5 citations · 18 across the 15 of their papers we have counts for
17 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…
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…
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…
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…