Adaptive retrieval is a technique to overcome the recall limitations of two-stage retrieval pipelines. Adaptive retrieval focuses mainly on effectiveness, but shows potential to improve efficiency. This research focuses on the trade-off between effectiveness and efficiency in adaptive retrieval. We explore the behaviour of neighbourhoods formed by the corpus graph, find that the effectiveness of adaptive retrieval varies across queries, and identify the most effective way to leverage the corpus graph. We investigate the impact of two different scoring mechanisms on the efficiency and effectiveness of an adaptive retrieval pipeline. We observe that score interpolation tends to improve adaptive retrieval's effectiveness, and incorporating an additional cross-encoder stage can lead to further gains. We compare our proposed solutions against several baselines to examine the trade-offs between effectiveness and efficiency. In our setting, we find that adaptive retrieval can improve efficiency at the cost of effectiveness. For our experiments, we introduce a visualisation tool for graph exploration and an adaptive retrieval component for efficient retrieval pipelines.

Numerous techniques have been developed in order to explain the reasoning process of black-box models. Among them is a class of models that are designed to be inherently interpretable: select-then-predict models (a.k.a. rationale-based models). These models are meant to explain their prediction by highlighting part of the input as evidence. The evidence, called the rationale, should consist of the most salient parts of the input text that contribute the most to the model's decision.
However, according to some recent studies, these models are not truly interpretable, because they do not provide faithful explanations (i.e., explanations that accurately reflect the true reasoning process of the model).
In this thesis we give a formal definition of the degree of unfaithfulness to quantify unfaithful behavior. Then, we introduce an experiment to test the faithfulness of select-then-predict models and prove that select-then-predict models can provide unfaithful rationales. Lastly, we introduce a loss function, which we call the unfaithfulness loss, which minimizes the degree of unfaithfulness of select-then-predict models and teaches them to produce more faithful and plausible rationales.

A central problem in information retrieval (IR) is passage ranking, where the task is to retrieve passages from a corpus and order them in decreasing relevance to an arbitrary search query.
Traditional lexical retrieval methods are susceptible to the vocabulary mismatch problem, where relevant passages are overlooked if they do not contain the exact query terms (e.g., synonyms), despite being semantically relevant.
A recent trend in IR is to address this issue by utilizing neural network models (dense rankers) which embed text sequences into dense vector representations that effectively capture their semantics through complex attention mechanisms.
For efficiency, dense rankers are often employed in a retrieve-and-re-rank setting, where a lexical ranker initially retrieves a subset of candidate passages, which are then reordered more accurately by a dense ranker.

In this thesis, we focus on the task of passage re-ranking.
We employ a dual-encoder architecture as re-ranker that employs a two independent query and document encoders, allowing document embeddings to be pre-computed. Dense query-passage similarity is computed as a dot product between their representations.
We then combine scores from both stages using score interpolation.

We identify query encoding latency as a bottleneck and propose an Average Embedding (AvgEmb) estimator. This novel model can efficiently predict an accurate query representation, without requiring any attention-based encoding.
It operates solely on looking up embeddings and computing their weighted average representation.
Our model is distilled from a TCT-ColBERT and achieves 98.6% of its teacher's accuracy while being 13.4X more efficient in query latency and 1.6X better in the full interpolated passage re-ranking pipeline on CPU.

Our code is publicly available on https://github.com/BovdBerg/fast-forward-indexes.

This paper is an analysis of the performance and logic behind different query expansion models. Query expansion and pseudo relevance feedback are techniques for adding more terms to a query based on the results of an initial query and the data in the body of documents. Four different query expansion models that are provided in the pyterrier python library and its extensions have been analysed, namely Bo1, KL, RM3, and Axiomatic query expansion. It was found that Axiomatic query expansion often does not perform any query expansion, and when it does, has no increase in performance. Bo1 and KL, although different in exact logic, have similar results most of the time. The most significant difference is the execution time, with Bo1 being faster with larger datasets and KL being faster with many documents on smaller datasets. Lastly, RM3 while not having a dominant performance has a lot of potential for good results with the right combination or parameters.

Ad-hoc retrieval involves ranking a list of documents from a large collection based on their relevance to a given input query. These retrieval systems often show poorer performances when handling longer and more complex queries. This paper aims to explore methods of improving retrieval effectiveness on these types of queries across different information retrieval (IR) tasks, within the context of Fast-Forward indexes. An analysis is conducted to determine the actual impact of query length and complexity. Interestingly, the hypothesis that longer queries are more challenging does not hold true for all cases, and in some datasets the opposite is true. To improve the performance of long and complex queries, two approaches are explored: utilising multiple dense models during the re-ranking stage instead of the traditional single model and reducing the queries via large language models. The use of multiple dense models for re-ranking proves to be effective, with two models providing the best balance between performance and ranking quality. Utilising LLM's for query reduction achieves performance similar to the original queries but fails to improve their ranking scores.

