Evaluating Large Language Models for Document-grounded Response Generation in Information-Seeking Dialogues (2023)

Abstract: In this paper, we investigate the use of large language models (LLMs) like ChatGPT for document-grounded response generation in the context of information-seeking dialogues. For evaluation, we use the MultiDoc2Dial corpus of task-oriented dialogues in four social service domains previously used in the DialDoc 2022 Shared Task. Information-seeking dialogue turns are grounded in multiple documents providing relevant information. We generate dialogue completion responses by prompting a ChatGPT model, using two methods: Chat-Completion and LlamaIndex. ChatCompletion uses knowledge from ChatGPT model pre-training while LlamaIndex also extracts relevant information from documents. Observing that document-grounded response generation via LLMs cannot be adequately assessed by automatic evaluation metrics as they are significantly more verbose, we perform a human evaluation where annotators rate the output of the shared task winning system, the two ChatGPT variants outputs, and human responses. While both ChatGPT variants are more likely to include information not present in the relevant segments, possibly including a presence of hallucinations, they are rated higher than both the shared task winning system and human responses.

## introduction accessing domain-specific knowledge in taskoriented dialogue modeling is a crucial aspect to provide information-seeking users with relevant, trustworthy and detailed information. often this knowledge has to be retrieved from various knowledge sources stored in diverse formats and multiple documents. once the relevant knowledge has been retrieved, a dialogue system then needs to combine it with the dialogue context and user query to generate an informed, coherent and fluent natural language response. in this paper, we present a comparison of methods for knowledge-grounded response generation in task-oriented dialogues which include traditional retrieval-augmented generation models as well as state-of-the-art large language models (llms), such as chatgpt @xcite , one of the gpt-model variants released by @xcite . while there is a wide range of possibilities to utilize llms for this task, we focus on two methods of prompting llms to investigate their capabilities for this particular scenario. the first method, uses a chat-interface ("chat-completion" foot_0 ) which takes as input 1) a dialogue context, 2) a short description of the system role and its domain, and 3) the last user utterance. it thus effectively provides the llm with context information about the topics in conversation so far and the general topic the user query is rooted in. the second method uses the llamaindex @xcite tool that combines information extraction from multiple documents with the llm. in this approach, dialogue context is used to extract relevant information from indexed documents and passed as context to the llm to generate a grounded response. our study assesses how suitable these two methods are for the given task by using the multi-doc2dial @xcite corpus for evaluation which includes task-oriented dialogues grounded in multiple documents. our paper is structured as follows: following the description of the multidoc2dial corpus in the next section, the dialdoc shared task is introduced which includes the test-set utilized in this paper. then, the four response generation methods compared in our study are described in detail, in particular focusing on the two new methods accessing the gpt-model. this is followed by a definition of the experimental design and a presentation of the results of objective and human evaluations and finished in a conclusion. ## multidoc2dial corpus the multidoc2dial dataset foot_1 contains 4.8k dialogues (61078 turns) between an informationseeking user and an agent. dialogues include an average of 14 turns and are grounded in 488 documents from 4 domains: department of motor vehicles (dmv), social security administration (ssa), federal student aid (sta) and veterans affairs (va). documents include html mark-ups (e.g. title, list) and document section information (title, text body, spans/sections). the dialogue data includes annotations at the turn-level for dialogue act, speaker role, human-generated utterance and the associated grounding span with document information. we chose this dataset for our evaluation as it provides a) task-oriented dialogues in which agent responses are grounded in multiple documents from four domains, b) the content of these documents, and b) manually entered agent responses plus gold standard passages from associated documents which provide relevant grounding information extracted from the document that may be used to assess the correctness of model generated responses. ## response generation methods this section introduces the response generation methods compared in this paper. the selection of methods was based on approaches which could a) serve as baselines because they had published performance results on the multidoc2dial corpus, b) were available at the time of writing, c) utilized the capabilities of the gpt-models, and d) could be evaluated in both objective and human evaluation. since the character of this study is to get an initial indication of the abilities of gpt-models for the given task of document-grounded response generation for information-seeking dialogue modeling, we restricted the number of approaches to a total of four, including two baseline models using established architectures and two methods employing gpt-models. ## experimental design we compare four response generation methods in a task-oriented dialogue scenario of which three are using document-grounded generation, in which user input requires the system to find and retrieve relevant knowledge from multiple documents and one method relying on the abilities of the selected llm to retrieve relevant knowledge from its own training data. methods are compared using a) objective metrics, i.e., rougel @xcite , meteor @xcite , token-level f1-score @xcite , and sacrebleu @xcite , and b) by human evaluation. ## conclusion this paper presented a study of documentgrounded response generation methods for information-seeking dialogue modeling particularly including two methods utilizing one of the chatgpt large language models for this task. the comparison was conducted by both objective metrics as well as by human evaluation. objective evaluation results showed that typical word-overlap based metrics are not suitable to fully assess the performance of these methods and human evaluation indicated that chatgpt-based models have strong potential in this domain, even exceeding appropriateness-scores for the humanauthored reference responses. just providing dialogue context and a system-prompt specifying the domain and the role of the response generator was sufficient to outperform human generated responses on the subjective appropriateness metric. while the system which additionally utilized document-retrieved information showed the highest appropriateness score, it was only marginally better than the system without additional retrieval step. however, the potential to access specific external information (or private information) not seen by the llm during training is essential in specific domains. especially when information from external documents can be reliably retrieved and methodically inserted into the llm prompts can help reduce hallucinations. however, human evaluation also visualized the challenges in assessing the accuracy and veracity of chatgpt-generated responses which can simultaneously appear very well formulated but factually wrong. in future work we plan to apply fact verification methods for assessing reliability of generated responses.