arXiv AI July 15, 2026 By Niranjan Kumar M, Balaji Nagarajan, Karthik Nair, Faysal Satter, Nithin Surendran

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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

Related stories

Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents

Efficient ASR Training with Conversations that Never Happened

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

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

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

HAL: Inducing Human-likeness in LLMs with Alignment