arXiv subjects

Hui-Ying Shih

Publications and source records attributed to Hui-Ying Shih.

3 recordsLinked to original sources

arXiv2025

Reasoning Beyond Majority Vote: An Explainable SpeechLM Framework for Speech Emotion Recognition

Speech Emotion Recognition (SER) is typically trained and evaluated on majority-voted labels, which simplifies benchmarking but masks subjectivity and provides little transparency into why predictions are made. This neglects valid minority annotations and limits interpretability. We propose an explainable Speech Language Model (SpeechLM) framework that frames SER as a generative reasoning task. Given an utterance, the model first produces a transcript, then outputs both an emotion label and a concise natural-language rationale grounded in lexical and acoustic cues. Rationales are generated by a reasoning-capable teacher LLM and used as intermediate supervision, combined with majority labels during fine-tuning. Unlike prior work primarily focused on boosting classification accuracy, we aim to enhance explainability while preserving competitive performance. To this end, we complement majority-label metrics with annotator-aware scoring that credits matches with any annotator label. On MSP-Podcast v1.12, our model maintains improvements over zero-shot SpeechLM baselines, and produces rationales that human evaluators find plausible and well grounded. This demonstrates that incorporating rationale supervision offers a practical path toward interpretable SER without sacrificing predictive quality.

eess.AS↗

arXiv2025

Explaining Intrinsic Moral Self-Correction with Mechanistic Interpretability

Intrinsic moral self-correction refers to the phenomenon where a language model refines its ethical judgments or aligns its outputs purely through prompting. While effective across diverse tasks, its mechanism remains unclear. We hypothesize intrinsic moral self-correction functions by steering hidden representations along interpretable latent directions. Evaluating six LLMs across four morality-related tasks, we demonstrate that the representation shifts induced by self-correction prompts align with contrastive steering vectors. This alignment transfers even when the steering vectors are constructed from a disjoint corpus. Notably, when applied via activation addition, these prompt-induced shifts can alter model behavior more effectively than the self-correction prompts and the steering vectors. Our findings suggest representation steering is the mechanistic driver of intrinsic moral self-correction.

cs.CL↗

arXiv2024

RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner

The reasoning abilities of large language models (LLMs) have improved with chain-of-thought (CoT) prompting, allowing models to solve complex tasks stepwise. However, training CoT capabilities requires detailed reasoning data, which is often scarce. The self-taught reasoner (STaR) framework addresses this by using reinforcement learning to automatically generate reasoning steps, reducing reliance on human-labeled data. Although STaR and its variants have demonstrated empirical success, a theoretical foundation explaining these improvements is lacking. This work provides a theoretical framework for understanding the effectiveness of reinforcement learning on CoT reasoning and STaR. Our contributions are: (1) criteria for the quality of pre-trained models necessary to initiate effective reasoning improvement; (2) an analysis of policy improvement, showing why LLM reasoning improves iteratively with STaR; (3) conditions for convergence to an optimal reasoning policy; and (4) an examination of STaR's robustness, explaining how it can improve reasoning even when incorporating occasional incorrect steps; This framework aims to bridge empirical findings with theoretical insights, advancing reinforcement learning approaches for reasoning in LLMs.

cs.AI↗