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Although generically expressing empathy is straightforward, effectively conveying empathy in specialized settings presents nuanced challenges. We present a conceptually motivated investigation into the use of figurative language and causal semantic context to facilitate targeted empathetic response generation within a specific mental health support domain, studying how these factors may be leveraged to promote improved response quality. Our approach achieves a 7.6% improvement in BLEU, a 36.7% reduction in Perplexity, and a 7.6% increase in lexical diversity (D-1 and D-2) compared to models without these signals, and human assessments show a 24.2% increase in empathy ratings. These findings provide deeper insights into grounded empathy understanding and response generation, offering a foundation for future research in this area. 

Recent research highlights the importance of figurative language as a tool for amplifying emotional impact. In this paper, we dive deeper into this phenomenon and outline our methods for Track 1, Empathy Prediction in Conversations (CONV-dialog) and Track 2, Empathy and Emotion Prediction in Conversation Turns (CONV-turn) of the WASSA 2024 shared task. We leveraged transformer-based large language models augmented with figurative language prompts, specifically idioms, metaphors and hyperbole, that were selected and trained for each track to optimize system performance. For Track 1, we observed that a fine-tuned BERT with metaphor and hyperbole features outperformed other models on the development set. For Track 2, DeBERTa, with different combinations of figurative language prompts, performed well for different prediction tasks. Our method provides a novel framework for understanding how figurative language influences emotional perception in conversational contexts. Our system officially ranked 4th in the 1st track and 3rd in the 2nd track. 

A bstract Hidden sectors are ubiquitous in supergravity theories, in strings and in branes. Well motivated models such as the Stueckelberg hidden sector model could provide a candidate for dark matter. In such models, the hidden sector communicates with the visible sector via the exchange of a dark photon (dark Z ′) while dark matter is constituted of Dirac fermions in the hidden sector. Using data from collider searches and precision measurements of SM processes as well as the most recent limits from dark matter direct and indirect detection experiments, we perform a comprehensive scan over a wide range of the Z ′ mass and set exclusion bounds on the parameter space from sub-GeV to several TeV. We then discuss the discovery potential of an $$ \mathcal{O} $$ O (TeV) scale Z ′ at HL-LHC and the ability of future forward detectors to probe very weakly interacting sub-GeV Z ′ bosons. Our analysis shows that the parameter space in which a Z ′ can decay to hidden sector dark matter is severely constrained whereas limits become much weaker for a Z ′ with no dark decays. The analysis also favors a self-thermalized dark sector which is necessary to satisfy the dark matter relic density.