Here is today's selection of cs.CL papers. The common themes include enhancing model understanding and addressing biases in language processing tasks. These papers explore innovative methods such as adaptive techniques for improving language-specific question answering and novel approaches to evaluating and aligning language models.

Penalizing Length: Uncovering Systematic Bias in Quality Estimation Metrics

Quality Estimation (QE) metrics are vital in machine translation for reference-free evaluation and as a reward signal in tasks like reinforcement learning. However, the prevalence and impact of length bias in QE have been underexplored. Through a systematic study of top-performing regression-based and LLM-as-a-Judge QE metrics across 10 diverse language pairs, we reveal two critical length biases: First, QE metrics consistently over-predict errors with increasing translation length, even for high-quality, error-free texts. Second, they exhibit a preference for shorter translations when multiple candidates are available for the same source text. These inherent length biases risk unfairly penalizing longer, correct translations and can lead to sub-optimal decision-making in applications such as QE reranking and QE guided reinforcement learning. To mitigate this, we propose two strategies: (a) applying length normalization during model training, and (b) incorporating reference texts during evaluation. Both approaches were found to effectively reduce the identified length bias.

Typoglycemia under the Hood: Investigating Language Models' Understanding of Scrambled Words

Research in linguistics has shown that humans can read words with internally scrambled letters, a phenomenon recently dubbed typoglycemia. Some specific NLP models have recently been proposed that similarly demonstrate robustness to such distortions by ignoring the internal order of characters by design. This raises a fundamental question: how can models perform well when many distinct words (e.g., form and from) collapse into identical representations under typoglycemia? Our work, focusing exclusively on the English language, seeks to shed light on the underlying aspects responsible for this robustness. We hypothesize that the main reasons have to do with the fact that (i) relatively few English words collapse under typoglycemia, and that (ii) collapsed words tend to occur in contexts so distinct that disambiguation becomes trivial. In our analysis, we (i) analyze the British National Corpus to quantify word collapse and ambiguity under typoglycemia, (ii) evaluate BERT's ability to disambiguate collapsing forms, and (iii) conduct a probing experiment by comparing variants of BERT trained from scratch on clean versus typoglycemic Wikipedia text; our results reveal that the performance degradation caused by scrambling is smaller than expected.

Document Understanding, Measurement, and Manipulation Using Category Theory

We apply category theory to extract multimodal document structure which leads us to develop information theoretic measures, content summarization and extension, and self-supervised improvement of large pretrained models. We first develop a mathematical representation of a document as a category of question-answer pairs. Second, we develop an orthogonalization procedure to divide the information contained in one or more documents into non-overlapping pieces. The structures extracted in the first and second steps lead us to develop methods to measure and enumerate the information contained in a document. We also build on those steps to develop new summarization techniques, as well as to develop a solution to a new problem viz. exegesis resulting in an extension of the original document. Our question-answer pair methodology enables a novel rate distortion analysis of summarization techniques. We implement our techniques using large pretrained models, and we propose a multimodal extension of our overall mathematical framework. Finally, we develop a novel self-supervised method using RLVR to improve large pretrained models using consistency constraints such as composability and closure under certain operations that stem naturally from our category theoretic framework.

Bridging Language Gaps with Adaptive RAG: Improving Indonesian Language Question Answering

Question Answering (QA) has seen significant improvements with the advancement of machine learning models, further studies enhanced this question answering system by retrieving external information, called Retrieval-Augmented Generation (RAG) to produce more accurate and informative answers. However, these state-of-the-art-performance is predominantly in English language. To address this gap we made an effort of bridging language gaps by incorporating Adaptive RAG system to Indonesian language. Adaptive RAG system integrates a classifier whose task is to distinguish the question complexity, which in turn determines the strategy for answering the question. To overcome the limited availability of Indonesian language dataset, our study employs machine translation as data augmentation approach. Experiments show reliable question complexity classifier; however, we observed significant inconsistencies in multi-retrieval answering strategy which negatively impacted the overall evaluation when this strategy was applied. These findings highlight both the promise and challenges of question answering in low-resource language suggesting directions for future improvement.

Brain-tuning Improves Generalizability and Efficiency of Brain Alignment in Speech Models

Pretrained language models are remarkably effective in aligning with human brain responses elicited by natural language stimuli, positioning them as promising model organisms for studying language processing in the brain. However, existing approaches for both estimating and improving this brain alignment are participant-dependent and highly affected by the amount of data available per participant, hindering both generalization to new participants and population-level analyses. In this work, we address these limitations by introducing a scalable, generalizable brain-tuning method, in which we fine-tune pretrained speech language models to jointly predict fMRI responses from multiple participants. We demonstrate that the resulting brain-tuned models exhibit strong individual brain alignment while generalizing across participants. Specifically, our method leads to 1) a 5-fold decrease in the amount of fMRI data needed to predict brain data from new participants, 2) up to a 50% increase in the overall brain alignment, and 3) strong generalization to new unseen datasets. Furthermore, this multi-participant brain-tuning additionally improves downstream performance on semantic tasks, suggesting that training using brain data from multiple participants leads to more generalizable semantic representations. Taken together, these findings demonstrate a bidirectional benefit between neuroscience and AI, helping bridge the gap between the two fields. We make our code and models publicly available at https://github.com/bridge-ai-neuro/multi-brain-tuning.