100 citations · 342 across the 39 of their papers we have counts for
11 papers · 1 filter
Concise Reasoning via Reinforcement Learning
Mehdi Fatemi, Banafsheh Rafiee, Mingjie Tang +1
A major drawback of reasoning models is their excessive token usage, inflating computational cost, resource demand, and latency. We show this verbosity stems not from deeper reason…
"Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer?
Benjamin Reichman, Kartik Talamadupula
Human communication is a multifaceted and multimodal skill. Communication requires an understanding of both the surface-level textual content and the connotative intent of a piece…
Emotional RAG LLMs: Reading Comprehension for the Open Internet
Benjamin Reichman, Adar Avsian, Kartik Talamadupula +2
Queries to large language models (LLMs) can be divided into two parts: the instruction/question and the accompanying context. The context for retrieval-augmented generation (RAG) s…
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury +3
Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning.…
Are Human Conversations Special? A Large Language Model Perspective
Toshish Jawale, Chaitanya Animesh, Sekhar Vallath +2
This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human). We analyze three…
Using Document Similarity Methods to create Parallel Datasets for Code Translation
Mayank Agarwal, Kartik Talamadupula, Fernando Martinez +5
Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has…
NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
Mayank Agarwal, Tathagata Chakraborti, Quchen Fu +6
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line. Participants were tasked with building models that can t…
Reading Comprehension as Natural Language Inference: A Semantic Analysis
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar +3
In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited…
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar +3
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it. However, t…
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel +10
Textual entailment is a fundamental task in natural language processing. Most approaches for solving the problem use only the textual content present in training data. A few approa…
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
Iulian Vlad Serban, Tim Klinger, Gerald Tesauro +4
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic…