5 citations · 8 across the 10 of their papers we have counts for
8 papers · 1 filter
NAVCON: A Cognitively Inspired and Linguistically Grounded Corpus for Vision and Language Navigation
Karan Wanchoo, Xiaoye Zuo, Hannah Gonzalez +5
We present NAVCON, a large-scale annotated Vision-Language Navigation (VLN) corpus built on top of two popular datasets (R2R and RxR). The paper introduces four core, cognitively m…
Benchmarking LLM Guardrails in Handling Multilingual Toxicity
Yahan Yang, Soham Dan, Dan Roth +1
With the ubiquity of Large Language Models (LLMs), guardrails have become crucial to detect and defend against toxic content. However, with the increasing pervasiveness of LLMs in…
Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models
Amey Hengle, Prasoon Bajpai, Soham Dan +1
While recent large language models (LLMs) demonstrate remarkable abilities in responding to queries in diverse languages, their ability to handle long multilingual contexts is unex…
Needle in the Haystack for Memory Based Large Language Models
Elliot Nelson, Georgios Kollias, Payel Das +2
Current large language models (LLMs) often perform poorly on simple fact retrieval tasks. Here we investigate if coupling a dynamically adaptable external memory to a LLM can allev…
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design
Nafis Neehal, Bowen Wang, Shayom Debopadhaya +4
CTBench is introduced as a benchmark to assess language models (LMs) in aiding clinical study design. Given study-specific metadata, CTBench evaluates AI models' ability to determi…
On the Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning
Mauricio Gruppi, Soham Dan, Keerthiram Murugesan +1
Text-based reinforcement learning involves an agent interacting with a fictional environment using observed text and admissible actions in natural language to complete a task. Prev…