Empathy plays a crucial role in prosocial behavior and supportive human interactions. According to emotional validation theory, effective empathetic conversations require observing and reflecting on the help-seeker’s situation before offering emotional support and guidance. While recent advancements in large language models (LLMs) have enabled fluent and coherent dialogue generation, our preliminary study reveals that existing LLMs struggle to generate emotional support response. Instead, they tend to offer repetitive solutions without sufficiently considering the emotional needs of help-seekers. To address this limitation, we propose EVA: empathetic LLMs with Emotional VAlidation. EVA enhances empathetic response generation through a two-stage training process: empathy acquisition and emotional validation alignment. For the emotional validation alignment, we introduce the Emotional Validation Aware Dataset (EVAD), which is annotated with levels of emotional validation theory as conversations progress. Additionally, we propose EVAEval, a novel evaluation metric designed to assess whether a model-generated response aligns with emotional validation theory. Experimental results demonstrate that the EVA method significantly improves empathetic response generation, achieving superior performance in both automatic and human evaluations. Furthermore, comprehensive analyses confirm that the EVA method effectively mitigates patterned responses while ensuring adherence to emotional validation principles.

Recent advancements in table-based question answering (table QA) have been driven by the development of table-specific reasoning strategies for leveraging large language models. Previous works employ sub-table-based reasoning, which involves matching query-relevant table values and aggregating them into sub-tables for precise reasoning. However, these approaches are limited to scenarios with query-relevant single tables, failing to handle real-world table QA settings that involve noisy multi-table sets. To address the challenges of real-world table QA, we propose **EASE**: **E**ntity-**A**ware **S**ub-table Generation for R**E**al-world Multi-table QA framework. Given a noisy multi-table set, EASE first extracts key entities from the question to construct a sub-table schema. It then populates this schema by utilizing a selected set of column values from the noisy multi-table set, thereby facilitating efficient and effective sub-table-based reasoning. We introduce a Noisy Multi-table QA dataset and conduct extensive experiments to evaluate EASE’s effectiveness on real-world table QA. Our results demonstrate that EASE effectively filters out irrelevant information while incorporating pertinent table values, leading to efficient and effective performance on real-world table QA. Our dataset can be found https://github.com/Metalchaos8527/ease_noisy_multi-table_qa.git

Despite the striking advances in recent language generation performance, model-generated responses have suffered from the chronic problem of hallucinations that are either untrue or unfaithful to a given source. Especially in the task of knowledge grounded conversation, the models are required to generate informative responses, but hallucinated utterances lead to miscommunication. In particular, entity-level hallucination that causes critical misinformation and undesirable conversation is one of the major concerns. To address this issue, we propose a post-hoc refinement method called REM. It aims to enhance the quality and faithfulness of hallucinated utterances by refining them based on the source knowledge. If the generated utterance has a low source-faithfulness score with the given knowledge, REM mines the key entities in the knowledge and implicitly uses them for refining the utterances. We verify that our method reduces entity hallucination in the utterance. Also, we show the adaptability and efficacy of REM with extensive experiments and generative results. Our code is available at https://github.com/YOONNAJANG/REM.