Hardy

Hardy, Sebastian Padó, Amelie Wührl et al.

News Recommender Systems (NRS) shape what users read, whose perspectives they encounter, and influence public discourse. Yet their design is value-laden: intentionally or not, NRS can embed undesired values in recommendation procedures, such as excluding underrepresented voices or favoring certain viewpoints, which may conflict with democratic goals. Existing solutions also lack mechanisms to explicitly control these values. Therefore, we introduce an approach that parameterizes NRS to support different democratic goals. We propose Aspect-Aware Candidate Generation (A2CG), a normatively configurable procedure for the candidate generation stage of NRS that allows designers to shape diversity in recommendations. Unlike prior work that only re-ranks candidates, A2CG introduces diversity at the start of the recommendation pipeline. A2CG represents articles along multiple diversity aspects: sentiment, political leaning, topic, and media framing. User interests are encoded using a Vector Quantized VAE, while a decoder-only model predicts the next article aspects users are likely to engage with. To broaden exposure to perspectives, A2CG injects diversity during retrieval by selectively flipping aspects in the predicted query, allowing candidate diversity to be tuned toward specific democratic models. Our method enables normative configurations that existing NRS cannot express. Unlike baselines with fixed structural biases, A2CG supports continuous calibration between democratic ideals without retraining. Empirically, A2CG generates novel, diverse, and serendipitous candidates while providing explicit parameter-driven control over the trade-off between personalization and democratic alignment. Rather than aiming for pointwise superiority, A2CG's main contribution lies in its controllability and ability to express flexible normative configurations.

Hardy, Sebastian Padó

Large Language Models (LLMs) achieve strong linguistic performance, yet their internal mechanisms for producing these predictions remain unclear. We investigate the hypothesis that LLMs encode representations of linguistic constraint violations within their parameters, which are selectively activated when processing ungrammatical sentences. To test this, we use sparse autoencoders to decompose polysemantic activations into sparse, monosemantic features and recover candidates for violation-related features. We introduce a sensitivity score for identifying features that are preferentially activated on constraint-violated versus well-formed inputs, enabling unsupervised detection of potential violation-specific features. We further propose a conjunctive falsification framework with three criteria evaluated jointly. Overall, the results are negative in two respects: (1) the falsification criteria are not jointly satisfied across linguistic phenomena, and (2) no features are consistently shared across all categories. While some phenomena show partial evidence of selective causal structure, the overall pattern provides limited support for a unified set of grammatical violation detectors in current LMs.

Hardy, Andreas Vlachos

Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which we guide using the source document. We demonstrate that this guidance improves summarization results by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. We also find that the summarization performance using the latter is 2 ROUGE-2 points higher than that of a well-established neural encoder-decoder approach trained on a larger dataset. Code is available at \url{https://github.com/sheffieldnlp/AMR2Text-summ}

Hardy, Shashi Narayan, Andreas Vlachos

There has been substantial progress in summarization research enabled by the availability of novel, often large-scale, datasets and recent advances on neural network-based approaches. However, manual evaluation of the system generated summaries is inconsistent due to the difficulty the task poses to human non-expert readers. To address this issue, we propose a novel approach for manual evaluation, Highlight-based Reference-less Evaluation of Summarization (HighRES), in which summaries are assessed by multiple annotators against the source document via manually highlighted salient content in the latter. Thus summary assessment on the source document by human judges is facilitated, while the highlights can be used for evaluating multiple systems. To validate our approach we employ crowd-workers to augment with highlights a recently proposed dataset and compare two state-of-the-art systems. We demonstrate that HighRES improves inter-annotator agreement in comparison to using the source document directly, while they help emphasize differences among systems that would be ignored under other evaluation approaches.