arXiv AI

Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking

arXiv:2607. 12085v1 Announce Type: new Abstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality.

arXiv AI

Aug 7

Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents

arXiv:2608. 06329v1 Announce Type: cross Abstract: Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.

By Noam Koren, Roy Bar-Haim, Abigail Goldsteen

llms agents benchmarks

arXiv AI

Jun 3

Efficient ASR Training with Conversations that Never Happened

arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.

By M\'at\'e Gedeon, P\'eter Mihajlik

llms multimodal benchmarks

arXiv AI

Jul 21

Toward Anthropomorphic Dialogue: A Closed-Loop Framework for Human-Like Chat Generation, Evaluation, and Preference Alignment

arXiv:2607. 17191v1 Announce Type: new Abstract: Human-like private chat requires more than fluent response generation: a system must preserve persona, relationship, memory, bounded knowledge, medium-specific timing, and a coherent multi-turn arc.

By Wentao Liu, Siyu Song, Xi Chen, Youjia Li, Xiaokun Wang, Min Ji, Ji Wang

reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 17

Project Kaleidoscope: Contextual, Human-Aligned Evaluation for Real-World AI Applications

arXiv:2607. 14673v1 Announce Type: new Abstract: Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public benchmarks rarely match a team's users, context, or policies, and human review is often tedious to scale.

By Leanne Tan, Rohan Jaggi, Shaun Khoo, Roy Ka-Wei Lee

llms benchmarks

arXiv AI

Jun 6

PEFT of SLM for Telecommunications Customer Support: A Comparative Study of LoRA Configurations with Energy Consumption Analysis

arXiv:2606. 05176v1 Announce Type: cross Abstract: While large language models (LLMs) show strong performance in natural language understanding and generation, their evaluation and adaptation to domain-specific constraints in telecommunications customer support remain limited.

By Lucas Tamic, Ilan Jaffeux-Cheniout, Xavier Marjou

llms fine-tuning

arXiv AI

Jul 3

HAL: Inducing Human-likeness in LLMs with Alignment

arXiv:2601. 02813v3 Announce Type: replace Abstract: Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize.

By Masum Hasan, Junjie Zhao, Ehsan Hoque

llms safety

arXiv AI

Jun 16

State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs

arXiv:2606. 16307v1 Announce Type: new Abstract: Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings, and largely absent from public datasets.

By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Das, Jitesh Chandra, Yun-Shiuan Chuang, Chaitanya Kulkarni, Arun Menon, Linsey Pang, Avinash Karn, Mouli V, Prakhar Mehrotra

llms agents safety

arXiv AI

Jun 10

T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains

arXiv:2606. 11070v1 Announce Type: cross Abstract: Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems.

By Genta Indra Winata, Amartya Chakraborty, Yuzhen Lin, Swasthi P Rao, Shikhhar Siingh, Houhan Lu, Nadia Bathaee, Sriharsha Hatwar, Paresh Dashore, Anmol Jain, Kshitij Tayal, Xiuzhu Lin, Anirban Das, Sambit Sahu, Shi-Xiong Zhang

llms agents benchmarks

arXiv AI

Jun 2

BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning

arXiv:2606. 02109v1 Announce Type: new Abstract: Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approaches fundamentally different from academic benchmarks.

By Shannon Serrao, Soumitra Chatterjee, Dorina Strori, Abhishek Sharma, Nathan Miller

llms agents benchmarks data-engineering

arXiv AI

Aug 7

What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)

arXiv:2608. 06202v1 Announce Type: cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness.

By Ro Encarnaci\'on, Tina Behzad, Emma Lurie, Dana\'e Metaxa

llms benchmarks safety

arXiv AI

Jun 30

SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation

arXiv:2606. 30491v1 Announce Type: cross Abstract: Background.

By Zhuhan Bao, Rui Yang, Bohao Yang, Zhiyi Liu, Sicheng Shu, Ruio Heerschap, Le Li, Doris Yang, Elisabeth Bond, Haoyuan Wang, Nicoleta Economou-Zavlanos, Joshua M. Biro, Matthew McDermott, Nan Liu, Anand Chowdhury, Kai Sun, Kathryn Pollak, Ed Hammond, Chuan Hong

arXiv AI

Jul 31

Large-Scale ChatBot Validation Through Customer Digital Twin Simulations

arXiv:2607. 26060v1 Announce Type: cross Abstract: LLM-based chatbots are transforming customer service in regulated domains such as banking, but scalable and cost-effective validation remains a critical barrier to safe deployment.

By Cristovao Iglesias, Devesh Batra, Alankar Atreya, Stefan Wagner, Robert Hankache, Patrick Sinclair, Giulio Pelosio, Michael McMillan, Greig A. Cowan, Raad Khraishi

llms agents safety

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