Tiziano Labruna

Tiziano Labruna, Bernardo Magnini

Task-oriented dialogues must maintain consistency both within the dialogue itself, ensuring logical coherence across turns, and with the conversational domain, accurately reflecting external knowledge. We propose to conceptualize dialogue consistency as a Constraint Satisfaction Problem (CSP), wherein variables represent segments of the dialogue referencing the conversational domain, and constraints among variables reflect dialogue properties, including linguistic, conversational, and domain-based aspects. To demonstrate the feasibility of the approach, we utilize a CSP solver to detect inconsistencies in dialogues re-lexicalized by an LLM. Our findings indicate that: (i) CSP is effective to detect dialogue inconsistencies; and (ii) consistent dialogue re-lexicalization is challenging for state-of-the-art LLMs, achieving only a 0.15 accuracy rate when compared to a CSP solver. Furthermore, through an ablation study, we reveal that constraints derived from domain knowledge pose the greatest difficulty in being respected. We argue that CSP captures core properties of dialogue consistency that have been poorly considered by approaches based on component pipelines.

Tiziano Labruna, Simone Gallo, Giovanni Da San Martino

Positional bias in binary question answering occurs when a model systematically favors one choice over another based solely on the ordering of presented options. In this study, we quantify and analyze positional bias across five large language models under varying degrees of answer uncertainty. We re-adapted the SQuAD-it dataset by adding an extra incorrect answer option and then created multiple versions with progressively less context and more out-of-context answers, yielding datasets that range from low to high uncertainty. Additionally, we evaluate two naturally higher-uncertainty benchmarks: (1) WebGPT - question pairs with unequal human-assigned quality scores, and (2) Winning Arguments - where models predict the more persuasive argument in Reddit's r/ChangeMyView exchanges. Across each dataset, the order of the "correct" (or higher-quality/persuasive) option is systematically flipped (first placed in position 1, then in position 2) to compute both Preference Fairness and Position Consistency. We observe that positional bias is nearly absent under low-uncertainty conditions, but grows exponentially when it becomes doubtful to decide which option is correct.

Tiziano Labruna, Sofia Brenna, Andrea Zaninello et al.

Large pre-trained language models have exhibited unprecedented capabilities in producing high-quality text via prompting techniques. This fact introduces new possibilities for data collection and annotation, particularly in situations where such data is scarce, complex to gather, expensive, or even sensitive. In this paper, we explore the potential of these models to generate and annotate goal-oriented dialogues, and conduct an in-depth analysis to evaluate their quality. Our experiments employ ChatGPT, and encompass three categories of goal-oriented dialogues (task-oriented, collaborative, and explanatory), two generation modes (interactive and one-shot), and two languages (English and Italian). Based on extensive human-based evaluations, we demonstrate that the quality of generated dialogues and annotations is on par with those generated by humans.