95 citations · 168 across the 50 of their papers we have counts for
54 papers
Evaluating LLMs as Agentic Workflow Executors in Real-World Customer Support Skills
Md Tahmid Rahman Laskar, Gundeep Singh, Xue-Yong Fu +11
Enterprise customer-support agents increasingly rely on Large Language Models (LLMs) to execute bounded, tool-using workflows. We study this problem through the lens of Skill LLMs:…
When Better Turns Do Not Make Better Agents: Diagnosing the Gap Between Next-Turn Metrics and Workflow Success
Md Tahmid Rahman Laskar, Xue-Yong Fu, Gundeep Singh +11
Agent models are frequently evaluated one decision at a time, where the model predicts the next action based on the gold interaction history, which is scored against a reference. W…
SFT or RL for Tool-Calling Agents? A Controlled Study Across Data, Method, and Scale
Md Tahmid Rahman Laskar, Xue-Yong Fu, Shashi Bhushan TN
Limited controlled evidence exists on how training data, adaptation method, and model scale jointly affect tool-calling performance in language-model agents. We evaluate supervised…
Routing Is Not Enough: Diagnosing Intra-Adapter Subspace Contention in MoE+LoRA Fine-Tuning
Mehreen Hossain Chowdhury, Nowshin Mahjabin, Ahmed Shafin Ruhan +3
Multi-domain fine-tuning often combines MoE routing with LoRA, assuming that token-level routing separates domain-specific updates. We test this assumption in MoE+LoRA using Python…
MemeCULT-1K: Benchmarking South Asian Cultural Context and Humor Understanding of Multimodal Models
Tawsif Tashwar Dipto, Mehedi Ahamed, Radib Bin Kabir +5
Meme understanding goes beyond recognizing visual content or literal text; it requires implicit cultural knowledge and pragmatic inference that most vision-language models still la…
VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?
Mizanur Rahman, Arshia Azimlu, Shadikur Rahman +4
Vision-language models (VLMs) have shown strong capabilities in generating visualization code from textual or visual specifications. However, real-world visualization authoring is…
DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?
Mizanur Rahman, Mohammed Saidul Islam, Ridwan Mahbub +3
Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as…
Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs
Gundeep Singh, Parsa Kavehzadeh, Jing Xia +5
Enterprise analytics aims to make organizational data accessible for decision-making, yet non-technical users still face barriers when using traditional business intelligence tools…
Chart Deception in Vision-Language Models: From Vulnerability to Mitigation
Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar +3
Information visualizations are widely used to communicate patterns, trends, and outliers, yet deceptive design choices-such as truncated or inverted axes, distorted aspect ratios,…
From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents
Md Tahmid Rahman Laskar, Xue-Yong Fu, Seyyed Saeed Sarfjoo +3
Voice agents increasingly require reliable tool use from speech, whereas prominent tool-calling benchmarks remain text-based. We study whether verified text benchmarks can be conve…
Assessing the Quality of Mental Health Support in LLM Responses through Multi-Attribute Human Evaluation
Abeer Badawi, Md Tahmid Rahman Laskar, Elahe Rahimi +6
The escalating global mental health crisis, marked by persistent treatment gaps, availability, and a shortage of qualified therapists, positions Large Language Models (LLMs) as a p…
Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models
Sawsan Alqahtani, Mir Tafseer Nayeem, Md Tahmid Rahman Laskar +2
Tokenization underlies every large language model, yet it remains an under-theorized and inconsistently designed component. Common subword approaches such as Byte Pair Encoding (BP…
Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization
Mizanur Rahman, Mohammed Saidul Islam, Md Tahmid Rahman Laskar +2
Text-to-Visualization (Text2Vis) systems translate natural language queries over tabular data into concise answers and executable visualizations. While closed-source LLMs generate…
Beyond Fertility: Analyzing STRR as a Metric for Multilingual Tokenization Evaluation
Mir Tafseer Nayeem, Sawsan Alqahtani, Md Tahmid Rahman Laskar +2
Tokenization is a crucial but under-evaluated step in large language models (LLMs). The standard metric, fertility (the average number of tokens per word), captures compression eff…
When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation
Abeer Badawi, Elahe Rahimi, Md Tahmid Rahman Laskar +6
Evaluating Large Language Models (LLMs) for mental health support is challenging due to the emotionally and cognitively complex nature of therapeutic dialogue. Existing benchmarks…
Deploying Tiny LVLM Judges for Real-World Evaluation of Chart Models: Lessons Learned and Best Practices
Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub +7
Large Vision-Language Models (LVLMs) with only 7B parameters have shown promise as automated judges in chart comprehension tasks. However, tiny models (<=2B parameters) still perfo…
DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
Elena Khasanova, Harsh Saini, Md Tahmid Rahman Laskar +3
The rapid advancements in Large Language Models (LLMs) have enabled their adoption in real-world industrial scenarios for various natural language processing tasks. However, the hi…
AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents
Md Tahmid Rahman Laskar, Julien Bouvier Tremblay, Xue-Yong Fu +2
The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problems has been on the rise with the rapid progress o…
DACP: Domain-Adaptive Continual Pre-Training of Large Language Models for Phone Conversation Summarization
Xue-Yong Fu, Elena Khasanova, Md Tahmid Rahman Laskar +2
Large language models (LLMs) have achieved impressive performance in text summarization, yet their performance often falls short when applied to specialized domains that differ fro…
LLM-Based Data Science Agents: A Survey of Capabilities, Challenges, and Future Directions
Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam +5
Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use,…
The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models
Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar +3
Information visualizations are powerful tools that help users quickly identify patterns, trends, and outliers, facilitating informed decision-making. However, when visualizations i…
From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Text
Ridwan Mahbub, Mohammed Saidul Islam, Mir Tafseer Nayeem +4
Charts are very common for exploring data and communicating insights, but extracting key takeaways from charts and articulating them in natural language can be challenging. The cha…
Text2Vis: A Challenging and Diverse Benchmark for Generating Multimodal Visualizations from Text
Mizanur Rahman, Md Tahmid Rahman Laskar, Shafiq Joty +1
Automated data visualization plays a crucial role in simplifying data interpretation, enhancing decision-making, and improving efficiency. While large language models (LLMs) have s…
Evaluating the Effectiveness of Cost-Efficient Large Language Models in Benchmark Biomedical Tasks
Israt Jahan, Md Tahmid Rahman Laskar, Chun Peng +1
This paper presents a comprehensive evaluation of cost-efficient Large Language Models (LLMs) for diverse biomedical tasks spanning both text and image modalities. We evaluated a r…