Entity matching (EM) is a critical step in entity resolution (ER). Recently, entity matching based on large language models (LLMs) has shown great promise. However, current LLM-based entity matching approaches typically follow a binary matching paradigm that ignores the global consistency among record relationships. In this paper, we investigate various methodologies for LLM-based entity matching that incorporate record interactions from different perspectives. Specifically, we comprehensively compare three representative strategies: matching, comparing, and selecting, and analyze their respective advantages and challenges in diverse scenarios. Based on our findings, we further design a compound entity matching framework (ComEM) that leverages the composition of multiple strategies and LLMs. ComEM benefits from the advantages of different sides and achieves improvements in both effectiveness and efficiency. Experimental results on 8 ER datasets and 10 LLMs verify the superiority of incorporating record interactions through the selecting strategy, as well as the further cost-effectiveness brought by ComEM.

Open-Domain Question Answering (ODQA) systems often struggle with the quality of retrieved passages, which may contain conflicting information and be misaligned with the reader’s needs. Existing retrieval methods aim to gather relevant passages but often fail to prioritize consistent and useful information for the reader. In this paper, we introduce a novel Reader-Centered Passage Selection (R-CPS) method, which enhances the performance of the retrieve-then-read pipeline by re-ranking and clustering passages from the reader’s perspective. Our method re-ranks passages based on the reader’s prediction probability distribution and clusters passages according to the predicted answers, prioritizing more useful and relevant passages to the top and reducing inconsistent information. Experiments on ODQA datasets demonstrate the effectiveness of our approach in improving the quality of evidence passages under zero-shot settings.

In argumentation, the claim is the foundational proposition that underpins the argument, serving as the central pillar upon which the argument is constructed. It guides the subsequent presentation of evidence, reasoning, and analysis, thereby facilitating the audience’s understanding of the core issue. Therefore, ensuring that the claim is precise and unambiguous is crucial for constructing a coherent and persuasive argument. While Large Language Models (LLMs) have demonstrated proficiency in text rewriting tasks such as style transfer and query rewriting, their application to claim optimization remains unexplored. Unlike other rewriting tasks, claim clarification requires the model to rewrite ambiguous or unclear segments of the claim, enhance the content by adding omitted key details, and eliminate redundant or verbose elements. Addressing this gap, this paper evaluates the performance of LLMs on the claim clarification task across various settings. While popular rewriting evaluation methods such as BLEU and Rouge rely on exact word matching, this paper introduces a novel semantic evaluation approach based on a sliding window mechanism. Three distinct LLMs, including LLama2, Mistral, and Qwen2, are assessed for their ability to clarify arguments through zero-shot or few-shot prompting, and supervised fine-tuning (SFT). Additionally, we propose a reinforcement learning-based clarification approach that optimally balances content preservation with claim clarity, thereby augmenting the performance of LLMs on the claim clarification task.

Pairwise Ranking Prompting (PRP) demonstrates impressive effectiveness in zero-shot document re-ranking tasks with large language models (LLMs). However, in the existing methods, PRP only outputs the same label for the comparison results of different confidence intervals without considering the uncertainty of pairwise comparison, which implies an underutilization of the generation probability information of LLMs. To bridge this gap, we propose PRP-Graph, a novel pairwise re-ranking approach, based on a refined scoring PRP unit that exploits the output probabilities of target labels to capture the degree of certainty of the comparison results. Specifically, the PRP-Graph consists of two stages, namely ranking graph construction and ranking graph aggregation. Extensive experiments conducted on the BEIR benchmark demonstrate the superiority of our approach over existing PRP-based methods. Comprehensive analysis reveals that the PRP-Graph displays strong robustness towards the initial ranking order and delivers exceptional re-ranking results with acceptable efficiency. Our code and data are available at https://github.com/Memelank/PRP-Graph.

Document logical structuring aims to extract the underlying hierarchical structure of documents, which is crucial for document intelligence. Traditional approaches often fall short in handling the complexity and the variability of lengthy documents. To address these issues, we introduce Seg2Act, an end-to-end, generation-based method for document logical structuring, revisiting logical structure extraction as an action generation task. Specifically, given the text segments of a document, Seg2Act iteratively generates the action sequence via a global context-aware generative model, and simultaneously updates its global context and current logical structure based on the generated actions. Experiments on ChCatExt and HierDoc datasets demonstrate the superior performance of Seg2Act in both supervised and transfer learning settings.

Query expansion (QE) is a critical component in the open-domain question answering (OpenQA) pipeline, enhancing the retrieval performance by broadening the scope of queries with additional relevant texts. However, existing methods like GAR and EAR rely heavily on supervised training and often struggle to maintain effectiveness across domains and datasets. Meanwhile, although large language models (LLMs) have demonstrated QE capability for information retrieval (IR) tasks, their application in OpenQA is hindered by the inadequate analysis of query’s informational needs and the lack of quality control for generated QEs, failing to meet the unique requirements of OpenQA. To bridge this gap, we propose a novel LLM-based QE approach named AGR for the OpenQA task, leveraging a three-step prompting strategy. AGR begins with an analysis of the query, followed by the generation of answer-oriented expansions, and culminates with a refinement process for better query formulation. Extensive experiments on four OpenQA datasets reveal that AGR not only rivals in-domain supervised methods in retrieval accuracy, but also outperforms state-of-the-art baselines in out-domain zero-shot scenarios. Moreover, it exhibits enhanced performance in end-to-end QA evaluations, underscoring the superiority of AGR for OpenQA.

Recently, there has been an increased emphasis on assessing the quality of natural language arguments. Existing approaches primarily focus on evaluating the quality of individual argument posts. However, they often fall short when it comes to effectively distinguishing arguments that possess a narrow quality margin. To address this limitation, this paper delves into two alternative methods for modeling the relative quality of different arguments. These approaches include: 1) Supervised contrastive learning that captures the intricate interactions between arguments. By incorporating this approach, we aim to enhance the assessment of argument quality by effectively distinguishing between arguments with subtle differences in quality. 2) Large language models (LLMs) with in-context examples that harness the power of LLMs and enrich them with in-context examples. Through extensive evaluation and analysis on the publicly available IBM-Rank-30k dataset, we demonstrate the superiority of our contrastive argument quality assessment approach over state-of-the-art baselines. On the other hand, while LLMs with in-context examples showcase a commendable ability to identify high-quality argument posts, they exhibit relatively limited efficacy in discerning between argument posts with a narrow quality gap.

Adversarial attacks have gained traction in order to identify vulnerabilities in neural ranking models (NRMs), but current attack methods often introduce noticeable errors. Moreover, current methods rely heavily on using a well-imitated surrogate NRM to guarantee the attack effect, making them difficult to use in practice. This paper proposes a framework called Imperceptible DocumEnt Manipulation (IDEM) to produce adversarial documents that are less noticeable to both algorithms and humans. IDEM instructs a well-established generative language model like BART to generate error-free connection sentences, and employs a separate position-wise merging strategy to balance between relevance and coherence of the perturbed text. Evaluation results on the MS MARCO benchmark demonstrate that IDEM outperforms strong baselines while preserving fluency and correctness of the target documents. Furthermore, the separation of adversarial text generation from the surrogate NRM makes IDEM more robust and less affected by the quality of the surrogate NRM.