In the past years, data has become increasingly important to more and more domains, leading to more efficient decision-making. As the amount of collected data grows, there is an increased need for tools that help with various Information Retrieval (IR) tasks. One of the most widespread IR tasks is ad-hoc retrieval which, for a given search query, returns a list of relevant documents from a large corpus ordered by their relevance. Initial Ad-hoc retrieval models were based on term matching, which could not overcome vocabulary mismatch. On one hand, initial strategies aiming to overcome semantic limitations were adopting query expansion, augmenting the initial search query with new terms to capture more relevant documents. On the other hand, newer strategies rely on Natural Language Processing (NLP) for ranking documents by semantic similarity. One such example is retrieve-and-re-rank models, which retrieve documents by their lexical similarity and re-rank the retrieved documents based on semantic similarities, by making use of NLP embedding models. This research focuses on analysing the performance of combining RM3, a pseudo-relevance feedback query expansion strategy, with the semantic re-ranking model TCT-ColBERT. This model is compared with the lexical retrieval model BM25 which serves as a benchmark, as well as with its components RM3 and TCT-ColBERT. Results indicate that on certain tasks, the model performs better (up to 7%), while on other tasks it performs worse (up to 3%).

Efficient and effective information retrieval (IR) systems are needed to fetch a large number of relevant documents and present them based on their relevance to the input queries. Previous work reported the use of sparse and dense retrievers. Sparse retrievers offer low latency but suffer from term mismatch issues, while dense retrievers improve performance at the cost of higher processing times. The literature proposed Fast-Forward Indexes, an interpolation-based re-ranking framework that leverages the benefits of both sparse and dense retrievers.

Although a lot of work was done in the field, most studies evaluate the performance of the proposed models only on the MS Marco dataset, neglecting other datasets. This study extends previous work by exploring how different sparse retrievers, employing no-encoder, uni-encoder, and bi-encoder architectures, perform in an interpolation-based re-ranking setting on datasets originating from various domains. Results show that bi-encoder-based retrievers outperform the other sparse retrievers in terms of recall but with a substantial increase in latency compared to simpler retrievers, which generally showed good performance. Further, when the retrievers were used in an interpolation-based re-ranking setting, they performed similarly in terms of ranking quality.

The crucial role of information retrieval (IR) is highlighted by its presence across a wide range of tasks, such as web search and fact-checking, and domains, including finance and healthcare. Effective and efficient IR systems are critical for finding relevant information from vast amounts of data. Traditional sparse retrieval methods such as BM25 are efficient but often fail to capture the context, while more recent dense retrieval models are highly inefficient in terms of resources and latency.
In our research, we evaluate multiple Transformer-based models to understand the impact of the semantic re-ranking phase within interpolation-based re-ranking, using the FAST-FORWARD indexes framework, a retrieve-and-re-rank approach which combines the benefits of both lexical and semantic matching. We focused on identifying specific scenarios in which particular models excel in terms of ranking performance or latency, aiming to provide model recommendations tailored to different settings. Our evaluations reveal that no single model outperforms others across all datasets. We hypothesise that the main factors influencing encoder performance are the datasets used for finetuning and the method employed for computing the contextualised vector embedding. However, ablations studies would be beneficial for validating these observations.

The research explores the impact of rank fusion functions within the retrieve-and-rerank framework with Fast-Forward Indexes. Using the BM25 sparse model for retrieval and TCT-ColBERT dense model for semantic score computation, various rank fusion functions are experimented for the interpolation stage. The interpolated rank in relation to the semantic and lexical ranks is explored. Parametric approach allows to easily adjust the influence of sparse and dense models on the final rank. On the other hand, non-parametric approach lacks flexibility and maintains an equal weight of sparse and dense scores by default. Moreover, the ranking effectiveness and latency are measured to further evaluate each function. Due to flexibility of parametric functions, convex rank fusion function and its normalized variants yield the best trade-off in latency and ranking effectiveness followed by reciprocal rank fusion. On the contrary, non-parametric functions, namely Inverse Square Rank Reciprocal, combMNZ, and Condorcet Fuse, generally performs worse.

Passage re-ranking is a fundamental problem in information retrieval, which deals with reordering a small set of passages based on their relevancy to a query. It is a crucial component in various web information systems, such as search engines or question-answering systems. Modern approaches for building re-ranking systems rely on neural language models such as BERT, or its derivatives, to create dense indexes for the target document corpus. While such approaches bring significant performance gains compared to classical lexical re-rankers, they have the disadvantage of increased memory costs.

A family of methods that can be used to reduce the memory footprint of a dense index is called vector quantization. Vector quantization algorithms usually rely on a combination of clustering and space manipulation operations to perform a lossy compression of the dense index at the expense of index performance. While vector quantization is widely used for first-stage retrieval, its use in the context of re-ranking is underexplored. To this end, this thesis evaluates the effectiveness of product quantization, a well-known vector quantization method, on single-vector dual-encoders, specifically TCT-ColBERT and Aggretriever. In addition to this, we show how linear interpolation of sparse scores can be leveraged to improve the performance of quantized dense indices with negligible costs to the memory footprint or speed. Last but not least, we propose WolfPQ, a learnable quantization method aimed at further improving quantization for re-ranking by bridging the gap between the objective functions used in training product quantization and re-ranking systems, respectively.