Rewriting Conversational Utterances with Instructed Large Language Models

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.

Early Exit Strategies for Approximate k-NN Search in Dense Retrieval

Learned dense representations are a popular family of techniques for encoding queries and documents using high-dimensional embeddings, which enable retrieval by performing approximate k nearest-neighbors search (A-kNN). A popular technique for making A-kNN search efficient is based on a two-level index, where the embeddings of documents are clustered offline and, at query processing, a fixed number N of clusters closest to the query is visited exhaustively to compute the result set. In this paper, we build upon state-of-the-art for early exit A-kNN and propose an unsupervised method based on the notion of patience, which can reach competitive effectiveness with large efficiency gains. Moreover, we discuss a cascade approach where we first identify queries that find their nearest neighbor within the closest t << N clusters, and then we decide how many more to visit based on our patience approach or other state-of-the-art strategies. Reproducible experiments employing state-of-the-art dense retrieval models and publicly available resources show that our techniques improve the A-kNN efficiency with up to 5x speedups while achieving negligible effectiveness losses. All the code used is available at https://github.com/francescobusolin/faiss_pEE

Understanding and Addressing Gender Bias in Expert Finding Task

The Expert Finding (EF) task is critical in community Question&Answer (CQ&A) platforms, significantly enhancing user engagement by improving answer quality and reducing response times. However, biases, especially gender biases, have been identified in these platforms. This study investigates gender bias in state-of-the-art EF models and explores methods to mitigate it. Utilizing a comprehensive dataset from StackOverflow, the largest community in the StackExchange network, we conduct extensive experiments to analyze how EF models' candidate identification processes influence gender representation. Our findings reveal that models relying on reputation metrics and activity levels disproportionately favor male users, who are more active on the platform. This bias results in the underrepresentation of female experts in the ranking process. We propose adjustments to EF models that incorporate a more balanced preprocessing strategy and leverage content-based and social network-based information, with the aim to provide a fairer representation of genders among identified experts. Our analysis shows that integrating these methods can significantly enhance gender balance without compromising model accuracy. To the best of our knowledge, this study is the first to focus on detecting and mitigating gender bias in EF methods.

Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering Platforms

Online Community Question Answering (CQA) platforms have become indispensable tools for users seeking expert solutions to their technical queries. The effectiveness of these platforms relies on their ability to identify and direct questions to the most knowledgeable users within the community, a process known as Expert Finding (EF). EF accuracy is crucial for increasing user engagement and the reliability of provided answers. Despite recent advancements in EF methodologies, blending the diverse information sources available on CQA platforms for effective expert identification remains challenging. In this paper, we present TUEF, a Topic-oriented User-Interaction model for Expert Finding, which aims to fully and transparently leverage the heterogeneous information available within online question-answering communities. TUEF integrates content and social data by constructing a multi-layer graph that maps out user relationships based on their answering patterns on specific topics. By combining these sources of information, TUEF identifies the most relevant and knowledgeable users for any given question and ranks them using learning-to-rank techniques. Our findings indicate that TUEF's topic-oriented model significantly enhances performance, particularly in large communities discussing well-defined topics. Additionally, we show that the interpretable learning-to-rank algorithm integrated into TUEF offers transparency and explainability with minimal performance trade-offs. The exhaustive experiments conducted on six different CQA communities of Stack Exchange show that TUEF outperforms all competitors with a minimum performance boost of 42.42% in P@1, 32.73% in NDCG@3, 21.76% in R@5, and 29.81% in MRR, excelling in both the evaluation approaches present in the previous literature.

DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts

Open-domain question answering requires retrieval systems able to cope with the diverse and varied nature of questions, providing accurate answers across a broad spectrum of query types and topics. To deal with such topic heterogeneity through a unique model, we propose DESIRE-ME, a neural information retrieval model that leverages the Mixture-of-Experts framework to combine multiple specialized neural models. We rely on Wikipedia data to train an effective neural gating mechanism that classifies the incoming query and that weighs the predictions of the different domain-specific experts correspondingly. This allows DESIRE-ME to specialize adaptively in multiple domains. Through extensive experiments on publicly available datasets, we show that our proposal can effectively generalize domain-enhanced neural models. DESIRE-ME excels in handling open-domain questions adaptively, boosting by up to 12% in NDCG@10 and 22% in P@1, the underlying state-of-the-art dense retrieval model.

SE-PEF: a Resource for Personalized Expert Finding

The problem of personalization in Information Retrieval has been under study for a long time. A well-known issue related to this task is the lack of publicly available datasets that can support a comparative evaluation of personalized search systems. To contribute in this respect, this paper introduces SE-PEF (StackExchange - Personalized Expert Finding), a resource useful for designing and evaluating personalized models related to the task of Expert Finding (EF). The contributed dataset includes more than 250k queries and 565k answers from 3 306 experts, which are annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. The results of the preliminary experiments conducted show the appropriateness of SE-PEF to evaluate and to train effective EF models.

SE-PQA: Personalized Community Question Answering

Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experiments and evaluate models for personalized search. This paper contributes to filling this gap by introducing SE-PQA (StackExchange - Personalized Question Answering), a new curated resource to design and evaluate personalized models related to the task of community Question Answering (cQA). The contributed dataset includes more than 1 million queries and 2 million answers, annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. We describe the characteristics of SE-PQA and detail the features associated with questions and answers. We also provide reproducible baseline methods for the cQA task based on the resource, including deep learning models and personalization approaches. The results of the preliminary experiments conducted show the appropriateness of SE-PQA to train effective cQA models; they also show that personalization remarkably improves the effectiveness of all the methods tested. Furthermore, we show the benefits in terms of robustness and generalization of combining data from multiple communities for personalization purposes.

Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation

Information Retrieval (IR) and Recommender Systems (RS) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution from the frontier. In detail, PDU analyzes the distribution of the points by investigating how far each point is from its utopia point (the ideal performance for the objectives). The possibility of considering fine-grained utopia points allows PDU to select solutions tailored to individual user preferences, a novel feature we call "calibration". We compare PDU against existing state-of-the-art strategies through extensive experiments on tasks from both IR and RS. Experimental results show that PDU and combined with calibration notably impact the solution selection. Furthermore, the results show that the proposed framework selects a solution in a principled way, irrespective of its position on the frontier, thus overcoming the limits of other strategies.

Caching Historical Embeddings in Conversational Search

Rapid response, namely low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. An observation with a potential to reduce latency asserts that conversational queries exhibit a temporal locality in the lists of documents retrieved. Motivated by this observation, we propose and evaluate a client-side document embedding cache, improving the responsiveness of conversational search systems. By leveraging state-of-the-art dense retrieval models to abstract document and query semantics, we cache the embeddings of documents retrieved for a topic introduced in the conversation, as they are likely relevant to successive queries. Our document embedding cache implements an efficient metric index, answering nearest-neighbor similarity queries by estimating the approximate result sets returned. We demonstrate the efficiency achieved using our cache via reproducible experiments based on TREC CAsT datasets, achieving a hit rate of up to 75% without degrading answer quality. Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.

ILMART: Interpretable Ranking with Constrained LambdaMART

Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%.