2023 Conference article Open Access

Commonsense injection in conversational systems: an adaptable framework for query expansion
Rocchietti G., Frieder O., Muntean C. I., Nardini F. M., Perego R.
Recent advancements in conversational agents are leading a paradigm shift in how people search for their information needs, from text queries to entire spoken conversations. This paradigm shift poses a new challenge: a single question may lack the context driven by the entire conversation. We propose and evaluate a framework to deal with multi-turn conversations with the injection of commonsense knowledge. Specifically, we propose a novel approach for conversational search that uses pre-trained large language models and commonsense knowledge bases to enrich queries with relevant concepts. Our framework comprises a generator of candidate concepts related to the context of the conversation and a selector for deciding which candidate concept to add to the current utterance to improve retrieval effectiveness. We use the TREC CAsT datasets and ConceptNet to show that our framework improves retrieval performance by up to 82% in terms of Recall@200 and up to 154% in terms of NDCG@3 as compared to the performance achieved by the original utterances in the conversations.Source: IEEE/WAT - 22nd International Conference on Web Intelligence and Intelligent Agent Technology, pp. 48–55, Venezia, Italy, 26-29/10/2023
DOI: 10.1109/wi-iat59888.2023.00013
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2023 Conference article Open Access

Rewriting conversational utterances with instructed large language models
Galimzhanova E., Muntean C. I., Nardini F. M., Perego R., Rocchietti G.
Many recent studies have shown the ability of large language models (LLMs) to achieve state-of-the-art performance on many NLP tasks, such as question answering, text summarization, coding, and translation. In some cases, the results provided by LLMs are on par with those of human experts. These models' most disruptive innovation is their ability to perform tasks via zero-shot or few-shot prompting. This capability has been successfully exploited to train instructed LLMs, where reinforcement learning with human feedback is used to guide the model to follow the user's requests directly. In this paper, we investigate the ability of instructed LLMs to improve conversational search effectiveness by rewriting user questions in a conversational setting. We study which prompts provide the most informative rewritten utterances that lead to the best retrieval performance. Reproducible experiments are conducted on publicly-available TREC CAST datasets. The results show that rewriting conversational utterances with instructed LLMs achieves significant improvements of up to 25.2% in MRR, 31.7% in Precision@1, 27% in NDCG@3, and 11.5% in Recall@500 over state-of-the-art techniques.Source: IEEE/WAT - 22nd International Conference on Web Intelligence and Intelligent Agent Technology, pp. 56–63, Venezia, Italy, 26-29/10/2023
DOI: 10.1109/wi-iat59888.2023.00014
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