5 citations · 19 across the 17 of their papers we have counts for
18 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…
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…
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…
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…
Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization
Md Tahmid Rahman Laskar, Elena Khasanova, Xue-Yong Fu +2
This work focuses on the task of query-based meeting summarization in which the summary of a context (meeting transcript) is generated in response to a specific query. When using L…
Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?
Xue-Yong Fu, Md Tahmid Rahman Laskar, Elena Khasanova +2
Large Language Models (LLMs) have demonstrated impressive capabilities to solve a wide range of tasks without being explicitly fine-tuned on task-specific datasets. However, deploy…
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective
Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen +1
This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluat…
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs
Xue-Yong Fu, Md Tahmid Rahman Laskar, Cheng Chen +1
In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models. A particul…
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching
Md Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu +3
In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching o…
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop
Md Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu +1
Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc. Not only that annotating such data is very challenging for the annotators, but a…
Entity-level Sentiment Analysis in Contact Center Telephone Conversations
Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar +3
Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain…
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts
Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar +2
We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the…
Developing a Production System for Purpose of Call Detection in Business Phone Conversations
Elena Khasanova, Pooja Hiranandani, Shayna Gardiner +3
For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not kno…
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich +4
An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base. However, deploying a neural entity linking system for…
Improving Punctuation Restoration for Speech Transcripts via External Data
Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar +2
Automatic Speech Recognition (ASR) systems generally do not produce punctuated transcripts. To make transcripts more readable and follow the expected input format for downstream la…