The paper introduces a method for forecasting conversational derailment by incorporating speech act information as an auxiliary signal to enhance pragmatic representations. This approach aims to reduce lexical noise and improve generalizability, especially in low-data and cross-domain scenarios. Experiments on three datasets demonstrate performance gains over existing methods.
By Angela Yifei Yuan, Christine De Kock, Christopher Leckie
Conversational derailment forecasting aims to predict when online discussions will escalate into hostility, enabling proactive moderation. Existing approaches often struggle in low-data settings and t...
The paper introduces a benchmark for topic matching in real-world ASR transcripts from contact centers, where noisy, punctuation‑free speech data must be classified into predefined topics. It presents a human‑annotated dataset of topic‑utterance judgments and evaluates three matcher types—regex, zero‑shot sentence embeddings, and Gemini‑based LLMs—using two topic representations: keyphrases and natural language descriptions. Experiments show that lightweight LLM matchers outperform the other methods, especially when natural language descriptions are used.
By Saman Rahbar, Xiliang Zhu, Irvin Cardoza, David Rossouw
arXiv:2606.01697v3 Announce Type: replace
Abstract: We propose RCEM, a Robust Conversational search EMbedder that is additionally equipped with LLM's query reformulation capability without losing bas...
By Kilho Son, Paul Hsu, Cha Zhang, Dinei Florencio
The paper introduces the Explainability Assistant, an open‑source conversational XAI system designed to interpret complex energy consumption forecasting models. By leveraging large language model function‑calling, it achieves 94% intent‑parsing accuracy and supports flexible natural‑language interaction across different ML problem types without task‑specific fine‑tuning. A comparative evaluation with energy domain specialists shows improved usability and consistent task accuracy, with all experts preferring the conversational interface over a traditional XAI dashboard.
By Rodion Krjut\v{s}kov, Eduard Barbu, Nikos Sakkas, Sofia Yfanti
The paper presents a tri‑agent framework for evaluating large language models’ question‑clarification abilities. It involves a Question Clarifying Agent that identifies ambiguities and asks follow‑up questions, a Respondent Agent that simulates human replies, and an Evaluator Agent that judges the dialogue using metrics such as ambiguity handling, question quality, dialogue efficiency, language appropriateness, and intent alignment. The authors illustrate the approach with synthetic supply‑chain data and discuss validating the evaluator against human judgments.
By Yikai Zhao, Saurabh Pandey, Pradeep Kumar Misra
arXiv:2610.09063v1 Announce Type: new
Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability c...
By Sourabh Kasliwal, Shubhranshu Singh
The paper introduces UPHELD, a large benchmark of human-to-human dialogues written by professional script writers, featuring realistic turn densities and over 36,000 per-turn human annotations. It evaluates existing automatic metrics and LLM-as-a-judge methods, finding them unreliable against expert human judgment. Using UPHELD, the authors develop a Mixture-of-Judges framework that improves correlation with human assessments by about 30%.
By Ilija Subasic, Andrew Rabinovich, Zhao Chen
arXiv:2606. 20400v1 Announce Type: new Abstract: Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings.
By Zahra Abbasiantaeb, Zeno Belligoli, Omar Essam, Mohammad Aliannejadi
Large Language Models (LLMs) are increasingly deployed in interactive systems where understanding user intent precisely is paramount. A key capability for such systems is effective question clarificat...
arXiv:2605. 27642v2 Announce Type: replace-cross Abstract: Soft prompting, also known as continuous prompting, is a parameter-efficient method for tuning LLMs to specific tasks.
By Pitipat Kongsomjit, Suryansh Goyal, Jacob Whitehill
SimXAI is a lightweight, LLM‑centric conversational layer that replaces traditional intent recognition, dialogue management, and template‑based response modules in GUS‑style ConvXAI agents. By using a single tool‑calling LLM to interface with existing XAI resources, it provides a modular interface that improves function‑selection accuracy, faithfulness, and handling of context‑dependent requests. In a user study, substituting SimXAI for the conversational layer of the TalkToModel agent led to more accurate task completion in fewer turns, demonstrating enhanced conversational quality while preserving faithfulness.
By Van Bach Nguyen, Qianli Wang, Nils Feldhus, J\"org Schl\"otterer, Christin Seifert