Emotionally Aware or Tone-Deaf? Evaluating Emotional Alignment in LLM -Based Conversational Recommendation Systems (2025)

Abstract: Recent advances in Large Language Models (LLMs) have enhanced the fluency and coherence of Conversational Recommendation Systems (CRSs), yet emotional intelligence remains a critical gap. In this study, we systematically evaluate the emotional behavior of six state-of-the-art LLMs in CRS settings using the ReDial and INSPIRED datasets. We propose an emotion-aware evaluation framework incorporating metrics such as Emotion Alignment, Emotion Flatness, and per-emotion F1-scores. Our analysis shows that most models frequently default to emotionally flat or mismatched responses, often misaligning with user affect (e.g., joy misread as neutral). We further examine patterns of emotional misalignment and their impact on user-centric qualities such as personalization, justification, and satisfaction. Through qualitative analysis, we demonstrate that emotionally aligned responses enhance user experience, while misalignments lead to loss of trust and relevance. This work highlights the need for emotion-aware design in CRS and provides actionable insights for improving affective sensitivity in LLM-generated recommendations.

## introduction conversational recommendation systems (crss) aim to provide personalized recommendations through interactive dialogue @xcite , but often lack emotional intelligence-the ability to understand and respond to user emotions. while llms have improved crs fluency and contextual relevance @xcite , their affective awareness remains underexplored. emotional alignment is vital for user trust and satisfaction @xcite , yet llmgenerated responses frequently exhibit emotional flatness or mismatches @xcite , reducing conversational quality. prior work has focused on intent and personalization @xcite , with limited attention to emotional grounding. with the advent of llms, recent crs architectures have adopted generative paradigms @xcite . while systems like chatcrs @xcite improve task goal guidance in llm-based crs, they do not explicitly evaluate emotional alignment in response generation. to address this gap, we conduct a systematic study of emotional behavior in llm-generated crs responses. we investigate how well llms align their emotional tone with that of the user, and whether their responses demonstrate sufficient emotional variability across conversation turns. our contributions are threefold: @xcite we propose an evaluation framework for emotional alignment, flatness, and diversity in crs; (2) we apply it across six llms on redial and inspired to systematically assess affective behavior; (3) we analyze misalignment cases and their impact on personalization, justification, and user satisfaction. ## related work emotion modeling in dialogue systems has gained importance with the rise of llms, yet remains underexplored in crss. early crss used modular pipelines with template-based responses @xcite , while recent llm-based systems like chatcrs @xcite @xcite offer greater fluency but often rely on synthetic supervision and shallow emotion alignment, and thus still lack deep emotional grounding. in open-domain dialogue, datasets like empatheticdialogues @xcite and models such as moel @xcite , mime @xcite , and empdg @xcite emphasize generating affective responses, yet such approaches are rarely applied in crs settings. recent studies reveal that even advanced llms struggle with emotional alignment and flatness @xcite , especially in task-oriented contexts. our work builds on these insights by evaluating emotional alignment and flatness in llm-generated crs responses using the redial dataset, addressing a critical gap in affect-aware recommendation dialogue research. ## conversational recommendation task definition we define a conversational recommendation system (crs) as a dialogue agent that interacts naturally with users and provides recommendations during conversation. formally, given multi-type context data-including the conversation history ## methodology we propose an evaluation framework to assess emotional intelligence in llm-based crs responses, focusing on alignment, flatness, and diversity. all user, ground truth, and model-generated utterances are annotated using a transformer-based classifier fine-tuned on goemotions @xcite and empatheticdialogues @xcite , assigning one of seven emotion labels: joy, sadness, anger, fear, surprise, disgust, or neutral based on ekman's taxonomy @xcite . the reliability of these annotations was confirmed via manual verification on a random subset of utterances, showing substantial agreement with the programmatically assigned labels. to assess the emotional behavior of llms in crs settings, we conduct the following evaluations: ## datasets and experimental setup to evaluate the emotional and conversational capabilities of llms in a crs context, we describe the task, datasets, model integration, and inference settings used in our experiments. ## emotional performance analysis of llms in crs we systematically evaluate the emotional intelligence of llm-generated crs responses across five key questions, focusing on alignment, diversity, flatness, misalignment, and impact on response quality. we use the ground-truth responses from redial and inspired as human baselines to contextualize llm performance. exact numerical values for these baselines are not reported, as our focus is on comparing relative trends across models. nonetheless, they represent realistic human conversational behavior, allowing qualitative interpretation of alignment, diversity, and flatness. the reliability of the emotion labels was confirmed via manual verification on a random subset of 150 user utterances from redial and inspired, showing substantial agreement with the programmatically assigned labels (@xmath0.79, macro-@xmath1.77), ensuring the annotations are suitable for analysis. our analysis assesses affective sensitivity and expressive range. to assess the emotional sensitivity of llms in conversational recommendation, we compute emotion alignment accuracy (equation 1 ) using two complementary criteria: exact match, requiring a direct match between the user's and model's emotions, and coarse match, which categorizes emotions into broader affective groups (positive, negative, neutral). these metrics quantify the extent to which model responses appropriately reflect the user's emotional state. as shown in figure 1 (page 4), mistral achieves the highest coarse emotion alignment, followed by llama 3.2 and gemini, with qwen performing moderately. flat emotional expression can make conversations feel dull, robotic, and disconnected. to assess this, we compute the emotion flatness score (equation 2), based on shannon entropy. higher scores indicate richer affective variation, while lower scores suggest emotional flatness-often due to repetitive or default use of neutral. this metric reveals whether models sustain affective dynamics or lapse into monotonous tones. as shown in ## limitations this work focuses on evaluating emotional alignment in llm-generated crs responses using automatic analyses. human evaluation of user experience has not been conducted yet and is left for future studies. ## conclusion this work presents an emotion-aware evaluation framework for analyzing llm responses in conversational recommendation settings. by annotating model and user utterances with emotion labels, we assessed emotional alignment, diversity, and misalignment patterns across redial and in-spired datasets. our findings reveal that while some models demonstrate moderate emotional sensitivity, many tend to default to neutral responses, resulting in flat and affectively misaligned conversations. these results underscore the need for integrating emotional intelligence into crss to foster more engaging and empathetic user experiences. i will provide you with a user input that contains some sort of chit-chat or question. i want you to generate an output text that incorporates a sort of chit chat and then followed by some question related to movies, actors, genres etc. example 1: user input: "hi, how are you?" output: "hi! i'm doing well. what kind of movies are you looking for?" now, do a similar task for the given user input. a.2 case 2: recommendation available i will provide you with a user input that contains some movie names, actor names, cast, directors, genre, etc. additionally, i will provide you with a recommendation that is relevant to the input. i want you to generate an output text that incorporates both the information from the user input and the recommendation. example 1: user input: "i really liked avengers and spiderman. they are both thrillers and tom holland featured in both of them. released in 2012 directed by tarantino." related attributes: "thor, chris hemsworth." output: "you can watch thor. it stars chris hemsworth and is similar to the avengers." example 2: if user recommendation is empty then ask the user a relevant question about their likings regarding genres, casts etc and engage with the user. example 3: if the user input is present and some ambiguity is present regarding the recommendation generated then clarify it with the user by asking more specific questions regarding the cast, year of release etc. now, do a similar task for the given user input and recommendation.