activity
20182026
most citedFew-Shot Bot: Prompt-Based Learning for Dialogue Systems

45 citations · 218 across the 68 of their papers we have counts for

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cs.CL2026

DIAL-SUMMER: A Structured Evaluation Framework of Hierarchical Errors in Dialogue Summaries

Sahana Ramnath, Nima Chitsazan, Mingyang Zhou +8

Dialogues are a predominant mode of communication for humans, and it is immensely helpful to have automatically generated summaries of them (e.g., to revise key points discussed in…

cs.CL2026

Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling

Alaa Elsetohy, Sama Hadhoud, Haryo Akbarianto Wibowo +4

Multilingual benchmarks rarely test reasoning over culturally grounded premises: translated datasets keep English-centric scenarios, while culture-first datasets often lack control…

cs.CL2026

PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues

Mohammad Rifqi Farhansyah, Hanif Muhammad Zhafran, Farid Adilazuarda +6

Code-switching is a widespread practice among the world's multilingual majority, yet few benchmarks accurately reflect its complexity in everyday communication. We present PingPong…

cs.CL2026

Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection

Tianyi Niu, Justin Chih-Yao Chen, Genta Indra Winata +6

Large Language Model (LLM) routers dynamically select optimal models for given inputs. Existing approaches typically assume access to ground-truth labeled data, which is often unav…

cs.CL2026

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data

Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett +94

Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heter…

cs.CL2025

Leveraging Parameter Space Symmetries for Reasoning Skill Transfer in LLMs

Stefan Horoi, Sangwoo Cho, Supriyo Chakraborty +4

Task arithmetic is a powerful technique for transferring skills between Large Language Models (LLMs), but it often suffers from negative interference when models have diverged duri…