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- Gianmarco MengaldoSuggested from co-authorship
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Works16 from public data
- Explainable natural language processing for corporate sustainability analysis32
It is argued that Explainable Natural Language Processing (XNLP) can significantly enhance corporate sustainability analysis and linguistic understanding algorithms (lexical, semantic, syntactic), integrated with XAI capabilities (interpretability, explainability, faithfulness), can bridge gaps in analyst resources and mitigate subjectivity problems within data.
- FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis13
This methodology enables an interpretation of the statistical relationship between aspect-based sentiment scores and stock prices, which offers explainability to AI-driven financial decision-making.
- Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding9
It is shown that training on Human Behavior Atlas enables models to consistently outperform existing multimodal LLMs across diverse behavioral tasks; with the targeted use of behavioral descriptors yielding meaningful performance gains.
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- ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis7
ESGSenticNet is constructed from a neurosymbolic framework that integrates specialised concept parsing, GPT-4o inference, and semi-supervised label propagation, together with a hierarchical taxonomy, and does not require any training, possessing a key advantage in its simplicity for non-technical stakeholders.
- OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization4
Omnisapiens-7B 2.0 is developed, a foundation model for social behavior processing that explicitly addresses learning from heterogeneous behavioral data that achieves the best and most consistent performance across 10 behavioral tasks, while also attaining the best performance on all five held-out benchmarks.
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- ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics2
The results show that the approach proposed guides SLMs to output domain-aligned responses, surpassing the performance of task-specific models and offering enhanced real-world applicability for extreme weather analytics.
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- SHALA-LLM: Smartly Handling Ambiguous Labels in Aligning LLMs1
Experiments on ambiguity-sensitive NLI and ER benchmarks, including ChaosNLI, GoEmotions, and MSP-Podcast, demonstrate that SHALA-LLM improves agreement with annotator label distributions, showing that modeling annotator disagreement can also strengthen classification performance.
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- Enhancing Language Models for Robust Greenwashing Detection–
A parameter-efficient framework that structures LLM latent spaces by combining contrastive learning with an ordinal ranking objective to capture graded distinctions between concrete actions and ambiguous claims is proposed.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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