Anirudh Sundar, Sinead Williamson, Katherine Metcalf, Barry-John Theobald, Skyler Seto, Masha Fedzechkina. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

We introduce a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning).Inspired by speech processing, these zero-shot tasks measure whether minimal differences in representation can be reliably detected.This offers a flexible and interpretable alternative to probing.Applied to XLM-R (Conneau et al., 2020a) across pretraining checkpoints and layers, we find that language discrimination declines over training and becomes concentrated in lower layers, while meaning discrimination strengthens over time and stabilizes in deeper layers.We then explore probing tasks, showing some alignment between our metrics and linguistic learning performance.Our results position ABX tasks as a lightweight framework for analyzing the structure of multilingual representations.

Preference-based Reinforcement Learning (PbRL) enables non-experts to train Reinforcement Learning models using preference feedback. However, the effort required to collect preference labels from real humans means that PbRL research primarily relies on synthetic labellers. We validate the most common synthetic labelling strategy by comparing against labels collected from a crowd of humans on three Deep Mind Control (DMC) suite tasks: stand, walk, and run. We find that: (1) the synthetic labels are a good proxy for real humans under some circumstances, (2) strong preference label agreement between human and synthetic labels is not necessary for similar policy performance, (3) policy performance is higher at the start of training from human feedback and is higher at the end of training from synthetic feedback, and (4) training on only examples with high levels of inter-annotator agreement does not meaningfully improve policy performance. Our results justify the use of synthetic labellers to develop and ablate PbRL methods, and provide insight into how human labelling changes over the course of policy training.

Generating realistic lip motion from audio to simulate speech production is critical for driving natural character animation. Previous research has shown that traditional metrics used to optimize and assess models for generating lip motion from speech are not a good indicator of subjective opinion of animation quality. Devising metrics that align with subjective opinion first requires understanding what impacts human perception of quality. In this work, we focus on the degree of articulation and run a series of experiments to study how articulation strength impacts human perception of lip motion accompanying speech. Specifically, we study how increasing under-articulated (dampened) and over-articulated (exaggerated) lip motion affects human perception of quality. We examine the impact of articulation strength on human perception when considering only lip motion, where viewers are presented with talking faces represented by landmarks, and in the context of embodied characters, where viewers are presented with photo-realistic videos. Our results show that viewers prefer over-articulated lip motion consistently more than under-articulated lip motion and that this preference generalizes across different speakers and embodiments.

Synthesizing natural head motion to accompany speech for an embodied conversational agent is necessary for pro-viding a rich interactive experience. Most prior works assess the quality of generated head motion by comparing them against a single ground-truth using an objective metric. Yet there are many plausible head motion sequences to accompany a speech utterance. In this work, we study the variation in the perceptual quality of head motions sampled from a generative model. We show that, despite providing more di-verse head motions, the generative model produces motions with varying degrees of perceptual quality. We finally show that objective metrics commonly used in previous research do not accurately reflect the perceptual quality of generated head motions. These results open an interesting avenue for future work to investigate better objective metrics that correlate with human perception of quality.