Kenan Alkiek

Aparna Ananthasubramaniam, Hong Chen, Jason Yan et al. · stanford

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all occurrences of LSM using two types of style: the use of function words and formality. Using this framework, we examine how levels of LSM differ in conversations depending on several social factors within Reddit: post and subreddit features, conversation depth, user tenure, and the controversiality of a comment. Finally, we measure the change of LSM following loss of status after community banning. Our findings reveal the interplay of LSM in Reddit conversations with several community metrics, suggesting the importance of understanding conversation engagement when understanding community dynamics.

Kenan Alkiek, Anna Wegmann, Jian Zhu et al.

Linguistic style is pivotal for understanding how texts convey meaning and fulfill communicative purposes, yet extracting detailed stylistic features at scale remains challenging. We present Neurobiber, a transformer-based system for fast, interpretable style profiling built on Biber's Multidimensional Analysis (MDA). Neurobiber predicts 96 Biber-style features from our open-source BiberPlus library (a Python toolkit that computes stylistic features and provides integrated analytics, e.g., PCA and factor analysis). Despite being up to 56 times faster than existing open source systems, Neurobiber replicates classic MDA insights on the CORE corpus and achieves competitive performance on the PAN 2020 authorship verification task without extensive retraining. Its efficient and interpretable representations readily integrate into downstream NLP pipelines, facilitating large-scale stylometric research, forensic analysis, and real-time text monitoring. All components are made publicly available.

Kenan Alkiek, David Jurgens, Vinod Vydiswaran

Can we bring large-scale reasoning to local-scale compute? Small language models (SLMs) are increasingly attractive because they run efficiently on local hardware, offering strong privacy, low cost, and reduced environmental impact. Yet they often struggle with tasks that require multi-step reasoning or domain-specific knowledge. We address this limitation through instruction intervention at inference time, where an SLM retrieves structured reasoning procedures rather than generating them from scratch. Our method builds an Instruction Corpus by grouping similar training questions and creating instructions via GPT-5. During inference, the SLM retrieves the most relevant instructions and follows their steps. Unlike retrieval-augmented generation, which retrieves text passages, instruction retrieval gives the model structured guidance for reasoning. We evaluate this framework on MedQA (medical board exams), MMLU Professional Law, and MathQA using models from 3B to 14B parameters without any additional fine-tuning. Instruction retrieval yields consistent gains: 9.4% on MedQA, 7.9% on MMLU Law, and 5.1% on MathQA. Concise instructions outperform longer ones, and the magnitude of improvement depends strongly on model family and intrinsic reasoning ability.

Hua Shen, Tiffany Knearem, Reshmi Ghosh et al.

Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes "alignment" limits meaningful progress and cross-disciplinary collaboration. In this position paper, we argue that the research community should explicitly define and critically reflect on "alignment" to account for the bidirectional and dynamic relationship between humans and AI. Through a systematic review of over 400 papers spanning HCI, NLP, ML, and more, we examine how alignment is currently defined and operationalized. Building on this analysis, we introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI -- supporting cognitive, behavioral, and societal adaptation to rapidly advancing AI technologies. Our findings reveal significant gaps in current literature, especially in long-term interaction design, human value modeling, and mutual understanding. We conclude with three central challenges and actionable recommendations to guide future research toward more nuanced, reciprocal, and human-AI alignment approaches.