Entity matching is the task of linking records from different sources that refer to the same real-world entity. Past work has primarily treated entity linking as a standard supervised learning problem. However, supervised entity matching models often do not generalize well to new data, and collecting exhaustive labeled training data is often cost prohibitive. Further, recent efforts have adopted LLMs for this task in few/zero-shot settings, exploiting their general knowledge. But LLMs are prohibitively expensive for performing inference at scale for real-world entity matching tasks.As an efficient alternative, we re-cast entity matching as a conditional generation task as opposed to binary classification. This enables us to “distill” LLM reasoning into smaller entity matching models via natural language explanations. This approach achieves strong performance, especially on out-of-domain generalization tests (10.85% F-1) where standalone generative methods struggle. We perform ablations that highlight the importance of explanations, both for performance and model robustness.

Data deduplication is a critical task in data management and mining, focused on consolidating duplicate records that refer to the same entity. Personally Identifiable Information (PII) is a critical class of data for deduplication across various industries. Consumer data, stored and generated through various engagement channels, is crucial for marketers, agencies, and publishers. However, a major challenge to PII data deduplication is the lack of open-source benchmark datasets due to stringent privacy concerns, which hinders the research, development, and evaluation of robust solutions.This paper addresses this critical lack of PII deduplication benchmarks by introducing the first open-source, high-quality dataset for this task. We provide two datasets: one with 1,000,000 unlabeled synthetic PII profiles and a subset of 10,000 pairs curated and labeled by trained annotators as matches or non-matches. Our datasets contain synthetic profiles built from publicly available sources that do not represent any real individuals, thus ensuring privacy and ethical compliance. We provide several challenging data variations to evaluate the effectiveness of various deduplication techniques, including traditional supervised methods, deep-learning approaches, and large language models (LLMs). Our work aims to set a new standard for PII deduplication, paving the way for more accurate and secure solutions. We share our data publicly at this link - https://zenodo.org/records/13932202.

As large-scale language models become the standard for text generation, there is a greater need to tailor the generations to be more or less concise, targeted, and informative, depending on the audience/application. Existing control approaches primarily adjust the semantic (e.g., emotion, topics), structural (e.g., syntax tree, parts-of-speech), and lexical (e.g., keyword/phrase inclusion) properties of text, but are insufficient to accomplish complex objectives such as pacing which control the complexity and readability of the text. In this paper, we introduce CEV-LM - a lightweight, semi-autoregressive language model that utilizes constrained edit vectors to control three complementary metrics (speed, volume, and circuitousness) that quantify the shape of text (e.g., pacing of content). We study an extensive set of state-of-the-art CTG models and find that CEV-LM provides significantly more targeted and precise control of these three metrics while preserving semantic content, using less training data, and containing fewer parameters.

Adopting contextually appropriate, audience-tailored linguistic styles is critical to the success of user-centric language generation systems (e.g., chatbots, computer-aided writing, dialog systems). While existing approaches demonstrate text style transfer (TST) with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons. First, it is difficult to collect large volumes of audience-specific stylistic data. Second, some stylistic objectives (e.g., persuasiveness, memorability, empathy) are hard to define without audience feedback. In this paper, we propose the novel task of style infusion - infusing the stylistic preferences of audiences in pretrained language generation models. Since humans are better at pairwise comparisons than direct scoring - i.e., is Sample-A more persuasive/polite/empathic than Sample-B - we leverage limited pairwise human judgments to bootstrap a style analysis model and augment our seed set of judgments. We then infuse the learned textual style in a GPT-2 based text generator while balancing fluency and style adoption. With quantitative and qualitative assessments, we show that our infusion approach can generate compelling stylized examples with generic text prompts. We make the anonymized code and data accessible.