Abhijnan Nath

Abhijnan Nath, Carine Graff, Andrei Bachinin et al.

AI support of collaborative interactions entails mediating potential misalignment between interlocutor beliefs. Common preference alignment methods like DPO excel in static settings, but struggle in dynamic collaborative tasks where the explicit signals of interlocutor beliefs are sparse and skewed. We propose the Frictional Agent Alignment Framework (FAAF), to generate precise, context-aware "friction" that prompts for deliberation and re-examination of existing evidence. FAAF's two-player objective decouples from data skew: a frictive-state policy identifies belief misalignments, while an intervention policy crafts collaborator-preferred responses. We derive an analytical solution to this objective, enabling training a single policy via a simple supervised loss. Experiments on three benchmarks show FAAF outperforms competitors in producing concise, interpretable friction and in OOD generalization. By aligning LLMs to act as adaptive "thought partners" -- not passive responders -- FAAF advances scalable, dynamic human-AI collaboration. Our code and data can be found at https://github.com/csu-signal/FAAF_ACL.

Shafiuddin Rehan Ahmed, Abhijnan Nath, James H. Martin et al.

Event Coreference Resolution (ECR) is the task of linking mentions of the same event either within or across documents. Most mention pairs are not coreferent, yet many that are coreferent can be identified through simple techniques such as lemma matching of the event triggers or the sentences in which they appear. Existing methods for training coreference systems sample from a largely skewed distribution, making it difficult for the algorithm to learn coreference beyond surface matching. Additionally, these methods are intractable because of the quadratic operations needed. To address these challenges, we break the problem of ECR into two parts: a) a heuristic to efficiently filter out a large number of non-coreferent pairs, and b) a training approach on a balanced set of coreferent and non-coreferent mention pairs. By following this approach, we show that we get comparable results to the state of the art on two popular ECR datasets while significantly reducing compute requirements. We also analyze the mention pairs that are "hard" to accurately classify as coreferent or non-coreferent. Code at https://github.com/ahmeshaf/lemma_ce_coref

Abhijnan Nath, Carine Graff, Nikhil Krishnaswamy

As Large Language Models (LLMs) integrate into diverse workflows, they are increasingly being considered "collaborators" with humans. If such AI collaborators are to be reliable, their behavior over multiturn interactions must be predictable, validated and verified before deployment. Common alignment techniques are typically developed under simplified single-user settings and do not account for the dynamics of long-horizon multiparty interactions. This paper examines how different alignment methods affect LLM agents' effectiveness as partners in multiturn, multiparty collaborations. We study this question through the lens of friction agents that intervene in group dialogues to encourage the collaborative group to slow down and reflect upon their reasoning for deliberative decision-making. Using a roleplay methodology, we evaluate interventions from differently-trained friction agents in collaborative task conversations. We propose a novel counterfactual evaluation framework that quantifies how friction interventions change the trajectory of group collaboration and belief alignment. Our results show that a friction-aware approach significantly outperforms common alignment baselines in helping both convergence to a common ground, or agreed-upon task-relevant propositions, and correctness of task outcomes.