10 citations · 12 across the 8 of their papers we have counts for
8 papers
How Good Are LLMs at Processing Tool Outputs?
Kiran Kate, Yara Rizk, Poulami Ghosh +4
Most realistic task automation problems require large language models (LLMs) to call tools, which often return complex JSON responses. These responses must be further processed to…
ToolRM: Outcome Reward Models for Tool-Calling Large Language Models
Mayank Agarwal, Ibrahim Abdelaziz, Kinjal Basu +4
As large language models (LLMs) increasingly interact with external tools, reward modeling for tool use has emerged as a critical yet underexplored area of research. Existing rewar…
Assessing Engineering Student Perceptions of Introductory CS Courses in an Indian Context
Utsav Kumar Nareti, Divyansh Gupta, Chandranath Adak +5
Understanding student perceptions of assessment is vital for designing inclusive and effective learning environments, especially in technical education. This study explores enginee…
LongFuncEval: Measuring the effectiveness of long context models for function calling
Kiran Kate, Tejaswini Pedapati, Kinjal Basu +5
Multiple recent studies have documented large language models' (LLMs) performance on calling external tools/functions. Others focused on LLMs' abilities to handle longer context le…
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal +23
Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challengin…
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang +43
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environmen…
Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models
Mayank Agarwal, Yikang Shen, Bailin Wang +2
Current language models tailored for code tasks often adopt the pre-training-then-fine-tuning paradigm from natural language processing, modeling source code as plain text. This ap…
Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations
Zilu Tang, Mayank Agarwal, Alex Shypula +4
This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanat…