5 citations · 12 across the 18 of their papers we have counts for
7 papers · 2 filters
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…
Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis
Kushal Raj Bhandari, Sixue Xing, Soham Dan +1
Large Language Models (LLMs), already shown to ace various unstructured text comprehension tasks, have also remarkably been shown to tackle table (structured) comprehension tasks w…
Large Language Model Confidence Estimation via Black-Box Access
Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh +2
Estimating uncertainty or confidence in the responses of a model can be significant in evaluating trust not only in the responses, but also in the model as a whole. In this paper,…
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…
Language Guided Exploration for RL Agents in Text Environments
Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel +2
Real-world sequential decision making is characterized by sparse rewards and large decision spaces, posing significant difficulty for experiential learning systems like $\textit{ta…