2 citations · 3 across the 6 of their papers we have counts for
6 papers
Evaluating Small Language Models for Front-Door Routing: A Harmonized Benchmark and Synthetic-Traffic Experiment
Warren Johnson, Charles Lee
Selecting the appropriate model at inference time -- the routing problem -- requires jointly optimizing output quality, cost, latency, and governance constraints. Existing approach…
Prompt Compression in Production Task Orchestration: A Pre-Registered Randomized Trial
Warren Johnson, Charles Lee
The economics of prompt compression depend not only on reducing input tokens but on how compression changes output length, which is typically priced several times higher. We evalua…
Adapting Task-Oriented Dialogue Models for Email Conversations
Soham Deshmukh, Charles Lee
Intent detection is a key part of any Natural Language Understanding (NLU) system of a conversational assistant. Detecting the correct intent is essential yet difficult for email c…
NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback
Ruijie Zhou, Soham Deshmukh, Jeremiah Greer +1
Current research in dialogue systems is focused on conversational assistants working on short conversations in either task-oriented or open domain settings. In this paper, we focus…
To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints
Barun Patra, Chala Fufa, Pamela Bhattacharya +1
State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extr…
ScopeIt: Scoping Task Relevant Sentences in Documents
Vishwas Suryanarayanan, Barun Patra, Pamela Bhattacharya +2
Intelligent assistants like Cortana, Siri, Alexa, and Google Assistant are trained to parse information when the conversation is synchronous and short; however, for email-based con…