While extensive work has examined the explicit and implicit biases in large language models (LLMs), little research explores the relation between these two types of biases. This paper presents a comparative study of the explicit and implicit biases in LLMs grounded in social psychology. Social psychology distinguishes between explicit and implicit biases by whether the bias can be self-recognized by individuals. Aligning with this conceptualization, we propose a self-evaluation-based two-stage measurement of explicit and implicit biases within LLMs. First, the LLM is prompted to automatically fill templates with social targets to measure implicit bias toward these targets, where the bias is less likely to be self-recognized by the LLM. Then, the LLM is prompted to self-evaluate the templates filled by itself to measure explicit bias toward the same targets, where the bias is more likely to be self-recognized by the LLM. Experiments conducted on state-of-the-art LLMs reveal human-like inconsistency between explicit and implicit occupational gender biases. This work bridges a critical gap where prior studies concentrate solely on either explicit or implicit bias. We advocate that future work highlight the relation between explicit and implicit biases in LLMs.

Emotion recognition in conversation (ERC) is a field that aims to classify the emotion of each utterance within conversational contexts. This presents significant challenges, particularly in handling emotional ambiguity across various speakers and contextual factors. Existing ERC approaches have primarily focused on modeling conversational contexts while incorporating only superficial speaker attributes such as names, memories, and interactions. Recent works introduce personality as an essential deep speaker factor for emotion recognition, but relies on static personality, overlooking dynamic variability during conversations. Advances in personality psychology conceptualize personality as dynamic, proposing that personality states can change across situations. In this paper, we introduce ERC-DP, a novel model considering the dynamic personality of speakers during conversations. ERC-DP accounts for past utterances from the same speaker as situation impacting dynamic personality. It combines personality modeling with prompt design and fine-grained classification modules. Through a series of comprehensive experiments, ERC-DP demonstrates superior performance on three benchmark conversational datasets.