visualization abilities are strongly affected in their searches compared to users who do not suffer from these impairments. Prior research into domain expertise demonstrated the influence it has had on the querying behavior of users in web search. They found that domain experts included more domain specific jargon in their messages, made longer queries and spent less time per search task. This paper examined these findings in a conversational search setting. Contrary to these findings, no significant relation between the domain expertise level and any of these results could be established. However, conducting the experiment to assess these findings has provided insight into how users respond to a conversational search study such as this one.